And if you want to go deeper, my AI Prototyping course with Reforge starts April 24th.

Many of the best practices we take for granted weren’t designed to help you build the best product. They were designed to avoid expensive mistakes.

The “minimum viable product” is the clearest example: no customer is looking for a product that just barely solves their need.

MVPs exist because building software is expensive, and we want to validate demand before investing further.

MVPs are no longer viable

That single constraint — the expense of building software — shaped every aspect of yesterday’s product lifecycle. We avoided building for as long as possible. And when we did build, we built as little as we could get away with.

We created an escalating ladder of artifacts — each requiring more effort but providing stronger signal:

We started with specs — relatively quick to write and easy to iterate.

After specs, we moved to visual designs. Wireframes come first — quick sketches to establish structure. Then detailed designs, sometimes pixel-perfect.

But even the best mockup can’t capture how modern software actually feels. Animations, generative AI output, native interactions — these only make sense when you experience them. A Figma mockup can show you a screen. It can’t show you whether an interaction feels right.

Still, we created these artifacts because we need conviction before we commit engineering resources. Visual designs are expensive to create and hard to iterate, but they were cheaper than building the wrong thing.

And when working software finally entered the picture, effort jumped dramatically — teams often skipped functional prototypes entirely because they’re nearly as hard to build as the MVP itself.

At each step, we got valuable signal that gave us the conviction to invest in the next step. If we didn’t get that conviction, we went back to square one — iterating cheaply until we figure out what’s missing.

This process wasn’t designed to help you build great products. It was designed to manage the cost of building the wrong ones.

From assembly line to jazz band

For decades, that was the right approach. When building software is expensive, you manage costs carefully. Even the most agile teams operated as assembly lines — handoffs from one team to another, conviction required at every stage to justify the next.

But what happens when the cost of building software drops dramatically?

Today, AI is changing the fundamental constraint of product management.

What once took weeks now takes hours. You can often create a working prototype in less time than it takes to write a detailed spec or sketch out a wireframe.

When working software becomes cheap to produce, the assembly line stops making sense. Why wait until the end of the process to see your idea come to life? Why hand off a spec when you can provide a prototype that better reflects the requirements?

Moving forward, the best product teams will operate like a jazz band. Instead of staged handoffs, team members riff off each other — each leveraging their unique strengths to get the right artifacts in front of customers. The flow isn’t one-directional. It loops back. It branches. Prototypes inform research. Research reshapes prototypes. A spec might be the first step, or it might be one of the last.

Working software is no longer just what we deliver at the end of the process. It’s integral to how we communicate, decide, and validate along the way.

The new operating model

In the old model, product development moved in a rigid sequence: PM hands off to design, design hands off to engineering, and each step introduces weeks of lag while teams wait for the next function to pick up the work.

In the new model, those handoffs collapse into a tighter loop — PM, design, and engineering riff together, using working prototypes to align early and iterate quickly. The result is less time spent translating intent across documents and meetings, and more time spent learning from real interactions and real customer conversations.

This isn’t incremental — it’s a fundamental shift in how we operate. In this new model:

1. Speed is no longer a competitive advantage — it’s table stakes. We can build faster, but so can our competitors. The market landscape and customer expectations are changing as quickly as we can build. Moving fast matters, but it’s not a moat.

2. Everyone is a builder — which makes prioritization harder. PMs, founders, salespeople, customer success teams — anyone with product input can now turn hunches into working software. That’s a flood of new possibilities, often from people closer to the customer. But more prototypes doesn’t mean better products. You need sharper validation to filter for what actually serves the customer.

3. The math on exploration has flipped. You can build five solutions instead of committing to one. Hunches that would never justify the resource investment can now get tested. What used to look like waste — building something you might throw away — is now the fastest path to the right answer.

This is why prototyping has become a must-learn skill. Not because it’s new and shiny — because it’s now foundational.

The best product builders are integrating working software into every phase of discovery and development.

They prototype to explore. They prototype to align. They prototype to validate. Fluency with prototyping is now as fundamental as the ability to write a clear spec or run a good customer interview.

Prototypes are decision-making tools

Prototyping is fun — and it’s easy to get pulled into crafting the perfect thing. It’s critical to remember that prototypes aren’t the deliverable; they’re a tool for making better and faster decisions.

This means the first question isn’t “What should we prototype?” Instead, start by asking: “What decision are we trying to make?”

Your answer determines what you build:

• Concept prototypes — Exploring which direction to pursue? Build rough variations to map the solution space.

• Design prototypes — Need to align stakeholders on what we’re building? Build something higher fidelity that gets everyone on the same page.

• Research prototypes — Need to validate whether customers will use it? Build something users can understand and get value from.

• Technical prototypes — Need to prove technical feasibility? Build something that tests how things work in the real world.

This is the jazz band in action. During any product cycle, you’ll reach for some combination of these prototypes — not in a fixed sequence, but as the questions demand. Each prototype answers critical path questions, helping the team build the conviction to launch.

The fast pace of prototyping also means we can throw out a lot more work. Dead ends that used to cost weeks now cost hours. This kind of exploratory iteration used to be the privileged domain of large companies with big research budgets. Anthropic reportedly cycles through 10 or more prototypes for a single feature, each iteration compressed to hours by AI coding tools. That kind of rapid experimentation is now accessible to anyone.

The prototype itself is never the point. The decision it enables is.

Prototypes are sketches, not finished paintings

An artist never starts by putting oils directly on a blank canvas. Oil painting is slow, deliberate, and expensive to change. So you start with a pencil sketch — fast, cheap, and easy to revise. The sketch captures the composition, the proportions, the intent. It tells you whether the painting is worth making. And then — this is the important part — the sketch gets painted over. It’s not the final product. It was never meant to be.

Your prototype is that sketch. It captures the flows, the interactions, the intent. It tells your team whether the product is worth building. But it was always meant to be replaced by the fully realized product — just as the pencil lines disappear under layers of oil paint.

Throwing away a prototype sounds inefficient. After all, it means building the similar things more than once. But the opposite is true. Trying to turn your prototype into production code weighs it down from the very start and undermines the and flexibility of prototyping. For the same reason, artists don't throw oil paints onto a blank canvas. Skipping the sketch ends up costing time, not saving it.

The data backs this up. When development consultancy Whitespectre audited an AI-built prototype — a Jira evaluation tool a PM had assembled in four hours using v0, Cursor, and ChatGPT — they found that only 30% of the code was salvageable for production. The rest was unusable code. That’s not a failure. That’s the process working exactly as intended. The prototype compressed weeks of alignment and discovery into a single afternoon.

The prototype’s job is to give the team a running start, not to cross the finish line.

The handoff is where you pass the conviction you’ve built — along with the decisions, context, and intent behind it — to the people who will bring it to life in production.

The culture required to make this work

AI prototyping isn’t just a matter of rolling out new tools. It’s a shift in how teams build.

Nobody has this fully figured out yet. The loudest LinkedIn takes — “we don’t need engineers anymore,” “only hire builders,” “I replaced my team with AI agents” — are mostly overreach.

AI prototyping is evolving how we work, not replacing who does the work.

The critical questions remain the same:

1. What do customers really want?

2. How do we best serve them?

3. How do we build a durable business around that value?

What has changed is the cost of exploring and communicating solutions. Teams that embrace this new way of working share a few traits:

• They prototype constantly — and with purpose. Skill comes from reps. The teams pulling ahead aren’t theorizing about AI tools; they’re using them every day. But volume alone isn’t the point. Every prototype answers a question. If you can’t articulate what you’re trying to learn, you’re not prototyping — you’re tinkering.

• They aren’t territorial. AI prototyping blurs traditional boundaries. PMs explore UX. Engineers define requirements. Designers validate technical assumptions. The best teams aren’t threatened by this — they welcome it. Assembly line handoffs become jazz band collaboration.

• They have permission to learn. This is the most important one. AI prototyping is a skill. Skills take practice. That means leadership gives teams autonomy to try things that might not work — and doesn’t expect immediate perfection. Teams without permission to fail don’t build the muscle.

Teams that embrace this shift build better products faster. Teams that don’t are operating at the same old speed while everyone around them accelerates.

Build software to build conviction

For decades, the expense of software shaped every aspect of how product teams operated. Artifacts, handoffs, gates, approvals — all designed to build conviction before spending.

That constraint is gone.

Working software is no longer just what you ship at the end. It’s how you communicate, make decisions, and validate ideas throughout the entire development process.

Remember where we started: the processes we’ve inherited weren’t designed to help you build great products. They were designed to manage the cost of building the wrong ones.

Now you can focus on what actually matters.

Prototyping is how you get there — not as a phase or a deliverable, but as a default mode for turning ambiguity into clarity. Fluency with prototyping is now as fundamental as writing a clear spec or running a good customer interview.

The opportunity isn’t just to work faster. It’s to finally build the way we always wanted to — with working software at the center of every decision.

Ready to get started? I’m presenting a hands-on session on AI prototyping and context engineering at Dan Olsen’s Lean Product Meetup next week — in-person at Intuit HQ in Mountain View or via Zoom. Dan’s community is one of the best in Silicon Valley for product builders, and this session will put the ideas from this post into practice. Grab a ticket here.

And if you want to go deeper, my AI Prototyping course on Reforge starts April 24th. You’ll learn how to turn prototyping into a practical skill you use every day — from software fundamentals to advanced techniques like data-driven prototyping and context engineering.

My goal was to go deeper than the tutorials out there and help non-technical people become AI-native builders. If prototyping and AI-powered development is reshaping how you approach your role — whether you're a PM, designer, engineer, user researcher, or something else entirely — check it out.

Join AI Prototyping starting April 24th

The most differentiated product? Might just be something nobody wants. And yet teams rally around it. Slide decks build toward it. Entire roadmaps get shaped by the question “What makes us different?” — when the far more important question is “What makes us better?”

Over my career consulting with dozens of companies on product strategy, I’ve found that the focus on differentiation is more destructive to clear strategic thinking than any other concept. The problem isn’t that differentiation is wrong — it’s that it’s incomplete. To understand why, we need to go back to first principles.

The commodity-monopoly spectrum

The concept of strategic differentiation comes from a first principle about pricing power.

Consider two types of products:

1. A complete commodity — like bananas. Every product is identical. Customers don’t care which one they choose. The market sets the price, not the company.

2. A complete monopoly — like the NFL. The product is the only one of its kind. The company has complete control over price & supply.

These two extremes behave very differently. Commodity sellers have to match the market price or lose share to a cheaper provider selling the same thing. Monopolies can lower prices to increase demand or raise prices to pad profits. They’re in control. And we know from NFL ticket prices which option monopoly companies choose — they’re much more in the business of maximizing profits than maximizing demand.

Most companies don’t exist at either extreme. In a capitalist economy, total monopolies get regulated or assailed by competition that steals away their monopoly power. And commodity sellers band together to reclaim pricing control — this is why OPEC exists.

How “differentiation” becomes a trap

So competitive differentiation comes from this fundamental strategic principle: commodity products are all the same as other products in the market; monopoly products are unique. As shorthand, we use terms like “competitive differentiation” or “unique selling proposition” to highlight that there’s value in being unique.

But here’s the nuance that gets lost: being unique only matters if customers in your market want the unique thing you’re selling. Uniqueness on its own is not a virtue. In fact, uniqueness for the sake of uniqueness can undermine your entire strategy because it conflates being different with being better.

On the flip side, sameness can be a virtue. A product that closely resembles another product with strong product-market fit benefits from selling into a market that already wants what it has. In a pre-product-market-fit company, the similarity of your product to others carries a lot more weight than differences customers may not want.

That doesn’t mean your product can be identical to the competition. All things being equal, customers choose the product they’ve heard of, the product their friends use, the product from an established company with a track record.

So you need something to break through the noise. But what? Let’s look at two markets that tell very different stories about what that “something” actually is.

Two markets, two lessons

Uber vs. Lyft: Advantage through network effects

In the ride-sharing market, Uber and Lyft are nearly identical products. Uber won not because it was simply different, but because it had inherent advantages. As the network of drivers grew, that created a compounding advantage: better prices and faster service. Yes, you could call better prices and faster service “different” — but the important point is that those differences confer an advantage — one that compounds over time, making it harder and harder for anyone to compete.

Tidal vs. Spotify: Different ≠ better

In the early days of music streaming, Tidal had genuinely interesting strategic differences. Founded by music industry veterans, it offered a library of music unavailable on Spotify and the ability to stream CD-quality audio. Tidal was meaningfully different.

But Spotify had an advantage. With its smaller library and less focus on audio fidelity, Spotify could do a better job actually streaming songs to customers. Songs started faster. Fewer cutouts. Fewer buffering issues. It worked on connections of varying quality.

On paper, Spotify was subpar compared to Tidal — a smaller library, lower audio quality. But they had an advantage: more reliable music streaming. At the end of the day, listeners want to enjoy music uninterrupted more than they care about exclusive tracks they may never search for.

From differentiation to advantage

The next time you’re in a heated strategic conversation, pay attention.

If “competitive differentiation” or “unique selling proposition” are taking up a lot of airtime, notice where the focus lands. These terms pull your attention toward what’s different — not what’s better.

Instead, spotlight the term competitive advantage. Ask:

• What advantages do we have relative to others in the market?

• How can we turn those advantages into a product that’s better for customers?

When you focus on advantages instead of differences, you realize the goal isn’t uniqueness. It’s competitiveness.

The most successful companies — the ones that are effectively monopolies or near-monopolies — didn’t get there because they were different from everyone else. They got there because they had the biggest advantage, and that advantage compounded over time.

Right now every product team is wrestling with the same question: How do we actually build AI products that work?

The advice is often too abstract (”AI will change everything!”) or too technical (“Here’s how you can optimize your RAG pipeline.”). What’s missing is the messy middle: the product decisions, team dynamics, and quality trade-offs that determine whether your AI feature delights users or gets abandoned after one try.

Over the past few years, Productboard has embarked on a journey to completely re-architect our product experience to be AI-first. We started with Productboard Pulse, a specialized product for analyzing customer feedback at scale, which taught us crucial early lessons about quality and user trust. Those learnings informed our work on Productboard Spark—an agentic experience currently in private beta that represents a fundamental rethinking of the product management workflow, not just AI features bolted onto our existing platform.

Here’s what we’ve learned from building Spark, organized around three critical questions:

1. How do you know you’re building the right thing? We’ll cover evaluating AI use cases, the difference between guiding and automating, why how you build matters as much as what you build, and why you need to build ahead of customer readiness.

2. How do you ship fast without sacrificing quality? We’ll share our framework for quality thresholds (Alpha/Beta/GA), the third dimension of AI products, our systematic approach to improving from 40% to 85% accuracy, and why customers’ expectations have fundamentally shifted.

3. How do you structure teams that scale? We’ll explain the distinct team phases from tight core groups to specialized pods, why the transition is painful, and how to maintain velocity through rapid growth.

How do you know you’re building the right thing?

The first challenge hits you immediately: AI is really good at some things and terrible at others, but it’s not always obvious which is which.

Traditional product development is deterministic. You build a feature, test it, and it behaves the same way every time. AI is fundamentally different. The same input can produce different outputs. This non-determinism creates a new challenge: before you can decide where to apply AI, you first have to understand what it can reliably do. That became our first major hurdle.

So how do you approach this?

Start with the customer problem, then validate AI fit

You still start with customer pain. That hasn’t changed. But here’s what’s different: you spend way more time on product discovery.

You need to validate not just that your solution is valuable, but that you can actually build it with the level of quality and reliability customers expect. Because customers might love your AI solution when it’s working, but have one too many bad outputs, and they’ll lose patience fast. With AI products, figuring out if you can build it well is just as important as figuring out if you should build it at all.

Here’s our approach:

1. Identify the most critical customer problems

2. Assess what AI capabilities excel at (summarization, analysis, generation)

3. Map those capabilities to customer problems

4. Validate feasibility through extensive testing—can we achieve the quality bar?

5. Prioritize use cases where both value and quality are achievable

And we want to emphasize that this isn’t building solutions in search of problems, but rather being strategic about sequencing. AI excels at summarizing text and analyzing qualitative feedback, so we started there with Pulse. We intentionally pushed back on use cases requiring deterministic outputs or perfect accuracy. Save those for later when you’ve built the muscle for systematic quality improvement.

Once you’ve identified the right use case, you face another critical decision about how AI should help the user solve a problem.

Ask: Are we guiding or automating?

There’s a huge difference between:

• Automating = AI completes the task (ex: summarize this customer feedback)

• Guiding (or as Ravi puts it, Amplifying) = AI helps users think through the task and deliver better results (ex: what questions should I ask about this feedback?)

Users often say they want automation, but in reality, people are still wary of AI output quality (rightfully so!). The bar for full automation is much harder to achieve, and it’s one that users are skeptical of. AI tools need to build trust with users first. That’s why we see guiding as the current preferred operating model for product managers.

Plus, as AI becomes a strategic thinking partner, it becomes an embedded part of the users’ day to day workflows, driving deeper engagement and a stickier product. When we evaluated Spark, we realized PMs don’t need AI to just craft product artifacts for them, they need help thinking through customer needs, competitive alternatives, and business context.

“The process is not that you tell the agent, ‘Hey, write an initiative brief or PRD’ and it just writes it for you. The process rather is that the agent needs to guide you through the steps that you need to go through as a human in order to gain a deep understanding of customers, their needs, your existing product limitations, competing alternatives, and the context of your business objectives. That’s what Spark is about. It’s the richness of the agent interaction.”

— Hubert Palan, Founder & CEO, Productboard

This insight became our filter to create a valuable experience. Don’t ask “can AI do this task faster?” Ask “will AI help users make better decisions and take better action?” That’s where you find use cases worth building.

For Spark, this meant building an agent that enhances how product managers approach strategic thinking, not just automating their deliverables.

Match your interface to how users actually work

The interface shapes whether your AI becomes indispensable or just another tool. Most teams default to chat because it’s familiar, but that’s rarely enough. You need to make this decision during discovery, not after you’ve already built the backend.

Align your interface to the task type:

Conversational interfaces work best for:

• Exploration and discovery

• Text generation and editing

• Open-ended questions

• Individual work

• Empowering users who know what they want

Structured interfaces work best for:

• Visualization and comparison

• Collaboration in a shared, persistent view

• Prioritization and decision-making

• Workflow orchestration

• Guiding users who aren’t sure what they need

Most valuable AI products need both.

“The value in having structured interfaces is that they create a shared mental model for people to consume information, which creates consistency in how people then collaborate together.”

— Hubert Palan, Founder & CEO, Productboard

Think about it: if every sales rep saw a completely different Salesforce interface, collaboration would be impossible. The same applies to AI products. Assess your UI trade-offs early.

Rethink workflows, don’t bolt AI onto old ones

Once you understand your interface needs, your users’ entry point, you can rethink workflows from scratch:

“Good AI products realize that the world in the AI era is going to change the flows itself. They’re introducing new flows that are AI-first.”

— Dominik Ilichman, Senior AI Product Manager, Productboard

Many companies bolt AI onto existing workflows, creating “legacy tools with AI features somewhere.” The better approach: Start from the beginning. Reimagine workflows, especially ones that are only possible with AI.

To validate without over-building, start maximally flexible. Build the simplest interaction pattern, validate with users, then optimize specific workflows that show strong adoption signals.

The hardest decision: Building ahead of your customers

Once you know what to build, you face an even harder question: How do you invest in AI while supporting existing customers who aren’t ready yet?

We had to accept a painful reality: you can’t satisfy early adopters and the late majority simultaneously. Early adopters will embrace imperfect AI and provide valuable feedback. The late majority expects “AI magic”—polished, predictable, and high quality outputs.

Almost always, you have to pick who you’re building for.

“You have early adopters, you have early majority, late majority, and so on, and you need to start building. We know that AI is getting diffused into society faster than any other technology. And so you are putting more resources towards that, expecting that people will catch up. You’re building ahead.”

— Hubert Palan, Founder & CEO, Productboard

The case we made to existing customers: “Bet on a company that will lead in the AI era, even if you’re not ready today.” Because, the company pushing boundaries today will be the one delivering mature, reliable AI when customers are ready to adopt.

How do you ship fast without sacrificing quality?

Speed and quality feel like opposing forces in AI. Move too fast and customers hate the output. Move too slow and you miss the window. The key is knowing what quality bar matters at each stage.

An example framework: Three quality thresholds

We learned to think about AI quality in tiers, not as a binary. Here’s an example framework (...emphasis on example because specific thresholds will vary based on your use case):

Alpha: Internal validation (40-60% accuracy)

• Does the AI understand the task?

• Are responses valuable?

• Can we articulate why it fails?

Beta: Controlled customer testing (70-85% accuracy)

• Would users trust this output?

• Does it accelerate their work?

• Are failures recoverable?

• Do customers come back?

GA: Broad release (85%+ accuracy)

• Is quality consistent across use cases?

• Can we maintain this at scale?

If you don’t have explicit frameworks to measure these thresholds, you’re guessing.

I would be lying if I said we had quality out of the gate with Pulse. We were a little bit premature given it was our first foray into generative AI and the entire industry was learning in real time. We launched before the quality was consistent enough to build user trust. And more generally, expectations outpaced what the technology could deliver. But through that process, we learned a lot more about the guardrails and bounds necessary for bringing an AI product to market. We fixed the quality issues (Pulse is now at 95% accuracy, soon to be 99%), and applied those lessons to Spark: rigorous evals, stricter quality gates, and systematic improvement before each release stage (the framework you saw above).

Specifically, we used a combination of engaged user metrics and AI quality evaluation scores to measure quality and determine when we were ready to move between thresholds.

During private beta, our primary signal was engagement and activation. We closely monitored how participants were using the product, where they were getting value, and where they were dropping off. We paired those quantitative activation metrics with live customer feedback, and actively pivoted the roadmap based on that data until we saw consistent improvements in activation and repeat usage. This was our bar for moving into public beta.

In parallel, we built a more robust AI evaluation framework to measure output quality directly. We use a mix of automated and human evaluations, grounded in “golden” datasets made up of both curated and synthetic data. These datasets allow us to score AI outputs against specific quality dimensions that vary by agent. For example, for our customer feedback analysis agent, we track metrics like feedback citation accuracy and insight relevance.

Once in production, we continue to validate quality by sampling real user traces and scoring them to flag strong versus poor AI interactions. This helps us understand how outputs are being received by users in real workflows and ensures we’re consistently meeting the quality threshold required for a reliable, high-trust experience.

But, it’s not just our process that has evolved. Generative AI is changing user expectations, even from our enterprise customers. Thanks to the likes of OpenAI and ChatGPT, people understand that innovation happens quickly and you don’t want perfect to get in the way of good.

Our observation: customers will accept solutions at 70-80% accuracy if they see you improving it frequently (ideally weekly). Traditional enterprise software taught users to expect perfection at launch, then slow annual updates. AI has inverted this. Ship when it’s useful, iterate in public, show progress constantly.

With AI products, “beta” means “good enough to accelerate your work today, measurably better next week.”

The third dimension: AI quality

Traditional SaaS products have two dimensions: what users see (frontend) and what makes it work (backend). AI products have three.

“Traditional SaaS products have backend and frontend. With AI, there is the third dimension of AI quality that you really need to focus on.”

— Dominik Ilichman, Senior AI Product Manager, Productboard

This changes everything about estimation. Teams estimate AI projects like traditional projects. Frontend time + backend time = done. This is wrong.

“When we started building AI functionalities, we took the same approach. We implemented frontend, we implemented backend including the AI backend, and then we released it. And we failed often because we didn’t account for the time on the quality iterations.”

— Dominik Ilichman, Senior AI Product Manager, Productboard

The reality is that you might build the UI and backend in two weeks, then spend two months iterating on AI quality. And that’s where setting team expectations, especially with leadership, is critical. The time to build says nothing about how complex quality iteration will be. Give yourself room to fail (and get better).

How to systematically improve quality

Here’s the process that took us from 40% to 85% accuracy with Spark:

Step 1: Write test scenarios rooted in customer understanding.

Pick a specific use case. Write 20-50 example questions a user would ask. Create reference answers that represent high quality outputs. This becomes your evaluation dataset.

Here’s the important part: you need deep domain expertise to write effective evals. You must understand exactly what good outcomes look like for your customers and infuse your market knowledge and key guardrails into these scenarios. Shallow understanding means imprecise evals, which means inconsistent quality.

Some best practices we’ve learned:

• Prioritize evaluating individual quality dimensions/metrics: Single dimensions are easier for evaluation tools to judge accurately; combining several single-criterion evaluations into an evaluation suite gives you granular insight into what’s performing well and what needs attention. For example, instead of one evaluation asking “Does the AI provide a good response?” (multi-dimension), create separate evaluations for accuracy, completeness, actionability, and tone—this reveals you might score 90% on accuracy but only 60% on actionability, pinpointing exactly what needs work.

• Make sure you’re testing what you care about: Similar to writing unit tests, it’s easy to write too many or irrelevant evals. Identifying the exact dimensions you care about is a useful exercise to clarify what quality means and align the team.

• Leave room for improvement: If everything scores 100%, you can’t measure further improvements. Make evals stricter or add more dimensions to enable continuous improvement.

• Don’t over-index on scores: Evaluation scores are highly variable and not super useful on their own. Use them to direct human reviewers’ attention and development effort.

Step 2: Automate measurement. Feed scenarios into an evaluation tool. Get a quality score (often starting at 40-50%). Set a target threshold (usually 80-90%). Iterate on prompts, context, and architecture until you hit it. And make sure to track changes so that you can tie your eval efforts to product improvements.

Step 3: Enable internal testing. Release to internal users early and often. Make it easy to report issues (Slack channel). The team sees real failures and fixes them immediately. This catches edge cases evaluations miss.

At Productboard, we start every new AI capability with eval frameworks.

“Very often we start at like 40% accuracy and then we have a target, and step by step we iterate until we get to 80-90% of accuracy.”

– Dominik Ilichman, Senior AI Product Manager, Productboard

This takes weeks, but it’s the only way to ensure quality before customers see it.

Remember: Nobody has this figured out

The most liberating insight for us has been that we’re all figuring it out together. It’s trial and error on how to build agentic workflows, how to manage context windows, and handovers between agents. No one has it down perfectly right now.

You’re not behind because you don’t have all the answers. The teams winning are those failing fast and moving through the learning curve.

How do you structure teams that scale?

AI products require different team structures at different stages. The mistake is trying to scale too early or staying small too long. Here’s how teams typically evolve and the risks at each stage:

Phase 1: Small core team (3-5 people)

Start with a tight group who knows everything. The goal is finding product-market fit for one use case. Ship internally weekly while iterating on quality.

What works:

• Everyone understands the full system

• Decisions happen fast

• Context is shared naturally

• Iteration is rapid

The risk:

• Everything lives in people’s heads

• Nothing is documented

• You can’t onboard new people easily

Phase 2: Scaling team (10-15 people)

As you prove the concept and expand use cases, you bring in more people. This is where things get messy.

Because when you have one team do everything, they know everything. The context and communication exist in one room. But the bigger the scale and the more people involved, the harder it can be to ensure you’re all working in the same direction.

What breaks:

• New team members lack context

• Communication overhead explodes

• Velocity often slows down

• Quality becomes inconsistent

The solution: Teams need to know what they’re working toward. They need a strong product vision.

With traditional deterministic software, this might be a 12-month roadmap with clear features and timelines. AI products work differently. Your concrete, tactical vision might only look 3 months ahead, focused on specific use cases and measurable quality improvements. But you still need a longer-term north star: how you want to impact your users and the space you’re working in over the next year.

The key is grounding your vision in problems, not solutions. In traditional software, you’re constrained by your ability to deliver what you’ve envisioned. With AI, you often don’t know how the solution will look months from now, but you should have deep clarity on the problems you’re solving.

Keep your documentation as centralized as possible, and ensure everyone understands how their work connects to both the near-term goals and the broader mission

Phase 3: Specialized teams (20-30+ people)

As you build multiple use cases and complex features, teams need to specialize: one for navigation, another for editing experiences, another for building agents, another for orchestration, and so on, depending on your circumstances. But do note, AI is no longer it’s own team. As Ravi himself has told us, every PM is really an AI PM, every team is really an AI team. It’s part of the fabric of modern products, not something distinct and separate.

What’s required:

• Clear ownership boundaries

• Shared architectural vision

• Strong documentation

• Regular sync points

Your team structure should match your product’s maturity. Don’t specialize too early (you’ll lose velocity). Don’t stay generalized too long (you’ll build inconsistent experiences).

The fundamentals still matter

After a few years of building AI products and supporting teams that are building AI products, one truth became clear: the fundamentals of product management haven’t changed.

Understanding customer needs. Defining clear workflows. Building with quality in mind. Iterating based on feedback. Creating shared context for teams. These principles still drive great products.

What has changed is how these fundamentals manifest. AI introduces non-determinism that requires new quality frameworks. Team structures evolve faster. Timeline estimation practices must account for quality iteration. Building ahead of customer readiness requires new conviction.

But at its core, building AI products is still about solving real problems for real people.

“Working on Spark is the most exciting product I’ve worked on. From the very beginning we were solving a problem that I, as an individual contributor PM, faced massively. And from maybe the first week, we already had a prototype that I started using in my day-to-day life.”

— Dominik Ilichman, Senior AI Product Manager, Productboard

If you’re building AI products, start by matching customer pain to your tech capabilities. Build prototypes fast. Measure quality rigorously. Let your team evolve as you scale. And remember: everyone is figuring this out together.

The teams that ship great AI products aren’t the ones who knew all the answers from day one—they’re the ones who learned fastest from what didn’t work.

See how we brought all of these learnings to life with Productboard Spark. Try Spark in public beta today!

We took a three-step checkout flow for a weight loss medication program and replaced it with a twenty-five-step intake. Medical history. Lifestyle questions. Weight loss goals. Personalized projections. The works.

Conversion jumped 40%.

Not despite the added steps. Because of them.

The step-to-step completion rate is north of 99%. Each step fosters their confidence. By the time they reach checkout, they are ready. Confident. The decision feels right.

This flies in the face of conventional wisdom. We’ve been trained to believe that every form field is friction, every additional screen is a drop-off risk. Remove steps. Reduce friction. Get out of the user’s way.

But that advice is incomplete. And following it blindly can cost you conversions, customers, and ultimately the business you’re trying to build.

The Tinder paradox

Here’s what makes this interesting: I’ve also worked on products where the opposite approach is exactly right.

When Tinder launched, the dating market was dominated by Match and eHarmony. Both required 20 to 30 minutes of profile creation. Detailed questionnaires. Personality assessments. The implicit message: finding love is serious business, and we need to know everything about you.

Tinder took a different path. User’s login with Facebook (instead of setting up a new account). They pick a few photos (pre-loaded from their Facebook reel). Bios are short—only a couple of lines.

A new user could from “maybe I’ll try this app” to actually swiping on people in under two minutes.

The result wasn’t just faster onboarding. It was market expansion. Tinder pulled in millions of people who never would have created an eHarmony profile—not because they didn’t want to date, but because they weren’t willing to invest 30 minutes to find out if online dating worked for them.

So now we have two data points that seem contradictory. Twenty-five steps beats three. But also: ninety seconds beats thirty minutes.

What’s actually going on?

The wrong question

Most product teams ask: “Should our onboarding be short or long?”

The right question is: “What does a user need to feel confident at the moment of decision?”

This reframe changes everything. Length becomes a dependent variable, not an independent one. The goal isn’t minimizing steps—it’s maximizing confidence at the point where you’re asking someone to commit.

Think about this as “confidence engineering”.

Everyone in AI is talking about context engineering—giving models the right information to produce good outputs. Confidence engineering is the human equivalent: giving users the right information, at the right pace, to produce a confident decision.

Good friction vs. bad friction

The mistake most teams make is treating all friction as bad friction. It’s not.

Bad friction creates doubt. It interrupts momentum without adding value. It asks for information the user doesn’t understand why you need.

Asking a user to create a password is bad friction. It seems simple, but it’s not. It’s cognitively expensive. Should I use my usual password? Make up a new one? Do I even trust this site? Will I ever come back? The user came to experience your product. Now they’re solving a security problem instead.

Most teams treat password creation as a given—something they just have to do. It’s not. It’s one of the worst friction points in any signup flow. Tinder never asks users to create a password. An increasing number of products are following suit: magic links, one-time passwords, sign-in with Google or Apple. They’ve recognized that the best way to handle bad friction is to eliminate it entirely.

Good friction builds confidence. It demonstrates that you understand the user’s situation. It answers their questions before they ask them. It signals quality and care.

The Sesame intake works because every question serves double duty. Yes, we’re gathering medical history. But we’re also signaling: this is comprehensive care, not a pill mill. When someone is about to inject themselves with medication for the first time in their life, that signal matters enormously.

Tinder’s short onboarding works because the “Aha!” moment is different and the stakes are low. Get the user swiping, and they’ll start to experience the product’s value. The worst case? They match with someone they’re not that into, and they unmatch. Low stakes means you can shorten onboarding and accelerate time to value.

The variable that determines whether friction is good or bad isn’t the friction itself. It’s the relationship between that friction and the confidence required for the decision at hand.

When to add steps, when to remove them

This gives us a framework for thinking about onboarding design.

Variable 1: Decision consequence

How significant is the commitment you’re asking for? A free trial signup is low consequence. A $500/month subscription is high consequence. Swiping on a dating profile is low consequence. Injecting yourself with medication is high consequence.

High-consequence decisions require more confidence. Users need to feel certain they’re making the right choice, because the cost of being wrong is meaningful.

Variable 2: Information gap

How much does the user already know about what they’re getting? If your product category is well-understood and your value prop is clear, users arrive with existing mental models. If your product is novel or complex, there’s more work to do.

Large information gaps require more onboarding—not to create friction, but to close the gap and build the confidence that comes from understanding.

Plot these two variables against each other and you get a matrix:

• Low consequence, small gap: Ultra-short onboarding. Get out of the way. (Tinder, casual games)

• Low consequence, large gap: Brief education, then let them explore. (New social apps, novel utilities)

• High consequence, small gap: Streamlined but thorough. Confirm expectations. (Premium subscriptions in known categories)

• High consequence, large gap: Extended onboarding that builds confidence through comprehensiveness. (Healthcare, financial products, enterprise software)

Sesame’s weight loss program sits squarely in that bottom-right quadrant. High consequence, large information gap. The 25-step intake isn’t friction—it’s confidence engineering.

The Psych budget

Darius Contractor has a framework called Psych’d that adds precision to this thinking. The idea: users arrive at your product with some motivation—they are psych’d to get started. Your onboarding flow either fosters or squanders that initial motivation. We can model this behavior as a “psych budget” that gets added to or subtracted from as the user moves through your product.

Some steps add psych points. Personalized content. Value delivery. Moments that make users feel understood or excited.

Some steps subtract psych points. Password creation. Unexpected form fields. Anything that feels like bureaucracy rather than value.

Power tip: Pay careful attention to how you phrase questions to the user. A subtle change can make the difference between a question the user knows the answer to, or one that gets them stuck and likely to churn. For example, we originally asked users in Sesame’s intake, “What was the date of your last physical?” Think about it… do you know the answer to that question? We changed to” “Have you had a physical in the last six months?” Much easier to answer

The key insight: a good onboarding isn’t necessarily short or long. A good onboarding keeps Psych high by building confidence and delivering value throughout the experience.

Let’s look at two examples—one that gets it right, one that doesn’t.

Calm, a promise delivered

Open the Calm app and you immediately hear rain sounds. Before you’ve created an account. Before you’ve entered any information. Before you’ve done anything at all.

Calm’s promise is that it will make you calmer. And they deliver on that promise in the first three seconds of interaction. Their commercials do the same thing—fifteen seconds of rain sounds, no pitch, no features, just the product working.

When you can deliver value during onboarding, length becomes almost irrelevant. Users aren’t tolerating your intake process in hopes of future value. They’re already getting value. Every additional step is just more of what they came for.

The question worth asking: can your onboarding be the product? Can the process of getting set up actually deliver the value you’re promising?

For Calm, this is natural. For other products, it requires creativity. But when you can pull it off, you’ve solved the confidence problem from a completely different angle—not by building confidence through information, but by building it through experience.

“Just for You”, a promise broken

Here’s a counterintuitive example from my time at Tripadvisor. We built a new recommendation algorithm that showed users hotels they were more likely to book based on their browsing history. In blind testing, it outperformed our default ranking. People converted better.

Then we branded it “Just For You” and made it our default sort order.

Conversion dropped.

Same algorithm. Different results. Why?

Before, our algorithm was working silently in the background. Now, travelers felt like we were making decisions for them—and they didn’t trust us to know what was “Just for You”.

The feature introduced doubt. Travelers came to Tripadvisor psychd to use authentic reviews to make a better decision. “Just For You” threw a wrench in that. What invisible forces were pushing them toward certain hotels? Were we hiding great hotels? They wanted the transparency of traveler reviews, not the opacity of an algorithm.

The lesson: confidence isn’t just about what you deliver. It’s about whether users feel in control of their decision.

The messy middle

Onboarding teams often hit a common failure mode. They want to keep the experience short, but they sneak in extra questions to appease various stakeholders. A few fields here, a quick survey there.

The result? An onboarding without a clear strategy. Not short enough to feel effortless. Not long enough to feel comprehensive.

This is the death zone. It’s where products land when teams compromise between “we need more information” and “we should reduce friction”—without asking the harder question: what do users actually need to feel confident?

The death zone creates the worst of both worlds. Enough friction to lose impatient users. Not enough substance to win over uncertain ones.

Pick a lane. Get radically short and remove everything that isn’t essential. Or get comprehensively long and make every step build toward commitment. Or deliver value so immediately that length stops mattering.

Just don’t split the difference.

Applying “confidence engineering” to your product

Here’s how to put confidence engineering into practice:

First, identify your “Aha!” moment. What’s the experience that makes users understand your value? For dating apps, it’s getting into a conversation. For healthcare, it’s the doctor consultation. For productivity tools, it’s completing their first task. For Calm, its… feeling calmer. Everything in onboarding should be building toward that moment.

Second, assess the stakes… from the user’s perspective. What are you asking users to commit to, and what’s the cost if they’re wrong? Be honest about this. A free trial with easy cancellation is low consequence. A annual subscription with complex setup is high consequence.

Third, audit every step. Does this build confidence or create doubt? Does it signal quality or feel like bureaucracy? Would removing it make users more or less certain about their decision?

Fourth, look for value delivery opportunities. Can any part of your onboarding actually be the product? Can you demonstrate value before asking for commitment?

The goal isn’t short onboarding or long onboarding. The goal is confident users who are ready to say yes.

The conventional wisdom—reduce friction, remove steps, get out of the way—isn’t wrong. It’s just incomplete.

Friction is a tool. Like any tool, it can be used well or poorly. Bad friction creates doubt and kills conversion. Good friction builds confidence and earns commitment.

The discipline of confidence engineering is learning to tell the difference—and designing onboarding that gives users exactly what they need to feel ready for the decision you’re asking them to make.

Sometimes that’s 90 seconds. Sometimes that’s 25 steps. The number doesn’t matter.

What matters is whether users arrive at the decision point confident that they’re making the right choice.

Tim invited me to read an early copy, and I was struck by how clearly he’s distilled years of hard-won lessons into a coherent framework. His Progress Wheel is the best unified theory I’ve found for how all the pieces fit together—defining the right strategy, setting meaningful goals, and reducing uncertainty through discovery. It captures what I’ve seen work with high-performing teams: ruthless focus on what matters, not just what looks impressive. If you’re tired of roadmaps that are really just to-do lists, check out Real Progress.

Check out Real Progress by Tim Herbig

Stephanie, an experienced Director of Product, nervously walked into the strategy presentation meeting. After weeks of meticulous preparation, she was finally ready to present the revamped product strategy for GearSwap, a marketplace for outdoor gear and equipment, to the C-level. Strategy had always felt hard to grasp for her, so she stuck to the templates that seemed bulletproof.

As she walked the executives through the structure, she felt a weight coming off her shoulders. Many nodding heads and few critical questions made her confident in her choices. In essence, her Product Strategy conveyed this message:

“The GearSwap marketplace will proactively help weekend warriors and professional adventurers alike to match trip challenges with the right gear. They will benefit from peer-to-peer and professional seller options across all outdoor categories. Buyers will choose us because of community trust and expert curation, while sellers prefer the additional marketing tools we provide for them to go beyond their existing sphere of influence.”

All the fields of her chosen canvas had information in them and connected with the company’s Mission to “Equip outdoor enthusiasts with the gear they need.” Leaving the room, Stephanie looked forward to revisiting the product organization’s roadmap items and prioritizing them according to this strategy. After all, they would have clear guidance on what to work on beyond opportunistic customer problems or sales requests.

Two weeks later, Stephanie and her team sat down for a prioritization meeting. In it, several external requests came up from different sources:

• A competitor had launched a gear insurance offering, including services such as on-site 12-hour replacement of damaged equipment.

• One of the largest resellers on their platform requested an integration with their custom warehousing software.

• Across customer support requests, the search for water sports equipment surfaced (pun intended). Should they go deep into this vertical?

Stephanie thought, “Well, that’s what our new strategy is for,” and she and her PMs worked through the requests, aligning them with their product strategy. Instead of clarity, they found endless ways to craft a narrative that linked each of these requests to their strategy.

Insuring gear wasn’t per se misaligned with their strategy. Facing a broken gear on a trip could be a real problem that their buyers seemed to experience. But…was it one they wanted to solve?

They wanted individuals and companies to be sellers on the platform. To protect their inventory, they likely have to fulfill this request from the global reseller, despite their warehousing software having numerous technical quirks to deal with.

What Stephanie and her team experienced was what I like to call Alibi Progress: A strategy that ticked the boxes of a given template or canvas, but failed to create value for the teams creating it in critical decision-making moments. To prevent that, we need to revisit our product strategy and return to the basics.

Why product strategy must be decisive

Amidst all the fun templates and processes, it’s easy to overlook WHY teams need product strategy in the first place and what it’s supposed to do.

I like to summarize it as “Product strategy is about enabling a team to confidently say yes or no to opportunities coming their way over the next 6 - 18 months.”

And instead of universal “good strategy/bad strategy” definitions, I believe in the power of contextual guidance to improve practices. In the case of product strategy, I believe its value comes from paying attention to three attributes:

Valuable product strategy requires three attributes: being decisive (making clear choices), layered (connecting to company strategy), and executable (translating into daily decisions). This article focuses on the foundation: decisiveness. Without it, even well-layered and executable strategies become justification machines rather than decision filters.

We will revisit how Stephanie’s product strategy guides execution by deriving actionable goals. Let’s assess why its lack of decisiveness made it difficult to prioritize stakeholder requests and market observations.

The consequences of indecisive strategy

Instead of treating product strategy as a monolith that you have to make more decisive all at once, consider it a cohesive machine with different dials to turn. In essence, you have to make choices across a few core questions that the strategy needs to answer:

Why do you want to act now, and what long-term ambitions drive your actions?

For whom do you want to solve problems, and what are these problems? Who else tries to solve that problem?

How do you plan to reach your audience? What makes them choose you over an alternative?

The answer to each of these questions represents a choice. And the specificity of each choice influences how decisive, and therefore practical, your product strategy will be.

In GearSwap’s case, their answers to these questions simply weren’t specific enough:

Why do you want to act now, and what long-term ambitions drive your actions?

Their Answer: “Proactively help match trip challenges with the right gear”
The Problem: This describes what the product does, not why it matters now. Without urgency or market insight, every request can claim to “help match challenges”—providing no prioritization basis.

For whom do you want to solve problems, and what are these problems? Who else tries to solve that problem?

Their Answer: “Weekend warriors and professional adventurers alike benefit from peer-to-peer and professional seller options across all outdoor categories.”

The Problem: This serves everyone with everything. Weekend warriors want convenience; professional adventurers need specialized reliability. Both buyer segments could want gear insurance. All seller types could justify the warehousing integration. When the strategy never chooses an audience or category focus, the team can’t choose either.

How do you plan to reach your audience? What makes them choose you over an alternative?

Their Answer: “Buyers choose us for community trust and expert curation; sellers choose us for marketing tools to expand their reach.”

The Problem: “Community trust” (peer-validation, anyone can list) contradicts “expert curation” (gatekeeping, quality control). “Marketing tools” is generic enough to justify any seller feature—warehousing integration, seller profiles, analytics. Without specific differentiation or distribution channels, every feature request fits.

Making these answers more specific also impacts the specificity of some of the other answers. Improving your product strategy “machine” bit by bit.

How to create a decisive strategy

Instead of trying to change the aforementioned strategic monolith all at once, consider the individual components you use to assemble your strategy choices.

Here are the different components GearSwap could sharpen to make the overall strategy more decisive:

Strategic components aren’t independent choices—they’re interconnected gears in a machine. Turn one dial, and the others naturally adjust to match.

When GearSwap narrowed its audience from “weekend warriors and professional adventurers alike” to solely “professional adventurers planning multi-day backcountry trips,” a cascade followed:

Seller focus sharpened automatically. Professional adventurers need technical gear with detailed specifications—not John Doe’s family tent from five years ago, but specialized equipment like wilderness cooking systems or technical climbing gear. This demands niche commercial sellers and experienced individual adventurers who can provide item condition details that matter for high-stakes activities.

Problems crystallized from vague to specific. Instead of generic “finding gear,” the problems became concrete: buyers need to verify technical gear meets safety standards for high-consequence activities; commercial sellers need to differentiate certified equipment from casual peer listings that can’t guarantee safety; individual sellers need to demonstrate maintenance practices that justify premium pricing.

Value propositions and differentiation evolved naturally. With a narrow audience and specific problems, “community trust + expert curation” transformed into “certified technical gear from vetted sellers with expertise-backed recommendations.” Distribution channels came into focus: niche outdoor shop websites, technical activity subreddits (r/alpinism, r/Ultralight), and discipline-specific forums (Mountain Project, Backpacking Light).

Simply by making a few parts of your strategy more narrow and thereby decisive, the rest can follow that specificity: Your value proposition becomes sharper and more connected to the components of buyers, sellers, and problem space, alternatives are more tangible and therefore more evident to differentiate from, and distribution channels can be discussed with a target audience in mind.

As an example, the product strategy could now be presented like this:

Why now / Long-term ambitions: GearSwap’s mission is to “Equip outdoor enthusiasts with the gear they need.” As large global resellers add operational complexity that small specialty shops can’t match, there’s an opening: experienced adventurers who prioritize gear expertise and safety over lowest price are underserved. These customers need a platform that connects them with knowledgeable niche sellers who understand technical requirements. GearSwap will become the trusted marketplace where expertise matters more than scale.

For whom / What problems / Who else:

• Buyers: Professional adventurers planning multi-day backcountry trips requiring technical equipment.

• Sellers: Niche outdoor shops and experienced individual adventurers with technical gear expertise

• Problems: Verifying gear safety standards for high-consequence activities; accessing specialized equipment when needed; trusting seller expertise on technical specifications

• Alternatives: Large outdoor retailer rentals (limited specialized inventory), and peer marketplaces (no safety verification)

How to reach / Why choose:

• Distribution: Online presence through niche outdoor shop websites; active participation in subreddits for technical activities (r/alpinism, r/Ultralight); targeted forums for specific disciplines (Mountain Project, Backpacking Light)

• Differentiation: Certified technical gear from vetted commercial sellers and experienced adventurers, with expertise-backed recommendations

• Seller value: Connect niche outdoor shops and knowledgeable individual sellers with safety-conscious customers who value quality over price

When presented with the previous requests again, the team can handle them differently:

Gear insurance with 12-hour on-site replacement:

Does this strengthen our expertise-driven differentiation? Our advantage is pre-trip confidence in verified gear, not emergency services. This adds operational complexity without reinforcing what niche sellers offer.

Decision: 🚫 Decline. Focus on seller verification systems instead.

Warehousing software integration for global reseller:

Are large global resellers our strategic seller segment? No—they’re who we’re differentiating against. This optimizes for the wrong seller type and doesn’t help niche shops showcase expertise.

Decision: 🚫 Decline. Prioritize features for small specialty sellers.

Water sports equipment vertical expansion:

Does this serve professional adventurers on technical trips? Extended sea kayaking or multi-day whitewater trips align with our expertise positioning. Casual beach rentals for weekend warriors don’t.

Decision: 🤔 Narrow scope. Only pursue technical water sports categories if niche sellers exist.

It’s not about worrying about any of these narrow choices being right. No one knows if a chosen strategy is the right one beforehand. What matters is making the call from a place of informed conviction and then putting systems in place to measure progress against these choices.

From strategic choices to strategic goals

Stephanie now has a decisive strategy. She can confidently evaluate requests against clear choices about audience, problems, and differentiation.

But strategic choices are qualitative. Daily decisions need quantitative signals. The bridge between them is a simple question:

“12 months from now, which three metrics would tell us this strategic choice worked?”

For each strategic component—your audience focus, your differentiation, your problem-solving priorities—identify the metrics that would signal your efforts serving the strategic choices.

GearSwap’s strategic translation:

The GearSwap team chooses these metrics to translate into focused quarterly goals:

Objective: Establish GearSwap as the trusted marketplace for technical backcountry gear

Key Results:

1. Increase multi-day trip equipment transactions from 35% to 55% of total GMV

2. Achieve 80% of active sellers with “outdoor specialists” verification

3. Reduce average gear certification time from 5 days to 2 days

4. Increase transaction conversion for professional adventurer searches from 12% to 20%

The connection to strategic choices is what transforms these OKRs from mere reporting tools into decision filters and progress indicators, measuring the strategy’s effectiveness and guiding future development.

Each Key Result validates a specific strategic choice:

KR1 (multi-day trip GMV) measures whether the professional adventurer audience focus is working.

KR2 (specialist verification) tracks the shift to niche sellers over casual listers.

KR3 (certification time) tests whether the safety verification differentiation can scale efficiently.

KR4 (search conversion) indicates if the expertise positioning builds the trust needed for technical gear purchases.

Now, when decisions arise, the team has both strategic criteria and progress indicators. Engineering’s algorithm rebuild? Evaluate against KR4. New seller onboarding features? Check impact on KR2. The OKRs don’t just measure—they guide.

Connecting the dots with the Progress Wheel

The connection between strategy and goals isn’t one-directional. It’s part of a larger system.

Strategy guides what to measure. But how do you know which strategic choices to make in the first place? That’s where a domain like product discovery comes in.

The Progress Wheel

Think of strategy, goals, and discovery as three interconnected practices that strengthen each other:

Strategy → OKRs: Strategic choices generate specific metrics worth tracking (as we just saw with GearSwap)

OKRs → Discovery: When metrics stall or surprise you, they reveal which assumptions need testing

Discovery → Strategy: Customer evidence validates or challenges your strategic choices

The cycle continues: Each practice informs and strengthens the others.

Strategy is a continuous system

Stephanie, with a decisive strategy in hand and a clear set of OKRs, embarked on the quarter. Midway through the quarter, she hit a snag. Her team committed to improving the transaction conversion rate from 12% to 20%, but they couldn’t improve the conversion rate beyond 14%.

Strategy and Goals are two legs of a three-legged stool—incomplete without Product Discovery.

Stephanie kicked off Product Discovery work to understand: “Why are professional adventurers dropping off before purchase?”

User interviews revealed something unexpected: experienced adventurers plan technical trips 6-8 weeks in advance, but most GearSwap gear isn’t listed with advance availability windows. They’re abandoning searches because they can’t plan ahead.

This discovery insight creates two ripple effects:

1. Immediate tactical response: Test features around advance reservations and availability calendars

2. Strategic refinement: The differentiation might need to evolve from just “certified gear + expertise” to include “guaranteed availability for planned expeditions”

Next quarter’s strategy incorporates this learning. Next quarter’s OKRs measure new metrics around advance purchases. Discovery continues validating whether this refined positioning resonates.

The wheel keeps turning. Strategy isn’t a one-time document. Goals aren’t just quarterly scorecards. Discovery isn’t a phase. They’re a continuous system where each practice makes the others more effective.

This interconnection—what I call the Progress Wheel—is what separates teams making Real Progress from those stuck in Alibi Progress. The details of how to strengthen each connection, what discovery methods work when, and how to time these cycles across quarters is beyond what we can cover here. But the core principle applies immediately: when you’re stuck in one domain, the answer often lies in strengthening its connection to another.

Your strategy can finally help you say no.

Your goals can tell you if that strategy is working.

And when they don’t align? That’s not a failure. It’s the signal to look deeper--to discover what your users really need.

The prototype we’ll build: a music discovery timeline showing how genres evolved from their earliest days. It has good typography, actual album information, real cover art from Spotify, and smooth animations. The kind of prototype you’d put in front of customers, not a wireframe you’d apologize for.

That used to require days or weeks with a full team. Now it takes 30 minutes and a little bit of design thinking.

We’ll cover three things: how to rapidly wireframe your ideas, how to generate realistic data that brings those ideas to life, and how Figma Make combines them into something that feels real.

This post is brought to you by Figma Make.

Figma Make transforms your ideas into interactive prototypes in minutes—no coding required. Start from your existing Figma designs, bring in realistic data, and iterate through conversation. Try Figma Make free and see how fast you can go from blank canvas to customer-ready prototype.

Would you hire an architect who couldn’t draw blueprints?

Imagine you’re about to remodel your house. Your architect has been working diligently and she’s ready to share her plans. You sit down, and she hands you… a Word doc?!

“Trust me, it’s going to be great.”

Would you write the check? Of course not. Architecture is a visual medium. You need to see the structure, the proportions, how spaces connect. Words aren’t enough. You need blueprints.

Digital products work the same way. They’re visual and interactive. You can’t fully communicate a product idea through written specs alone.

For years, I’ve encouraged every PM I’ve worked with to learn wireframing. User experience design is an essential PM competency, not just the designer’s job. As I wrote in How To Become a Peak Product Manager:

The best PMs don’t just describe customer needs, they work with designers to translate those needs into easy-to-use experiences.

Here’s why this matters. When you write “the user taps the album cover and sees track details,” everyone pictures something different. Does a drawer slide up? Does the whole screen transition to a new view? The right answer depends on the user’s intent and the workflow you’re building.

You can’t leave those decisions to chance.

Instead, when you show the interaction visually, everyone sees the same thing. That’s what blueprints do.

Wireframes → a better way to prompt

Most people prompt AI design tools with words. But AI has the same problem your team does: words aren’t specific enough. Software is visual. Visual prompts work better.

Wireframes aren’t just a bridge between product and design anymore. They’re the blueprints that guide your whole team—humans and AI.

From blank canvas to beautiful prototype

Here’s exactly how I went from blank Figma page to working prototype. The process is surprisingly simple once you understand the pieces.

👋 Watch the video to follow along as I go through every step.

1. Start with the Simple Design System

I know, I know. The blank page is intimidating. What font will I use? Does that font size look nice? Should I bold the headlines? How do I create a button? Where do I find nice looking icons?

A few months ago, Figma introduced Simple Design System—available in every new file. Over 400 components, typography presets, colors, icons, and even full layouts.

To get started, I drop an iPhone 16 frame onto the page—again, an easy Figma preset that moves decisions off my plate.

2. Auto-layout everything

My next simplification: use Figma’s auto layout feature for everything. No manual positioning—Figma arranges things vertically or horizontally automatically. I even turn it on for the main iPhone 16 frame since mobile UI is just a vertical stack.

Using auto layout, I add some margins as well. I set 24 pixels for top and bottom margins, 16 pixels on the sides.

Pro tip: use multiples of 4 or 8 for spacing. It keeps everything aligned to a grid even if you don’t know design theory.

3. Rough out your first elements

Let’s design a feature to explore a genre’s history through a timeline of seminal albums.

I want a cover photo at the top. So I add a gray square as a placeholder. Figma Make will fill this in with a real image later.

Name the layer “cover photo.” That naming matters—you’ll see why.

I add a title: “Downtempo.” We’ll build our wireframe around this genre, then use Claude to generate real data—artist names, album titles, descriptions—to make the prototype feel authentic.

The default text looks terrible. Wrong size, wrong font. But this is where Figma’s Simple Design System saves us. It has pre-built typography options. I choose “Title Page” and suddenly the text looks professional. I didn’t make any design decisions—I just picked from styles that already work together.

I add a description below the title, and set the text to the “Body Base” style. It fits in seamlessly.

Next, I add tags for the genre’s most important artists using Figma’s Tag component. Set horizontal auto layout, enable wrapping. Done.

After about 10 minutes, I have a wireframe that looks pretty clean. But it’s still just boxes and placeholder copy.

4. Mix in realistic data

Great wireframes need great copy. The shapes show layout. The words explain the experience.

So far, the copy in my wireframe is “FPO”—for position only (fancy designer term!). I could spend hours researching Downtempo music—writing genre overviews, compiling artist lists, finding album details.

Or I could use AI, which excels at this kind of content generation. Here’s the prompt I gave Claude:

I’m interested in the history of downtempo music. Generate a JSON file that includes the genre name, a description, and an array of 15-20 milestone albums. For each album include: name, release date, artist, a 1-2 sentence description of why it was a milestone, and relevant tags.

Minutes later, I have what I need. Real artist names. Actual album titles. Release years. Well-written descriptions: “Mezzanine by Massive Attack (1998) - This dark, brooding album pushed trip-hop into more experimental territory with its heavy use of guitar samples and collaborative approach with vocalists like Horace Andy.”

This isn’t Lorem Ipsum. This is data that tells a story.

I asked for JSON because structured data is easier for AI tools to parse—important when we build our Figma Make prototype later.

But I’m missing something: album covers. I want real album art, not placeholder images. This is a little tricker for AI.

Claude won’t able to reliably look up album covers without a little help from a friend. I built an MCP server that fetches album covers from Spotify and Deezer. Took about 30 minutes in Claude Code (maybe a future post?). Now Claude can grab the real artwork for any album.

I ask Claude:

Add album covers to this dataset using the album-cover MCP server.

This will take some time. Let’s go back to Figma while Claude does its thing.

5. Finish the wireframe

I want each year to show its important albums. I layout the album UI: cover art, artist name, album name, description, and tags.

Every element is from the Simple Design System and auto layout makes it easy to get the UI laid out nicely. As needed, I pull over some copy from the JSON we generated to get a feeling for how it will look with real data.

In less than 20 minutes, the wireframe is done:

6. Send to Figma Make

Now, we’re ready to build the prototype. Right-click the design and select “Send to Figma Make.”

My prompt is dead simple:

Based on the attached design, build a feature that shows the timeline of a music genre. Use this data to build the prototype:

I paste in the JSON.

Figma Make reads my wireframe—the structure, the layer names, the semantic organization—and generates a working prototype. Real album covers appear in the right places. Artist tags populate at the top. The timeline structures itself by year with albums underneath.

Figma Make understands that the wireframe provides structure and the JSON provides content. It even replaces my placeholder hero image with an actual photo.

No code written. No manual placement of 20 album covers. Just a wireframe, a data file, and AI doing the rest.

7. Iterate with taste

The first version works, but I want to refine it:

Make the cover images larger and add a play button for each album.

Figma Make updates the prototype. Bigger album covers. Play buttons appear on hover with a nice animation.

Let’s change to a dark theme with full rounded buttons. Use this green as the primary color: #1ed760. Use dark gray for the cards #1f1f1f and even darker gray for the background.

The entire design transforms. It starts looking like Spotify. Professional. Intentional.

Add a cool animation to the year as a user scrolls down.

As I scroll down the timeline, each year animates in with a subtle effect. The albums fade in as they enter the viewport.

Each time, Figma Make doesn’t just do what I ask—it adds taste. Hover animations. Subtle fades. Professional polish.

Making the most of Figma Make

Most AI design tools ask you to describe what you want in words. Figma Make flips this: start with a visual, then AI builds from it.

After building this prototype, a few things stood out:

1. First pixel to working prototype in one tool. You wireframe in Figma, generate the prototype in Figma, iterate in Figma. No export/import dance. The entire workflow stays in the canvas where you’re already thinking spatially.

2. Simple Design System removes guesswork. You focus on visual thinking—layout, hierarchy, user flow—not all the details. The components have proper padding, correct spacing grids, typography that scales. You snap together pieces that work.

3. Semantic understanding beats screenshots. Sure, you can paste screenshots into any AI prototyping tool. Figma Make reads your actual layers, components, and structure. Screenshot tools see pixels. Figma Make sees meaning.

4. Built-in taste. Good spacing defaults. Typography that looks right. Animations that feel smooth. You don’t need design expertise to ship something professional.

The result? I went from idea to customer-ready prototype in just 30 minutes.

Better prototypes = better signal

Design-driven prototyping enables teams to accelerate the time from initial hypothesis to initial test. More importantly, it makes that test more reliable by helping teams create better prototypes.

Bad prototypes break the “suspension of disbelief.” Customers have to imagine what the product will eventually look like when it’s “real.” Users act like they’re using the product instead of actually using it. That creates noise.

Great prototypes maintain the suspension of disbelief. They feel real, so users forget they’re testing. That creates signal.

Over to you…

You don’t need years of design training to create something worthwhile.

Next time you have a product idea, don’t write a spec. Open Figma. Drop in a frame. Add simple components. Sketch the structure—what goes where, how it flows.

Then send it to Figma Make and watch it transform into a working prototype. Put it in front of customers. That’s real signal—signal that helps you make better decisions.

With AI, visual thinking is your new superpower.

This post is brought to you by Figma Make.

Figma Make transforms your ideas into interactive prototypes in minutes—no coding required. Start from your existing Figma designs, bring in realistic data, and iterate through conversation. Try Figma Make free and see how fast you can go from blank canvas to customer-ready prototype.

At Tripadvisor, trip planning was one of the hardest features I worked on. Trip planning is deeply personal—everyone does it differently. We ran research sessions with travelers to understand their workflows. Several showed up with dog-eared notebooks scrawled with handwritten notes and stuffed with Post-its.

The notebooks told us everything we needed to know about what people wanted. But our paper prototypes couldn’t capture the dynamic reality of trip planning—the back-and-forth between travelers, the way plans evolved as people added ideas, the messiness of group decisions.

Today, we could put a working prototype in front of those travelers in an afternoon. That would have accelerated our learning and gotten us to the right solution weeks faster.

Vibing our way to a prototype…

Let’s start with a simple prompt:

Make a website for planning a Paris trip with multiple people, include some activities, hotels and restaurants, over three days, add user profiles and let people comment on things. Make it look nice.

I know, I know. This is phoning it in. Reforge Build thinks we can do better and asks me some follow-up questions:

I comply:

Next, Reforge Build shows me a preview. Building the full prototype will take some time, so this visual preview allows me to quickly iterate on the design before jumping into the full build.

I’ve asked Reforge Build to move the participants to the right:

Reforge Build creates a detailed plan and starts an in-depth build process:

And here is the first version of our prototype:

The trouble with “vibe prototyping”

We’ve actually gotten a remarkable result considering how little effort we put into the original prompt, but could we put it in front of users?

There are some layout issues which we could fix with some prompting, but there is also a deeper issue. A photo of the Eiffel Tower is used for the Sacré-Cœur Basilica and Sainte-Chapelle. This discrepancy ruins the “suspension of disbelief”. Put this in front of a user during testing, and they immediately know it’s fake. They stop engaging naturally with the experience.

Your research is compromised before it begins.

There’s another issue. The prototype is inflexible. Want to test it with a traveler who is planning a trip to Thailand instead of Paris? You have to start over completely. Different destination, different prompt, different prototype.

This is the difference between a toy and a tool. Toys are cool demos that impress for 30 seconds. Tools are decision-making artifacts that help you ship better products.

Why “vibe prototyping” falls short

When you ask AI to “make a trip planning website,” you’re asking it to simultaneously:

• Figure out the UX (what screens, what interactions)

• Design the visual look (colors, typography, layout)

• Create the data model (what fields, what relationships)

• Generate realistic content (names, photos, descriptions)

It’s like asking one person to be designer, PM, engineer, and content writer at once—all with very little guidance. The result? Average across the board.

Taking your prototypes to the next level

A prototype is a tool to help you make better decisions about what to build.

Usually, that means we need to put them in front of fickle customers. So, your prototype needs to look and behave like your actual product. A good prototype fulfills three types of requirements:

1. Design requirements: The prototype looks like your product and seamlessly extends your visual language. Users should feel like they’re interacting with your actual product, not a generic template.

2. Functional requirements: The prototype behaves like your product. If your product has certain patterns or conventions, the prototype should honor them.

3. Data requirements: The prototype feels like its build on authentic customer data, not fake, placeholder data.

You can’t vibe your way into this. Building a good prototype requires similar planning to building real products.

For most PMs, this means developing a new muscle: data modeling. The design requirements and functional requirements come naturally. We’re used to writing specs and working with designers. But, we typically leave the data modeling to engineering.

Let’s see how we can use a data-driven approach to build a more authentic and flexible prototype. Detailed step-by-step instruction are included below. You can follow in Reforge Build or any other prototyping tool.

Need a live demo? Watch me walk through this on Claire Vo’s How I AI podcast:

Step 1: Generate realistic, structured data

We’ll actually start in Claude rather than Reforge Build. I’ve found Claude to be a great tool for generating mock data for two reasons: 1) its output feels very authentic and 2) Claude can work with a wide array of MCP servers (more on that below).

I asked Claude to generate data in JSON format. JSON (which stands for JavaScript Object Notation) is kind of like HTML for data… its a universal format that apps can understand which incorporates both the structure and content underlying the data.

I used this prompt to generate the data:

Using JSON, generate a sample itinerary that I can use to prototype a shared trip itinerary feature. The destination is Paris. The itinerary should include an itinerary name, cover photo, and a date range covering three days. There should be 3-4 travelers associated with the itinerary. Each traveler should include a first name, last name, avatar photo, and preferred travel style like "Foodie", "History Buff", etc. For each day, include a collection things to see on each day. There should be 12-15 items in total. The items should a hotel for Day 1 and popular things to see (such as the Louvre or Eiffel Tower) on each subsequent day. Each item should include a name, a start time, a duration, a star rating, a number of reviews, tags to describe the item (like Historic Sites or Cultural Landmark), a photo, and a short description. Some items should have notes from one or more travelers. The notes should be in chronological order and respond to each other like a message thread for each itinerary item. Use unsplash-mcp-server to find photos for the cover photo, avatar photos, and itinerary items.

The prompt is specific about what content we’re looking for. It also asks for imagery (for both people and places). This is thorny… LLMs don’t have reliable imagery data in their training sets. They’ll often hallucinate URL’s that don’t exist (leading to broken images) or pull in low-quality results.

We can solve for this by using an MCP server which allows Claude to access data from another source. In this case, I’m using the Unsplash MCP Server which takes a query (like “Eiffel Tower” or a hotel name) and returns actual URLs for relevant photos from Unsplash. This means our mock data gets real, high-quality images.

Generating the data takes a minute. Claude is generating complex JSON while making multiple API calls to Unsplash for every user and place generated. But the result is worth it: an incredibly detailed, authentic dataset with accurate image URLs.

💾 You can download the fill paris_itinerary_sample.json file here.

NOTE: Instead of generating mock data from scratch, you’ll likely be able to assemble it using anonymized data from your existing product.

Step 2: Generate the prototype from your data

Now, we’re ready to go back to our prototyping tool. Since we have structured data, the prompt is simple:

Generate a trip itinerary feature based on the sample data below:
[paste in the JSON data]

The data schema provides unambiguous context to the LLM… a rock solid foundation to build on. Now, your prototyping tool can focus purely on UX instead of juggling design, data modeling, content generation, and business logic simultaneously.

Sidebar: You can think about this as “rolling your own” multi-agent system. Here we’ve used two tools, Claude and Reforge Build, as subagents: one to create structured data and the other to build our prototype. The combination of two specialized “agents” (rather than one LLM working with a single context window) delivers much better results—and helps us avoid context rot.

The result: A beautiful prototype with relevant photos. Detailed itineraries. Realistic traveler profiles. Message threads that sound like actual users. Something we can put in front of customer.

Here is what Reforge Build generated:

Step 3: Test & iterate

Here’s where the data-first approach pays off again. Reforge Build generates clean, modular code that maps directly to our data structure. This makes iteration faster and more reliable.

For example, we can easily add a feature to show gaps in the day:

Add blank cards if people have time in between activities. 

The result is dynamic based on the itinerary data (notice the prototype has calculated the start time and duration of the free block):

We can even go back into Claude and ask it to generate an entirely new itinerary based on the same data schema. The prompt in Claude is trivial:

Now generate an itinerary for the same travelers for a four day trip to Thailand.

A few minutes later, we get an entirely new itinerary:

💾 You can download the fill thailand_itinerary_sample.json file here.

Updating the prototype is easy. The data is isolated from the rest of the prototype, making it easy to change directly and ensuring that changes flow cleanly to the rest of the app.

In Reforge Build, you can access the Code View to see all of the code that has been generated. In there, we'll find a file with the itinerary data (in this case, under data/itinerary.ts):

We can simply paste in the new JSON data and hit reload. Viola! We’re on our way to Bangkok:

We now have a prototype that is flexible, reusable… and, most importantly, testable with real users across multiple scenarios.

Why this works (and what it means for your team)

Prototypes should be tools, not toys.

To live up to that promise, you need a sophisticated approach to prototyping that gives AI the context needed to build powerful, flexible, and authentic prototypes of your product.

Design, functionality, and data—the three legs of the stool—are each vital to building a strong prototype. Most teams have design and functional requirements covered. But data gets overlooked, and it’s the foundation.

By making data a first-class concern in your prototyping workflow, you’ll build believable prototypes you can put in front of users.

That’s the difference between a cool demo and a process that helps you ship better products.

Check out Reforge Build

The data-driven technique we discussed works with any prototyping tool. But that’s just the start. Product teams need a tool that fits into their workflow. Reforge Build is design to tap into established systems:

• Design system. Start prototypes that match your design system using the browser extension, screenshots, or Figma integration

• Knowledge system. Store customer feedback, product strategy, data schemas, and product docs at company and project levels

• Discovery process. Create and compare multiple design variants simultaneously to explore different directions

• Team collaboration. Share, comment, and remix prototypes directly. Turn any prototype into reusable templates for your team.

• User research. Get customer feedback through Reforge Research AI interviews and surveys (coming soon)

Try Reforge Build

In “Founder mode for the rest of us”, I discussed the idea that founder mode isn’t reserved for founders. Every great leader I’ve worked with operates with the level of ownership, speed, decisiveness, and attention to detail that defines a good founder. Which means you’ll encounter these leaders throughout your career… and working with them requires a completely different approach.

High-performance leaders demand more than the standard playbook for “managing up”—they move faster, dive deeper into details, and expect you to operate with the same level of ownership they do. In this post, I’ll share what I’ve learned about winning trust, maintaining credibility, and thriving alongside leaders who operate at this intensity.

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More than busywork

As you get more senior, a frustrating trend emerges: less time with your team, more time aligning leaders and executives:

• As an individual contributor, you might spend 10% of your time managing up.

• As a new manager, that jumps to 30%.

• By the time you’re a senior leader, you’re often spending half your time coordinating with other leaders and executives.

This isn’t busywork—it’s the nature of senior leadership. Your path to success fundamentally changes. Instead of tangible deliverables, your impact is measured in influence.

Here’s the tension: You increasingly need leadership support to assign resources, back decisions, and provide strategic context. But the leaders you need support from have less time, limited context, and must balance competing priorities.

These challenges intensify when working with leaders who operate in “founder mode.” They’re context-switching constantly, dealing with challenges you have limited visibility into, and making rapid decisions with imperfect information.

As a result, it can be difficult to get the time and attention you need from executives.

This can be frustrating. Why would a leader ask you to work on something they don’t have time to support?

In a perfect world, senior leaders would be able to give full time and attention to every person in their org and every product initiative. In reality, successful executives instinctively spend their time and attention where they feel it will have the highest ROI.

Managing up is the art of breaking through the noise to convey the value of what you’re working on and get the support you need—and it’s one of the most effective ways to accelerate your career.

Let’s look at three habits that help you build strong relationships with high-performance leaders:

1. Put yourself in their shoes

2. Foster their confidence in you

3. Make clear, compelling asks

Habit #1 → Put yourself in their shoes

Few people take the time to understand what it’s like to be an exec at a fast-growing, high-performance company. It’s easy to look at an executive’s title, compensation, and status and think: “I’d be happy to have your problems.”

Some people assume senior leaders have plenty of time to think and coach—that’s their job, after all. In reality, senior execs must switch contexts constantly, ruthlessly prioritize, and end each day knowing they just scratched the surface of what needs their attention.

Elon Musk once reflected on how he spends his time:

“I don’t have a lot of open space. It’s generally back-to-back meetings and it’s insane...my days are like insane torrents of information. I don’t recommend it. I’ve been thinking, how long can I keep this up? Because I don’t want my brain to explode, and the meetings that are scheduled are not just nice-to-have, they’re meetings that are essential.”

Think about what it’s like to be an executive at a high-performing company:

• Their day is stacked with 6-8 hours of essential meetings, barely scratching the surface of demands on their time

• They receive hundreds of emails daily and spend non-meeting time managing constant information flow

• They’re held accountable for aggressive goals, often shaped by factors beyond their control

• They’re dealing with challenges you have limited visibility into—reorgs, fundraising, layoffs, poor performers, board meetings

All of this may sound familiar—you may be in the same boat. In the face of these demands, senior leaders need to feel confident their teams are making progress. They gravitate to leaders who worry about the same things, run toward hard problems, and take ownership.

You can foster the right mindset by asking the right questions about your boss and other key leaders:

• What is top of mind for them person right now?

• How could my work create leverage to support their priorities?

• What context do they need on my work to do their job better?

• What are the high leverage ways they can support my work?

• How do they like to consume information and make decisions?

• How do they like to give and receive feedback?

• What does success look like for them—personally and professionally?

These questions help avoid a common trap. Many product leaders conflate their priorities with their boss’s priorities, assuming “my success is their success.”

In reality, your boss is facing an array of priorities, constraints, and stressors you don’t see and may have nothing to do with what you’re working on. Shift your point of view and frame your work in terms of how your boss defines success—not what is necessarily best for you and your team.

A few weeks after starting at Tinder, I had a 1-on-1 with the PM leading Messaging. I asked, “What’s the most important thing on your roadmap?”

He paused. “Can I be real? Nothing in Messaging will move the needle. The critical path for the product is before matches even happen.”

Rather than advocate for his team, he shifted to my perspective by asking what was best for the product overall. It served him well. He was the first person I thought of when we needed to build a new pod for one of the most important initiatives in the company.

Habit #2 — Foster their confidence in you

When executives have confidence in you, you’re able to operate with more agency and have a much easier time getting buy-in.

Executives have limited resources to allocate, imperfect information, and are constantly making trade-offs. This can create situations you look around and see resources going to teams that don’t seem like they need them… while your team isn’t getting the resources despite needing them much more.

Getting resources isn’t a matter of being right. There are many competing answers on how to spend resources—all of which seem right depending on your perspective. Resources gather where leaders have confidence.

Executives aren’t asking themselves: Does this team need more resources? (Invariably, the answer is “yes”). Instead, they ask themselves: Where can I deploy limited resources to get the maximum ROI?

Confidence compounds, for good or bad

Building executive confidence creates a virtuous cycle: (1) you make the most of the resources available, (2) your executive’s confidence in you grows, (3) it’s easier to get future buy-in, (4) you get more resources and support—and the cycle repeats.

The reverse is also true. A lack of confidence leads to a vicious cycle:

The Credibility vs. Alignment Map

There are two components to developing an executive’s confidence in you:

1. Personal Credibility: How effective have you been in the past when you’ve gotten buy-in?

One of the things that becomes increasingly important as you get more senior is your ability to predict and then achieve the future. Public company CEOs are constantly evaluated on this—they must set quarterly guidance, commit to targets publicly, and then deliver against those commitments regardless of market conditions or unexpected challenges. Missing guidance has severe consequences: stock price drops, board pressure, loss of investor confidence.

This is why CEOs and executives value leaders who can shoulder the same responsibility: forecasting what’s achievable, committing to it, and then making it happen despite changing circumstances. They need people who won’t just try their best, but who can confidently say “we will deliver X” and then actually deliver it.

2. Alignment with Executive: How aligned is what you’re trying to accomplish with what matters most to the executive?

One of the first questions executives ask themselves is: “Is this what I would do if I were in this person’s role?” The closer the answer is to yes, the more confidence they have.

This isn’t about executives micromanaging—it’s about pattern recognition. They’ve built their judgment through years of decisions and outcomes. When you’re pursuing an approach they would take themselves, it validates both your judgment and theirs. More practically, when your work directly connects to their top priorities—the commitments they’ve made to the board, the metrics they’re accountable for—supporting you becomes an obvious decision rather than a risk.

Executives want to see you succeed, and their instinct is that the best way for you to succeed is to do the things that they would do.

However, the goal isn’t to stay in total alignment. The best product leaders take smart risks and consistently deliver more than what is expected.

Are you out on a limb?

Taking risks is a dance, because it often means doing things that are out of alignment with an executive’s perspective.

Think of it like going out on a tree limb—there are four stages:

1. Executive Alignment: You’re not that far out. You’re doing something the executive would do themselves. If it fails, you don’t lose much credibility—the executive views it as their own lapse in judgment.

2. Executive Support: You go a bit farther out. The executive’s stance: “I don’t know if this is the right thing, but I trust your judgment.” If the initiative fails, you’ll lose some credibility, but it’s tempered by the exec thinking: “I enabled this decision and share accountability.”

3. Executive Permission: You’re far out on a limb. The executive’s stance: “I don’t believe this is right, but I’ll let you do it.” High risk, high reward. Success impresses the executive with your judgment. Failure costs you significant credibility—the exec thinks: “As I expected, I was right and they were wrong.”

4. Executive Disagreement: You’re over the limb. The executive’s stance: “I don’t believe this is right, and I won’t let you do it.” This may cost you some credibility depending on how compelling your case was and how well you “disagree and commit.” Persisting after being overruled will further undermine your credibility.

The challenge? You don’t always know how far out on the limb you are. You want to deliver beyond expectations, but you need to do so without eroding confidence.

This is why the Credibility vs. Alignment map is valuable—it helps you navigate that tension. Executives see you as an asset when you either: (1) align to their priorities and help achieve them, or (2) have earned a track record they can count on.

A few years ago, I worked with two Director-level product leaders who started around the same time. Min led a larger team working on what was considered the company’s future. Max led a smaller team working on impactful but less strategic initiatives.

Max’s smaller team delivered immediately. When he saw opportunities to do more, he led with impact: “With more engineers, I can achieve X% more growth.” His team grew slowly—each time, Max achieved more with the additional resources.

Min’s team also delivered, but not as much as leaders expected. She said she didn’t have enough resources and couldn’t do everything being asked. She needed more to do her job.

Her team grew too—after all, she was working on the future of the business. But each time, she found reasons for needing even more engineers, PMs, and designers. Leadership’s confidence began to waver. The CEO wondered why such a large team wasn’t doing more.

Eventually, Min’s team stopped growing, and people questioned whether she was the right leader. Max took over parts of her team, and Min’s career stalled.

For Max, the virtuous cycle was in full motion—he became one of the most senior executives at the company. Min got caught in a vicious cycle that undermined her promising career, despite outward signs of success like a growing team.

Habit #3 → Make clear and compelling asks

As we discussed earlier, “managing up” is ultimately about getting the support you and your team need to be successful. When making asks of executives, your job is to help leaders help you.

Maybe this sounds familiar: you’ve painstakingly defined a plan and you’re simply looking for approval to kick-off. Instead, your boss or others offer a raft of unrelated or distracting ideas. You leave the meeting without the approval you need and with more unanswered questions than when you entered.

A common saying goes: “When given a choice between A or B, most senior executives will choose C.” Execs are trying to add value with limited context, so it’s not surprising that they try to provide feedback from first principles rather than “sticking to the script.”

Be clear: what are you asking for?

The first step is to be clear about the scope and purpose of your ask. This focuses leadership’s attention on what matters most.

There are five ways executives can provide value, and each requires a different conversation. Making it clear what kind of discussion you want to have sets the stage for an intentional discussion rather than a general one. Here are the five most common types of asks:

1. Decision: Need executive to make a decision (e.g., should we prioritize customer segment A or B?)

2. Resource: Need additional resourcing for product work (e.g., approval to hire another PM)

3. Action: Need executive to take specific action (e.g., approving a promotion for a higher performance)

4. Input: Need executive input to help inform your decision-making (e.g., context from the latest board meeting)

5. Alignment: Need to share information with an executive that doesn’t require any specific action (e.g., flag a performance issue that may require future escalation)

Provide the context necessary to make a decision

Next, prepare the right context. Tailor what you share to the specific ask. Overloading executives with irrelevant details can overshadow the key points you want to communicate.

I’ve seen talented leaders derail executive reviews with irrelevant details, discarded options, and caveats. The detailed thinking that helped them arrive at a clear decision overwhelms a brief meeting, and executives leave more confused than confident.

Below are key pieces of information needed for each type of conversation:

Decision

• What are the options?

• What options are off the table?

• What options have been considered and rejected?

• What inputs are important to consider?

• Where are there tradeoffs or risks?

Resource

• What is the cost of the resource?

• What is the return on investing that resource?

• What is the opportunity cost?

• What is the tradeoff if resources aren’t allocated?

Action

• What challenges will the executive face when taking action?

• How urgent is the action?

• Why is the executive uniquely able to take the action?

Input

• What context do you have? -or- What context does the executive have?

• Where are there gaps in your understanding?

• What outcome are you trying to achieve?

Alignment

• How does the discussion impact the executive?

• What future implications might the discussion have?

• When will the topic need to be discussed again?

Provide space for discussion

Your job is not to be right or get what you want.

Your job is to lead a conversation that helps leadership make the right decision for your company, even if that may not be the decision that you want for your team. Regardless of the outcome, leaders will appreciate the thoughtfulness, collaboration, clarity, and openness you’ve brought to the conversation, and they’ll know that they can turn to you to move the needle on important areas for the business.

In making asks, I often see folks fail on either end of two extremes:

1. Over-dependence. Some product leaders want prescriptive direction from executives. They’re hesitant to take action without explicit alignment, so they ask for detailed direction on what to do. But executives don’t have bandwidth to provide great direction like this—they’re distracted by competing priorities and need you to drive ideas forward. These “high maintenance” leaders become low-leverage.

2. Over-independence. Other product leaders go the opposite direction, minimizing inputs from executives altogether and bringing them “solutions not problems.” But this makes you a “low-visibility” leader. Executives become disconnected from your work, and over time their confidence erodes—they wonder what’s really happening and whether you’re on track. When you turn them into “approvers” rather than “collaborators,” you miss their most valuable contribution: the judgment and pattern recognition they’ve built over years. The goal isn’t to avoid executive input—it’s to amplify it.

Match leadership’s intensity

Managing up with high-performance leaders isn’t about managing their expectations—it’s about matching their intensity and making them more effective.

Resources & support flow to people who are aligned with leadership’s priorities and have earned their confidence.

The best product leaders I’ve worked with don’t view managing up as a chore or political maneuvering. They see it as a core part of their job—giving leverage to the executives they work with, so those executives can give leverage back to them. That’s how you build the kind of trust that lets you operate with real autonomy, even on the highest-performing teams.

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Over the last couple of years, I’ve spent a lot of time with companies helping them navigate the AI shift—a time that is opportunity rich, but fraught with challenges for companies big and small.

and I built the Reforge AI Strategy program to help leaders win in the most intense environment we’ve seen in the history of tech. The course is available on demand and our second cohort starts on October 16th.

Join the AI Strategy course

We have a fantastic line-up of guests including:

• Justin Farris, VP of Product at Read.ai

• Laura Burkhauser, CEO at Descript

• Darius Contractor, Chief Growth Officer at Otter.ai

• Jeff Chow, Chief Product & Technology Offier at Miro

As a sneak peek, here is just one of many concepts we’ll cover in the AI Strategy program: how to build AI products competitors can’t match.

In the book Blue Ocean Strategy, the authors describe two types of markets:

• Blue Oceans are untapped markets where competition is limited and opportunities are rich.

• Red Oceans are crowded markets where established players fight intensely for market share.

In Red Ocean markets, companies have to beat the competition and take market share. As a result, companies often compete on price and margins suffer due to higher marketing costs and lower prices.

In Blue Ocean markets, competition is irrelevant or obsolete. Companies succeed by creating market share, benefiting from innovative products, healthy profits, and strong defensibility.

Over the last 50 years, tech has unfolded in three waves: the PC wave, the Internet wave, and the Mobile wave. Historically, the transition between waves (the “platform shifts”) have created blue ocean opportunities. New startups have moved quickly to capitalize on the new technology, while incumbents struggled to orient themselves around the new reality.

Why? Because the PC, Internet, and Mobile waves disrupted incumbents, undermining the advantages they had established. For example, Adobe needed to rethink and rebuild its design products for cloud-based collaboration, opening up a window of opportunity for Figma to build an Internet native product.

This disruption created fertile ground for new companies to emerge as winners. Each wave has seeded pillar companies, leading to an unprecedented rally of wealth creation:

• Personal Computing → Microsoft, Intel, Oracle, Adobe

• Internet → Google, Facebook, Salesforce, Figma

• Mobile → Apple, Uber, TikTok

This time is different. In many cases, AI is accelerating, rather than disrupting incumbents.

Let’s go back to our Adobe example. While Adobe wasn’t able to beat Figma to the cloud collaboration market, they have moved quickly to layer generative AI across their portfolio. This includes adding AI features into existing products, like Generative Fill and Generative Expand, and launching new standalone products like Adobe Firefly. Adobe is still far from parity with Figma’s collaboration features, but they have quickly leapfrogged on AI features—closing off AI opportunities for hopeful startups. (Check out Will AI create massive opportunities for startups? for a deeper dive into disruptive vs. accelerating technologies).

Today, AI products find themselves playing in a competitive, lightning fast environment. Competition is coming from every side:

• fast-moving AI platforms like OpenAI and Anthropic

• established tech companies like Adobe and Microsoft

• and a raft of well-funded startups

So one question needs to be at the top of every product leader’s mind:

How do we win in the most intense environment in the history of tech?

Let’s look at how you can use AI, including off-the-shelf AI models, to create an unfair advantage for your product and your company.

Many companies start by trying to reinvent the wheel. They assume AI advantage comes from advancing the state-of-the-art, and teams spend months or years developing custom models & infrastructure.

Certainly, that is a viable path—if you can lead the pack. AI labs, like OpenAI and Anthropic, have created massive value by innovating at the model layer,

But, this approach isn’t feasible for most companies. The cost of ML talent and training is prohibitive. And sometimes even that isn’t enough. Inflection, for example, had all the cards, but faced an uphill battle competing with the growing landscape of roughly equivalent models.

For most companies, its not feasible to advance the state-of-the art with AI… but its also not necessary. Over the last few years, a remarkable thing has happened. The most advanced AI capabilities are not being hoarded by the biggest tech companies. Instead, AI is now democratized—available via easy-to-use, affordable APIs and open source models.

You don’t need to reinvent the wheel, but you can’t get away with underinvesting. Some companies make the mistake of sprinkling basic AI features, like chatbots, onto their products.

While these features are easy to add, they rarely deliver meaningful value to users and can be quickly replicated by competitors.

Instead of being on one end of either of these extremes, there is a third approach: thinking of AI capabilities as building blocks that can be assembled in unique combinations with your product and its data… just as LEGO bricks can create endless possibilities.

The most effective AI features are built from three elements that, when combined, create competitive advantage:

1. AI Capabilities

2. Your Data

3. Your Functionality

AI products start, of course, with AI. Powerful AI models enable us to build products that would have seemed magical less than 2 years ago. Many of those models are just an API call away.

So if everyone has access to the same AI capabilities, then how do we create AI-powered products that stand out?

Your competitive advantage… your unfair advantage… comes from what is uniquely yours:

1. Your data

2. Your product’s functionality

3. Your understanding of unmet customer needs

Let’s look at these in more detail.

Your data provides context for AI models—it is the most important element to turn off-the-shelf AI capabilities into something bespoke for your product.

Next, we need to think about what your product does (its functionality). Your product’s functionality determines how AI behaves—it gives AI superpowers that can’t be had anywhere else.

Every AI feature is assembled from these three elements—the AI capabilities you choose, your data, and your functionality.

Next, we need to determine how the three elements connect to each other. These interactions are where true differentiation happens.

You data provides context to the AI—giving it the necessary information for AI to perform its task effectively. Context is critical to turning off-the-shelf AI models into custom solutions for your user’s needs.

For example, a medical diagnostic tool can feed patient history into an AI to help identify potential conditions.

AI generates output such as insights, content, or predictions. This output creates value in two ways. First, by providing results your customers want and second by becoming part of your proprietary data set.

For example, a content management system might help people use AI to tag and categorize articles, helping customers organize their content while enhancing your proprietary metadata.

Orchestration is the next piece of the puzzle. Your product determines when to use the AI—whether directed by the user or triggered automatically in the background. Importantly, AI does not replace traditional software. Instead, traditional software and AI complement each other, each excelling in different situations. Your product orchestrates the interactions between these different types of systems.

For example, an email client might offer AI writing suggestions when it detects the user pausing for more than 5 seconds.

Finally, let’s look at one of the most exciting developments. AI is getting increasingly good at using tools. AI can learn when to use your product, giving it unique superpowers.

For example, a customer support AI agent can be configured to use a payment system as one of its tools. Then, the agent can decide to automatically initiative a refund when it identifies eligible use cases.

We are at the very beginning of what’s possible with tool use, and this will be one of the most exciting areas as agents can increasingly autonomous. (Check out Tool Augmented Generation for a deeper dive.)

The power of this approach lies in creating combinations that competitors cannot easily replicate.

While they may have access to the same AI models, they won’t have your unique data and functionality—making your AI features distinctively valuable and difficult to match.

AI doesn’t just create new capabilities—AI magnifies the value of what your company already has.

Let’s look at an example…

Over the last 18 months, Miro has evolved into an AI-powered collaboration space that helps teams supercharge their teamwork. Underlying their strategy is a simple, powerful principle:

The canvas is the prompt.

This approach has led to a portfolio of AI features, with AI prototyping standing out as one of the most powerful. Teams can transform a pile of messy notes into a clickable prototype in moments—all without leaving the board.

Importantly, Miro is using off-the-shelf AI models to build features others can’t easily replicate. They do this by integrating their data and product functionality deeply into the AI experience. Here’s how the blocks connect:

• Context: The entire board’s content provides situational awareness to the AI

• Output: The AI generates a prototype based on the current work, which becomes part of the canvas

• Orchestration: Prototypes can be invoked when users need and the system asks questions as needed for additional context

• Tool Use: The AI uses Miro’s canvas as a tool, placing the prototype on the board. Another Miro feature, AI Sidekicks, adds comments from an “Agile Coach” or “Product Leader” just like any other user would.

Miro is using the same AI capabilities available to everyone, but to power a feature that only they can deliver.

Let’s look at how you can use this approach for your own products.

Every day, new AI capabilities emerge. Just this year, we’ve seen massive improvements in deep reasoning and video generation — things that seemed like science fiction a few years ago.

With all these AI capability, we have more ways to solve customer problems than every before. But, we need to be wary of solutions in search of problems.

Just because AI can do something, doesn’t mean it should.

Always start with the customer and work backwards to identify which AI capabilities can solve their unmet needs.

The key to powerful AI features lies at the intersection of what’s possible and what users want.

Descript has reimagined video editing: instead of working with cumbersome timelines, creators edit videos through AI-transcribed scripts. Although AI is core to how Descript works, they don’t emphasize the technology. Instead, they package advanced AI features around a very human need: helping creators look and sound their best.

Take their Eye Contact feature, which seamlessly edits videos so creators appear to maintain constant eye contact with the camera rather than looking at their notes. Another feature, Studio Sound, uses AI to enhance audio quality, delivering professional results regardless of where creators record.

The most exciting thing about being a product builder today is that we have an ever-growing menu of AI building blocks to choose from. We can translate, edit, manipulate, understand, and generate text. The same capabilities exist for images, video, and audio. We have increasingly powerful reasoning models that can plan tasks, use tools, and coordinate with other agents.

With this vast toolkit available, the key question becomes: which unmet customer needs can now be solved? This is where selecting AI capabilities should start—not with what’s shiny and new, but with identifying important customer problems that have shifted from impossible to achievable.

Once you’ve identified which AI capabilities can address real customer needs, you’re ready to determine how those capabilities integrate with your existing data and functionality.

As we’ve discussed, your data is crucial to building effective AI features—its provides the essential context that AI models need to deliver relevant, personalized results.

Without proper context, even the most sophisticated AI models produce generic outputs. Your proprietary data represents a key competitive advantage because it contains unique information that your competitors don’t have access to.

You can tap into various types of high-value data:

• Real-time data that provides context LLMs won’t have in their training datasets (flight prices, inventory availability, traffic conditions)

• User-specific data that is highly personalized and which competitors can’t access (listening or viewing patterns, healthcare records, purchase history)

• Domain-specific data that is high-value and hard-to-find (legal cases, medical research, editorial content)

• Human judgment data that augments existing sources with curated content (curated images, ratings & reviews, social proof)

• Reinforcement data that helps you improve results over time (thumbs up/down on responses, user corrections)

The most powerful AI features often emerge when you connect multiple types of data—like combining historical usage patterns with real-time user context to provide predictive assistance.

Your product’s functionality encompasses everything your product can do—both what’s visible to users and what happens behind the scenes.

The relationship between AI and your product’s functionality works both ways:

• Your product determines when to use AI to solve customer problems.

• But, increasingly, AI can use your product like a tool—tapping into your product’s functionality as part of generating output.

You can tap into various types of high-value functionality:

• Specialized workflows, such as contract review pipelines, financial approval processes, or medical prescription management

• Unique algorithms, such as recommendation or personalization systems

• Business rules that encode decision criteria or implement complex requirements (insurance underwriting rules, loan approval criteria)

• Integrations into multiple systems, such as integrating with a healthcare records system or payment system

Let’s look at another example…

Granola is one of the fastest growing AI apps. The just raised another $43 million to accelerate their vision of AI-powered note-taking.

But, AI note-taking is a Red Ocean, filled with competitors like Otter, Fireflies, Fathom, and integrated AI note-taking in Zoom, Teams, Notion, Google Meet, and ChatGPT.

How did Granola break through?

They identified an unmet customer problem.

Most of the existing note taking products take over the note-taking process… they act as a secretary that people can delegate to.

However, for an important segment of users, note taking is a critical part of their job. They don’t want someone else to take their notes… they want to take better notes.

So, Granola created a platform for them… for the person that wants AI to amplify rather than replace.

Note: This spectrum from autonomy (I want AI to do the job) to amplification (I want AI to help me do the job better) is an important framework for understanding product positioning. In nearly every category, we have AI products at varying points along the spectrum. For example, AI coding tools like Lovable are for people who want the AI to write the software for them, while tools like Claude Code and Cursor are for engineers who want AI to amplify their skills.

Granola is one of the fastest-growing AI companies, yet they rely entirely on off-the-shelf AI systems.

For transcription, they use Deepgram. For LLM features—note generation, the ability to chat with notes, and generating artifacts like follow-up emails—they rely on Anthropic and OpenAI.

Despite relying on off-the-shelf AI systems, Granola has assembled these capabilities into something uniquely powerful. Here’s how:

• Context - Their context includes both the notes users take and the transcriptions they generate

• Output - They generate enhanced notes that become part of an ever-expanding repository of knowledge—a second brain

• Orchestration - Granola is implemented as a Mac app, not a web app, giving it the ability to detect when meetings start, access system-level audio, and automatically trigger transcription

• Tool use - Calendar integrations provide metadata about meetings, including attendees and topics

They have assembled a unique, differentiated AI product—built not by developing proprietary AI models, but by understanding an unmet customer need and crafting a solution that elegantly addresses it.

I’ve seen a lot of the history of tech first-hand. This is the most intense time I’ve ever seen—ripe with opportunity, but fraught with challenges.

Winning the AI era is hard, but not impossible. It won’t require hard-to-find ML researchers and hundreds of millions of dollars to train frontier models. It will require a deep understanding of customer problems, emerging AI solutions to those problems, and the unique role your product can play.

To get started, ask yourself four questions:

1. What unmet customer problems need to be solved?

2. What AI capabilities can solve those problems in novel ways?

3. What proprietary data can power those solutions?

4. What superpowers can our product give to AI?

Winning with AI isn’t just about the technology—it’s about turning what’s uniquely yours into a competitive moat.

This is one of many powerful concepts we cover in the AI Strategy course, designed to give you the ideas, frameworks, and tools to navigate today’s unprecedented opportunity. We built the Reforge AI Strategy program to help leaders win in a rapidly evolving landscape. The course is available on-demand and our second cohort starts on October 16th.

Join the AI Stategy course

Product teams often treat specifications like paperwork—a necessary evil before the "real work" of shipping. Engineers get celebrated for elegant code, designers for beautiful UX. PMs get kudos for delivering impact. But specs? They’re typically rushed through, tossed aside, forgotten.

This was always a mistake. In my Product Competency Toolkit, Feature Specification sits at the top—first among twelve competencies—for a reason. It underpins Product Execution, along with Product Delivery and Product Quality.

Flawless execution is the foundation of good product management, and a great spec is the starting line.

Today, the way we build is shifting. Engineers are getting faster—much faster. AI can turn rough ideas into working code in minutes. The bottleneck is no longer building. It's knowing what to build, and aligning the team around those requirements.

Suddenly, the humble specification isn't ephemeral paperwork. It’s the foundation of product management—it's becoming the source code itself.

Why PMs are suddenly the bottleneck

In his recent talk Building Faster with AI, Andrew Ng noted an unprecedented trend:

"For the first time in my life that I saw managers proposed to me having twice as many PMs as engineers. I still don't know if this proposal is a good idea, but I think it's a sign of where the world is going." —Andrew Ng

This validates what we predicted in our previous post: as engineers deliver many times faster with AI, companies need more PMs to support those productive engineers, not fewer.

As product delivery accelerates, that puts intense pressure on every other aspect of product management—understanding customer needs, crafting the right features, validating impact.

And all of that pressure gets focused into one artifact—the spec.

Specs—the new source code

In traditional software development, programmers write human readable “source code” which gets compiled into highly optimized, machine readable “object code”. The “object code” (also known as a binary) is a byproduct that can be recreated with the source code. The source code is literally the source of truth.

Sean Grove, from OpenAI, has a provocative thesis. In his recent talk, The New Code, he argues that a well-written prompt (i.e., the spec) is the new source code.

Seen in that light, we’re doing AI development backwards. We craft careful prompts to communicate our intentions to models. The AI generates code. Then we keep the code and throw away the prompt.

"This feels like you shred the source and then you very carefully version control the binary," Grove observes.

Think about that. In traditional programming, source code is sacred. The source contains comments, structure, and documentation—everything needed to understand and modify the system. The binary is just a downstream artifact.

But with AI, we've flipped this relationship. We treat the generated code as the artifact worth keeping and the specification—the prompt—as disposable.

Grove argues this gets it exactly backwards. Code, even elegant code, is what he calls a "lossy projection" from the specification. Just like decompiling a binary won't give you the original comments and variable names, reading code won't tell you the full intent behind it.

The specification, however, contains everything. A sufficiently robust spec can generate "good TypeScript, good Rust, servers, clients, documentation, tutorials, blog posts, and even podcasts."

More importantly, specifications do something code cannot: they align both humans and machines on shared goals. Grove puts it simply: "A written specification effectively aligns humans and is the artifact that you use to communicate and discuss and debate and refer to and synchronize on."

He predicts: "In the near future, the person who communicates most effectively is the most valuable programmer. And literally, if you can communicate effectively, you can program."

The new scarce skill isn't coding. It's writing specifications that fully capture intent and values.

For product managers, this should sound familiar. It's what we've always done—just now the machines are listening too.

Wait, didn’t prototypes kill the spec?

Until recently, the product lifecycle had to start with the spec (or PRD or concept doc or napkin) that gets the initial wireframing, designing, prototyping, and MVP development under way.

The traditional approach felt like a necessary evil. Engineers built basic MVPs to get something—anything—into customers' hands. "If you're not embarrassed by the first version of your product, you've launched too late" became gospel.

Our entire approach to building products has recently undergone a significant shift, and in this new world, the spec is often the output, not the input.

Today, you don't need to ship a single line of code to get a prototype into customer hands. Tools like v0, Lovable, and Replit let you build functioning prototypes in hours, not weeks. No engineering required.

This isn't just faster—it's fundamentally different. Armed with a "vibe coded" prototype, you can gather real customer feedback before writing a single line of your feature spec. You can test assumptions, iterate on flows, refine interactions.

• The old workflow looked like this: vague idea → wireframes → designs → engineer-built MVP → customer feedback → painful spec revision → wireframes → designs → rebuild → pray.

• The new workflow: vague idea → rapid prototype → customer feedback → crystal-clear spec → AI-assisted implementation.

Prototypes haven't killed the spec. They're making specs better.

Spec-driven development in action

Let's look at a hands-on example of how this all plays out. is the founder of decimals, a platform (in stealth) that enables experts in the creator economy to place talent from their network into jobs.

• Are you an AI-first PM looking for a new opportunity? Drop in your details and we’ll let you know when we come across interesting opportunities.

• Are you a company looking to hire an AI-first PM? Apply to our talent network and we’ll share a curated shortlist of candidates.

Danny is going to walk us through their spec-driven development process—a process that has had two impacts:

1. massively improved communication with engineering for the bigger features, and

2. enabled Danny, despite having no prior coding experience, to go all the way from detailed spec to live feature for the smaller stuff.

Over to Danny…

Here’s a recent example I worked on. For context, we’re building a product that enables expert influencers to place candidates from their networks into jobs. After shipping a new landing page, we needed to give our experts a quick way of accessing the link to their own page.

We need a button in the header that directs to the company-apply page. I'm focused on the emails at the moment. This is vibe code-able if you can pick it up please.

This was from my co-founder, referring to a simple button that needed to be shipped. Easy enough, and precisely the kind of thing that a non technical person can now ship themselves.

Here’s the setup and steps for how this got shipped within a couple of minutes:

• Project management tool: Linear

• IDE: VS Code

• Extension: GitHub Copilot Pro

• Model: Claude Sonnet 4

• MCP Server: Linear MCP server to allow Linear tickets to be accessed via Copilot

Let’s take a look at the steps in the process (follow along in the Loom video above):

1. Generate a Linear ticket out of the Slack message from my co-founder (00:14)

2. Clarify what I want the new copy to be in the ticket above (00:25)

3. Open up Copilot and prompt Claude to open up the Linear ticket (01:18)

4. Prompt Claude to review the ticket and analyse it relative to the codebase (01:52)

5. Prompt Claude to create a branch and implement these changes (02:30)

6. Test the changes to ensure they’re working as expected. (02:52)

7. Open a pull request (PR) on GitHub to ship these changes into the codebase (03:55)

8. Wait for an engineer to review/approve the PR

Zooming out, the true power of this setup comes into focus. Yes, this is a trivial example, but the point remains: a non-technical person can now go between Linear tickets, a codebase, and an engineer, all with a couple of prompts to Claude via GitHub Copilot.

And again, the critical part of all of this isn’t the code itself: it’s the spec.

Now, it’s important to caveat the above with what’s needed for it to work well:

1. Being specific: Using vague specs will only result in a messy codebase. Using Claude to take an initial draft of a ticket, review the codebase, and help make it more specific is an essential part of the process. In our case, we also have guidelines for writing a good spec.

2. Being selective: Using the above for simpler tasks is ideal. The more complex the ticket, the greater the need for someone who knows what they’re doing to get involved (“self-serve” ends up being anything but, in these cases).

3. Gatekeeping: This approach only works well because there’s an actual engineer who knows what they’re doing, reviewing changes and ensuring the balance between simplicity and functionality is maintained.

But let’s be clear: the rules have changed. Specs are the source of truth for everyone building the product, including the LLM.

With the proper setup, it is entirely realistic to expect to be contributing to a codebase as a non-technical person. With enough time and patience, you can start to understand the codebase and implement changes yourself, rather than simply relying on Claude to bail you out every time.

One day, you might even expect an AI agent to ship your ticket whilst you sip your coffee.

If you’re a PM and worried about what AI is going to do to your job, the key is to realize that the job itself is changing. But that’s also the case for everyone else. The great thing is that the core skills required for an excellent PM are even more valuable in this new world.

As we predicted in a previous post, the companies that are working in this way will need more, not fewer PMs. And it turns out that prediction is working out rather well:

Long live the spec!

William Gibson, the science fiction writer who coined the phrase “cyberspace,” once said:

The future is already here—it’s just not very evenly distributed.

I’m reminded of this when I think about what is and is not possible with AI. Today, AI's gains are profoundly uneven. Some things—generating code, text, images—have made quantum leaps. They operate at AI speed. Other things—talking to customers, discovering their needs, convincing them to buy—still move at human speed.

This uneven distribution is reshaping product teams. The spotlight is shifting from implementation work to understanding work. The core skills that have always set apart the best PMs—understanding user needs, defining problems clearly, designing elegant solutions—have become exponentially more valuable.

The best PMs turn those insights into specs—specifications that align teams, guide implementation, and serve as the lasting artifact in an increasingly automated development world.

Others remain insulated, at least for now. Airbnb’s CEO, Brian Chesky, says weaving AI into the product will take years. Clearly, they aren’t facing imminent threat from the Gen AI boom.

So, why are some companies vulnerable while others stand on solid ground?

That's exactly what we built the AI Disruption Risk Assessment to answer. In our latest Reforge post, and I break down this framework from our AI Strategy course.

We ran the first cohort in April, and participants loved this assessment. It cut through the noise and showed them exactly where their company stood—and where they needed to go. They wanted to bring it back to their teams.

Consider this post an amuse-bouche—a little taster to get you started. Check out the full Reforge post for all the deets.

Read the full Reforge post

Customers are “divinely discontent”

Historically, the bar for Product Market Fit raised gradually over time. Jeff Bezos once wrote in a shareholder letter:

That was in 1998.

In the Reforge Product Strategy program, Casey Winters and Fareed Mosavat describe this as the Product Market Fit Treadmill. During a technology shift, the PMF threshold (the red line) accelerates:

In this past, this acceleration happened over time, primarily because of “technology diffusion”. In other words, it took time for people to come online or get mobile phones. The diffusion of AI technology is much more rapid. It took ChatGPT just 5 days to reach 1 million users.

Customer expectations aren’t rising at a predictable, linear pace over longer periods of time—they are spiking nearly instantly. This creates Product Market Fit Collapse, a phenomenon unprecedented in the history of tech.

How to tell if your product is at risk

The AI Disruption Risk Assessment consists of 18 distinct factors in 4 risk areas:

• Use Case - How will AI impact how users engage with your product?

• Growth Model - How will AI impact your product’s growth model?

• Defensibility - How will AI impact your product’s defensibility?

• Business Model - How will AI impact how your product monetizes?

No single factor dictates whether your product is or is not at risk. Instead, you’ll evaluate how your product stacks up in each of these factors.

To help with your evaluation you can use the AI Disruption Risk Tool, an online tool that was “vibe coded” using v0.dev with a single prompt.

Use the AI Disruption Risk Tool

Sidebar: I was astonished that v0 was able to build the tool with a single prompt. I uploaded the right lesson from AI Strategy and asked:

This document describes the AI Risk Assessment Framework, a tool that product managers can use to assess how at risk their product or company is of disruption by AI. Based on this document, generate a single page for segmented into four sections: Use Case Factors, Growth Model Factors, Defensibility Factors, and Business Model Factors. In each section, include a row for each factor that includes the Factor and the Description. On that same row, include 7 radio buttons that let a user select from Low Risk to High Risk. Include the Low Risk and High Risk descriptions under labels on the left (Low Risk) and right (High Risk) side of those buttons.

All buttons should be set to the middle value 4.

Include a AI Vulnerability Score that updates as the user makes their radio button selections. Sum the score on each row (from 1 to 7) to calculate the AI Vulnerability Score. Indicate the numeric score and a classification of Low Vulnerability, Moderate Vulnerability, or High Vulnerability.

Done in about 10 minutes — a fully functional tool built in less time than it would have taken to make a Google Sheet.

Evaluating Use Case risk

Customer expectations shift during technology transitions, and understanding how these changes impact your product requires looking at the fundamentals of how and why people use what you've built.

The difference between vulnerable and resilient isn't random. It comes down to eight key factors that determine whether your product can withstand AI disruption or becomes another casualty as incumbent products subsume more and more use cases.

• Primary Workspace vs. Adjacent Tool — Core work happens in primary workspaces; adjacent tools get replaced first

• Outlier Output vs. Commodity Output — Exceptional quality creates defense; "good enough" can be replaced by AI

• Human Judgement vs. Pattern Recognition — Nuanced decisions resist AI; predictable patterns don't

• Hard to Automate vs. Easy to Automate — Creative, contextual work survives; structured tasks get automated

• Conservative vs. Tech-Forward Customers — Early adopters switch fast; conservative users provide buffer time

• Human Relationships Matter vs. Irrelevant — Personal connections create moats; outcome-focused customers readily switch to AI

• Varied vs. Consistent Output — AI excels when variation adds value; strict consistency requirements expose AI's weakness

• Frequent vs. Infrequent Use — Habits require 10x improvements to break; forgotten products are vulnerable to new, AI-native players

Evaluating Growth Model risk

Some companies are watching their distribution channels crumble. Search gets disrupted, growth loops break down, and AI strips away the incentives that once drove user participation. Others are hitting the accelerator. AI supercharges their sharing, collaboration, direct customer relationships, and sales motions.

The difference comes down to how dependent your growth model is on systems that AI is actively dismantling versus strengthening. Products built on search traffic and user-generated content face headwinds, while those with direct relationships and human-centered growth loops often find tailwinds.

• Stable vs. Disrupted Distribution Channels — AI is reshaping search, social, and content discovery; SEO-dependent businesses face the biggest risk

• Intact vs. Weakened Growth Loop — UGC platforms lose contributors when AI provides instant answers; sharing and collaboration loops stay strong

• Direct vs. Mediated Customer Relationships — Direct relationships build resilience; third-party dependencies create vulnerability when those channels shift

Evaluating Defensibility risk

AI is rewriting the playbook. Moats that seemed impenetrable are evaporating overnight. Years of carefully curated user content? AI swallowed it whole during training. Search traffic? Users are bypassing Google entirely. Content advantages? Gone the moment AI can match your output in seconds. The winners aren't clinging to obsolete defenses—they're building new ones around data AI can't touch, emotional engagement it can't fake, and switching costs it can't dissolve.

• Proprietary vs. Public Data — AI commoditizes publicly available information; only unique data creates lasting advantage

• Data-Driven vs. Content-Driven Value — Personalized experiences built on unique data stay protected; generic content gets crushed

• Emotional Engagement vs. Functional Utility — AI easily replaces functional tasks; emotional experiences are harder to replicate

• Strong vs. Weak Network Effects — Some network effects (like those based on human interaction) remain durable, while others (like UGC loops) are rendered obsolete

• High vs. Low Switching Costs — Users stick with products when switching is challenging or costly, and they’ll try out new AI entrants if friction is low

Evaluating Business Model risk

Finally, AI isn't just changing how products work—it's rewriting how they make (and spend!) money. Per-seat pricing suddenly makes no sense when one person can do the work of five. On top of that, compute costs pile up with every power user. You're selling fewer seats while spending more per customer.

The winners? Companies with fat margins who can absorb the hit and keep experimenting. The losers get caught in a vice: rising AI expenses on one side, customers comparing their enterprise tool to $20 ChatGPT on the other. No wonder smart companies are ditching seat-based models entirely.

• Value-Based vs. Per-Seat Pricing — Value-based pricing captures AI productivity gains; per-seat models lose revenue as AI reduces headcount needs

• Strong vs. Weak Unit Economics — Healthy margins absorb AI compute costs and enable experimentation; thin margins handcuff innovation

Putting it all together

After evaluating all 18 factors, add up your scores to calculate your overall vulnerability:

• 18-36: Low Vulnerability – Strong position with significant AI moats

• 37-72: Moderate Vulnerability – Some challenges but defendable

• 73-126: High Vulnerability – Significant risk requiring urgent action

Your total score is just the starting point. Focus on any factor where you scored 5 or higher—these are your most pressing vulnerabilities and should drive your AI strategy.

Stack Overflow: A case of Product Market Fit Collapse

Software developers routinely run into roadblocks—those hard-to-fix bugs that grind progress to a halt. For years, that meant turning to Stack Overflow for quick, reliable answers. Then, in late 2021, GitHub Copilot and ChatGPT appeared, offering faster, more personalized guidance. Almost overnight, the steady river of visits began to recede. The problem developers face hasn't changed, but the solution shifted—from Stack Overflow's Q&A forum to AI assistants that live right in their code editor.

Stack Overflow's assessment reveals why this disruption hit so hard. They scored high-risk across critical factors: an adjacent tool serving commodity content to tech-forward users who jump ship fast. Their growth model relied on search traffic and user-generated content—both now disrupted by AI. Most damaging, their value proposition depended on public data already in AI training sets. When developers could get instant answers without leaving their workspace, Stack Overflow's loop broke. Companies with this many vulnerabilities can't wait and see—they need to move before their window closes.

Airbnb: Taking their suite time

Airbnb tells a different story entirely. While Stack Overflow immediately felt the heat from AI, Airbnb's CEO Brian Chesky calmly announced that weaving AI into their product would take years. That's not denial—it's confidence born from a fundamentally different risk profile.

Airbnb's assessment reveals why they can afford this measured approach. They score low-risk across most critical factors: a primary workspace where relationships matter deeply, serving customers who value human connections. Their growth model relies on direct relationships with hosts and travelers rather than search traffic or content loops that AI can easily disrupt.

Most importantly, their core value comes from real-world experiences and human trust—things AI can enhance but never replace. Companies in Airbnb's position have the luxury of strategic patience, using AI to improve their platform rather than racing to avoid obsolescence.

Where do you stand?

We're at the beginning of the AI technology shift. As AI accelerates, customer expectations will rise to match. The rules have changed, and most leaders aren't ready. The AI Disruption Risk Assessment is your first step—showing exactly where your product stands today so you can build the strategy you need for tomorrow.

In the AI era, standing still means falling behind.

If you want to dive deeper, Reforge's AI Strategy program is designed for this moment. It gives you the tools, frameworks, and strategic insight to not just survive the AI shift—but to lead through it.

Recently, I was walking through some designs with Finn. He had incorporated stunning photography that fit perfectly with product's design. I asked him where he had sourced it from, fully expecting to learn about some elite stock photography site.

But, no. Finn had generated the images in Midjourney. These were the most evocative, natural, and useable AI-generated images I've ever seen. Finn generously agreed to share his secrets with me... and with you.

Welcome to the best guide on image generation I've found (works great for video too!).

Getting consistent, high-quality output from AI image generators can feel a bit like luck. Alright, a lot like luck. You can get much better results by better understanding how the AI interprets your words and structuring your prompts strategically. Here are my thoughts and techniques on how I get great imagery that I use daily in my design work.

A note: I use Midjourney as I find it the best one for image generating, but there are lots of others and these thoughts might apply to them too. Take ‘em all with a pinch of salt as I’m in no way an AI expert or image generator specialist. When it comes down to it, I'm just a guy, sitting in front of a screen… waiting for fuzzy images to load.

First thing is, let’s get the more technical bit out the way.

How Midjourney reads your prompts

Midjourney uses a diffusion model, a type of machine learning system trained on millions of images paired with text descriptions. Over time the model learns to understand the relationship between words and visual concepts. It creates images through a denoising process—starting with static and progressively adding detail until it forms a complete image that it thinks matches your prompt.

The AI breaks down your prompt into tokens—individual words or phrases—and weighs each element based on its training data. Words at the beginning of your prompt typically receive more attention, and the system looks for artistic concepts, styles, and subjects it recognizes from its vast dataset.

Midjourney also considers relationships between words. When you write "modern glass office," it understands this as a cohesive architectural concept rather than just "modern" plus "glass" plus "office" separately. This contextual understanding is what makes strategic word placement so important.

Okay, I’m oversimplifying things, but that’s the gist of it—now let’s look at actual prompts.

Breaking down an effective prompt

Let's examine a well-structured prompt to see how each element contributes to the final result:

"An empty stylish office chair behind a trestle desk in the early morning light in a modern glass Italian architect’s office in Milan, FujiColor C200"

Here's how Midjourney would process each component:

• "An empty stylish office chair"—Establishes the main subject with specific descriptors that guide the aesthetic

• "behind a trestle desk"—Adds compositional context and furniture style details

• "in the early morning light"—Defines the lighting quality and time of day

• "in a modern glass"—Sets the architectural environment and style

• "Italian architects office in Milan"—Reinforces the design aesthetic and cultural influence

• "FujiColor C200"—Applies the film stock's warm, saturated color palette

Each word builds upon the previous ones, creating layers of visual information that guide the AI toward a specific aesthetic vision.

The Foundation: Subject, Setting and Style

Every effective image generation prompt should include three core elements:

Subject

What is the main focus of your image? Be specific rather than generic. In our example, "stylish office chair" has more direction than simply "chair". The word "stylish" immediately suggests clean lines and contemporary design. Of course style is an opinion but you see what I mean.

The kind of chair with some questionable stains and broken levers

The kind of chair colleagues pinch from you the moment you’re off for lunch

Setting

Where does the scene take place? Our example combines spatial relationships ( "behind a trestle desk" ), lighting conditions ( "early morning light"), and architectural context ( "modern glass office" ).

Style

How should the image look and feel? This includes both visual style. "Italian architecture” suggests sophistication and design excellence and technical approach by using the film stock reference "FujiColor C200”.

Word order and emphasis

Midjourney pays more attention to words at the beginning of your prompt. Notice how our example prompt prioritizes the main subject first: "An empty stylish office chair" leads the description, ensuring this remains the focal point even as other elements are added.

Effective structure: Subject → Key details → Setting → Lighting → Style references

Our example follows this pattern:

1. Subject: "empty stylish office chair"

2. Key details: "behind a trestle desk"

3. Setting: "modern glass office"

4. Lighting: "early morning light"

5. Style references: "Italian architect’s office in Milan, FujiColor C200"

Lighting is what creates mood

The phrase "early morning light" in our example does a lot of the heavy lifting for our image. It suggests:

• Soft, directional lighting

• Warm color temperature

• Long shadows

• Peaceful, quiet atmosphere

Using time of day helps to get the mood better than trying to be overly specific about exactly what type of lighting you want. Natural phrases seem to work well, like “early evening”, “middle of the day”, and “late at night”.

Seasonal lighting

You can also add seasons to help create the right feeling. For example changing this to something like an “autumn rainy morning light” to create that kinda dark and more gloomy lighting you get in the fall. Using this creates much better consistency than trying to describe the lighting itself. Notice how in these examples, adding a season invokes autumnal leaves and weather. Something you would otherwise have to specifically add.

Nice, feel that sun…

Ok, where’s my brolly?

Style references and cultural cues

Adding "Italian" to our architectural prompt tells Midjourney a lot of hidden meanings to incorporate:

• Clean, sophisticated design principles

• High-quality materials and finishes

• Attention to proportions and details

• Modern European aesthetic sensibilities

Cultural and stylistic references work because they tap into Midjourney's training on countless images associated with these concepts.

Buongiorno Milano

Good Morning New York

The film stock secret

One of the most reliable techniques in Midjourney prompting is using film stock names. This approach provides instant, consistent aesthetic control without needing to describe complex technical details. Don’t worry if you don’t know any film stocks. There are some good’uns in here.

In our example, "FujiColor C200" tells Midjourney to apply:

• Warm, slightly saturated colors

• Film grain texture

• Natural skin tones (if people were present)

• Nostalgic color grading

• Soft contrast typical of consumer film

Popular film stocks and their effects:

• Kodak Portra: Warm, natural skin tones with beautiful color rendition

• Kodak Ektar: Ultra-saturated, vibrant colors

• Kodak Gold: Warm, nostalgic vintage tones

• Fujifilm Velvia: Punchy, saturated colors perfect for landscapes

• Fujifilm Pro 400H: Soft, dreamy pastels

• FujiColor C200: Balanced, warm consumer film aesthetic

• Cinestill 800T: Dreamy nighttime glow with distinctive light halos

• Kodak Tri-X: Classic black and white with strong contrast

Adding film stock references is far more precise than using vague terms like "cinematic" or "vintage". Try it out on any prompt and you’ll see what I mean.

Things to stop doing

Drop the LLM-generated prompts

Be careful when you generate prompts from LLMs as they often choose quite "cheesy" and overused descriptions. Things like "British leather chair in a country manor library." These clichéd combinations can make your images feel generic, predictable and just ‘meh’.

Good prompt structure Claude, well done but I would say this is…. Twee.

Avoid generic personality descriptors

Try not to use prompts like "A confident CEO in a tailored blazer" as these often lead to overused body language cues which look forced and inauthentic. In this example, "confident" apparently means crossing arms, head tilted upwards, looking down your nose—that sort of thing. A bit basic and overdone.

Beware of bias

Representation: The other issue is bias & representation—a tricky subject to discuss—but one very much present in AI. Considering gender, race, cultural heritage, age and ability in your prompts will give you much better representation in the output. Sadly it seems AI has inherent human biases.

For example, images generated using "a confident CEO" come back waaaay too often as a white man. Sigh. Instead, maybe look to be more specific with your prompt. Why not use "a woman CEO in a tailored green suit, standing in her company's office" This gives you control over representation while avoiding generic stereotypes.

Body image: Another bias to be aware of when generating images of people is body image and weight—again another tricky topic—but images skew towards slim. So using appropriate natural language like “A portrait of a stocky man in white shirt” will get more realistic looking people. It seems that strangely more on the nose terms wield better results.

People seem slim by default

More realistic representation

What about those modifiers?

The "--no" modifier limitation: This is like painting vs sculpting. A painter starts from a blank canvas and adds to create. A sculptor typically starts from raw material and removes to create. I have found AI image generation to be predominantly better at additive generation. When asking to remove bits or ignore things, you end up with weird artifacts and glitches and too many fingers. Sometimes this effect can be interesting and usable, but it's not easy to replicate.

What about “--stylize” This is the setting of how wild Midjourney should go. However when using film stock in prompts they seem to override the stylize settings. so if you're aiming for consistency then I would leave stylize alone and use film stock to get the feeling instead.

Overcomplicating & contradictory elements

Our example prompt is detailed but focused. Avoid rambling descriptions that dilute the core message. And don't mix incompatible styles like "modern glass office" with "rustic wooden cabin" unless that contrast is intentional.

Generic descriptions & spatial relationships

"Nice office" tells Midjourney nothing useful. "Modern Italian architectural glass office" is better. Also notice how our example establishes spatial relationships—the chair is "behind" the desk, not just somewhere in the room.

Things to start doing

Film stock strategy

Look around for film stocks you like and use the ones that work best for the context. Mixing and matching film references can work but often results in darker/moodier results. You can find lots on what film stock looks like and the vibe it gives out there on t’internet. But here’s a good article on Kodak vs Fujifilm to get you started.

Start conceptual, then refine

If you're struggling to know what to create, one way is to try a bland and unstyled prompt to see how it's interpreted, then use those as a starting point to refine and style it up.

A working example of this would be creating an image for an article or blog post. Let's say the post was about "Young people experience career burnout earlier than others." You could try that as a prompt but it will give you illustrations of heads on fire or smoking desks. The key is adding a film style, it narrows it down and although you'll likely get back some pretty bland options like people asleep at their desks—these can then be developed into more refined prompts.

Ugh that’s a no to this one, right!?

Okay, better. But are they in the loo?

Alright, there we go. The vibe is ‘I’m the last in the office’ and the tone feels right & relevant but also kinda sad.

Natural vs. generic language

Use natural terms to get the mood right vs. generic or overly descriptive language. "Early morning" has a lot more metadata than "nice soft lighting".

Get inspired for better results

Yes, you can think of prompts off the top of your head. But finding images, photographers & visual references you like can really help refine the result. For example, "a portrait of a woman in her office" could be quite soulless or sterile. But with details like "books on desk corner" or "reflections of a street in Marseille” there could be too much noise in the image. So see how great photographers frame their images. What's the focus? The setting? How busy is the image? Basically channel your inner Laura Pannack.

Honing your eye

The last thing is a little more personal. And that is that it comes down to you and your taste. These tips can help create consistent, good quality generated images, but it's your taste that will decide which ones get seen or not. The good news is you might already have this, and it's developing all the time—the bad news is there is no shortcut to getting it dialed in.

A first step in your (Mid)journey

So if you made it here, well done. There is no prize I’m afraid but I’ll just leave you with this… Midjourney (and other image generation models) responds best to clear, meta-data rich direction. If you can communicate your vision using this structured approach, you’ll get better, more consistent results. Master this framework (aka copy & paste it) and you'll find yourself creating great images that you’ll actually want to use.

It’s Ravi again with a couple of final notes. First, there is actually a prize! Finn has put together an image prompt gallery and included more examples at the end of this post:

🎁 Finn's Image Prompt Gallery

I got to thinking… why does Finn’s approach work so well?

Reading through these prompts brought me back to my childhood. You see, my Mom was a photo researcher—she was responsible for finding images for text books and other publications. She’d bring me into the office some weekends and I’d help pour over light tables full of slides, each carefully marked with a short description and keywords—essential metadata to find images in a time before the Internet. Yes, I’m dating myself.

A good prompt looks a lot like those descriptions written carefully by professional photographers who wanted their work to be properly cataloged. Other common elements include:

• Film stock: Fujifilm Velvia, Kodak Portra, etc.

• Camera brand: Leica, Hasselblad, etc.

• Aperture: f1.2 (for blurry background), f22 (for sharp background)

• Focal length: 18mm (for wide angle), 85mm (for portraits), 200mm (for telephoto)

• Lens type: Fisheye, Macro, Tilt-Shift

• Shutter speed: 1/8 (for motion blur), 1/500 (for freeze frame)

• Exposure: Overexposed 1 stop, Underexposed 1/3 stop

• Flash: On or off

It turns out, these elements work great for prompts. Here’s an example of two portraits, one generated without any camera metadata and the other generated with camera metadata:

The photo on the left looks bland and synthetic, while the photo on the right looks more authentic, artistic, and natural. The metadata provides specific information, but it also serves a higher-level purpose—it guides the model to use the more professional photos in its training data set and generates better output as result.

AI images have gotten remarkably good, but they often look a little off for production use. These techniques, and many more than you’ll discover, can take AI-generated images to the next level.

Have fun experimenting!

AI promises to multiply not just a PM's individual output, but the outcomes their teams can deliver. But let's be clear-eyed about what this means: fundamental changes are coming to our field, and they're coming fast. The product managers and leaders who proactively embrace and prepare for this AI-powered future will gain a significant lead over those who wait and see.

Many product teams are already using generative AI, primarily chat-based LLMs, in their daily work. However, these tools often hit a ceiling. They're limited by the constrained amount of data they can access and typically operate in a separate environment from the core systems where we manage our day-to-day product work.

We’re transitioning towards the next wave in AI which will be deeply integrated into these "single source of truth" systems. This means more than just conversational interfaces for answers and actions. Imagine AI armed with intimate knowledge of your business goals and strategy, all your product ideas and opportunities under consideration, the makeup and capacity of your product teams, and the needs voiced by your prospects and customers — no elaborate prompt required.

Powering this evolution will be AI agents. While agentic AI is relatively new in market-ready products, its potential is so transformative that it warrants close attention.

Think of agents less as tools and more as infinitely scalable digital teammates. AI agents are:

• Proactive: They anticipate needs rather than just waiting for prompts, continuously monitoring and notifying of important changes, and suggesting relevant best practices.

• Autonomous: Agents can manage tasks over hours, days, or even weeks, orchestrating work by coordinating with other agents and services.

• Goal-oriented: Instead of just executing instructions, they can define their own tasks in service of a stated goal, detect failures, retry, or escalate to a human if needed.

• Context-aware with persistent memory: They draw from vast amounts of data, possess a longer working memory, and learn user preferences and work styles over time.

• Able to take action: Agents can manage workflows across multiple systems, use tools (pull data, perform computations, create visualizations), and communicate proactively (send emails, notify users).

So, how will this AI evolution, from current generative tools to future agents, impact each phase of the product development lifecycle?

Phase 1: Discovery — distilling insights & shaping strategy

1. Distilling insights from customers, colleagues, and the market. Product teams are drowning in inputs: feature requests, customer feedback, competitive landscape and company strategy. But how much of this treasure trove actually informs quarterly prioritization, annual planning, or critical trade-off decisions? How many valuable insights walk out the door when a teammate leaves? Organizations that can effectively leverage these inputs gain a substantial edge.

• Today: Solutions like Productboard Pulse are already using AI to distill trending themes from large volumes of feedback. AI can help probe these themes, surface customer context, and link insights to feature ideas. You can even ask questions about the feedback in the system.

• Looking forward: Imagine proactively monitoring the needs of your top customers, surfacing new trends directly to PMs. Agentic AI will be able to continuously scan the market, customer interactions, and internal data streams, delivering synthesized intelligence that was previously impossible to gather.

2. Product strategy. Product strategy will remain the domain of product leaders, driven by their market context, product sense, and judgment. However, a huge part of strategy is synthesizing and weighing inputs – something AI excels at.

• Today: Product leaders are constrained by the amount of context they can feed into the LLM. Some are experimenting with developing their own RAG-based systems to funnel larger amounts of internal data into the LLM as context, but it is a substantial undertaking to develop these systems and introduces new challenges related to data freshness and governance.

• Looking forward: AI will increasingly act as a thought partner, harnessing inputs related to your product, business, customers, and market. It can help formulate various strategic scenarios, stress-test assumptions, and identify potential blind spots, allowing leaders to focus on the ultimate strategic choices.

3. Product ideation and prioritization. With a clear strategy in place, the focus shifts to generating innovative ideas and making tough prioritization calls to bring that strategy to life.

• Today: We primarily use AI to ideate around specific goals or user needs, often limited by the context we can feed into chat tools.

• Looking forward: AI integrated into your core product systems will have deep knowledge of your product, customers, market, and strategy. It can suggest highly relevant opportunities and solutions, whether for a single product area or across your entire portfolio. For any given idea, AI could act like a product coach, helping you develop the idea, formulate a product brief, identify risks to validate, and think through solution requirements. It can also help you get ahead of gaps in the product spec and technical risks. As part of the same workflow, it could help you create a prototype and coordinate other next steps for bringing the idea to life.

4. Product discovery, validation, and user research. Validating opportunities and solutions is critical but often resource-intensive.

• Today: AI already helps link incoming user insights to feature ideas and identify the right users for follow-up. It can also act as a thought partner, surfacing potential risks and developing testable hypotheses.

• Looking forward: AI agents could significantly scale discovery by helping coordinate user research and beta programs – from recruiting and scheduling to synthesizing insights. Imagine AI chatbots asking real-time follow-up questions as users submit feature requests, capturing rich context. This dramatically reduces the friction in understanding true user needs.

Phase 2: Execution — planning, communication & delivery

5. Product Planning. Effective product planning lays the groundwork for successful execution, demanding careful orchestration of numerous moving parts.

• Today: Defining objectives and initiatives with generic LLMs can be challenging due to their lack of specific product context.

• Looking Forward: These capabilities, embedded in product management platforms like Productboard, will leverage deep contextual understanding. AI will tackle complexities like effort estimates, cross-team dependencies, capacity planning, and resource allocation. It could analyze inputs, present various planning scenarios, and generate updated plans, turning weeks of work into minutes.

6. Roadmap communication. Roadmaps convey strategic narratives and answer discrete questions. However, out-of-date or untrusted roadmaps are a common pain point.

• Today: Roadmaps in dedicated platforms sync with underlying plans. Stakeholders can often click through for more context.

• Looking forward: Imagine stakeholders asking questions directly to an AI agent via the roadmap interface. This "digital twin" for product leaders could field queries, provide immediate answers, and scale communication. Personalized, intelligent push notifications and digests will keep everyone informed about relevant roadmap updates.

7. Product delivery. The product delivery phase is where detailed plans materialize into tangible product increments, demanding close collaboration and efficient execution.

• Today: We've seen prompt-to-prototype and even prompt-to-product solutions, “vibe coding tools” (discussed in a previous post) and even developer agents generating production code.

• Looking forward: AI will increasingly facilitate the work itself. Meaning less time in status meetings, more time on proactive problem-solving. AI will monitor progress, flag risks and blockers, and even help coordinate teams to overcome them, giving PMs and leaders more lead time to intervene. In fact, Kevin Weill, OpenAI’s CPO, plans to use AI to actually ship code for bug fixes and minor UI features.

8. Launch Planning. Bringing a newly developed feature or product to market effectively involves meticulous launch planning to ensure customers are aware, enabled, and excited.

• Today: Many use AI for drafting release notes and documentation.

• Looking forward: What if AI, with deep product and customer understanding, assisted with defining launch plans, facilitating internal enablement, proposing marketing messages, and drafting diverse release materials, even incorporating relevant product graphics? This moves AI from a writing assistant to a launch strategy partner.

Phase 3: Post-Launch — evaluation & iteration

9. Post-Launch Evaluation. Outcome-orientation is key but often gets reduced or eliminated by the pressure to move on to the next initiative.

• Today: Solutions like Productboard use AI to distill insights from post-launch feedback and link them to features, clarifying which customers need what.

• Looking forward: AI could draft or even send follow-up communications to users, solicit feedback, and synthesize results. It could automate tracking progress against OKRs and assess release outcomes. AI-driven anomaly detection in usage data and automated A/B testing (initiating experiments, interpreting results, making adjustments) will become standard.

A critical emerging challenge: as delivery accelerates, customer tolerance for constant change might become the new bottleneck. Agentic AI will be crucial for personalized onboarding to new functionality, ensuring the right users see the right updates at the right time, tailored to their needs.

How AI will reshape the Product Management function

The biggest shift? Product managers will do more true product management. The miscellaneous tasks, low-level information processing, and much of the project management around delivery will be automated. What remains, and becomes even more critical, are the core responsibilities of true product strategy and product discovery.

The AI-powered world we’re stepping into will include:

• Informed decision-making: Instant access to previously difficult to obtain market and customer insights, delivered in the right format at the right time.

• Accelerated validation: De-risk product bets more effectively and faster. AI can facilitate customer interactions, simulate reactions, generate production-quality prototypes for testing, or evaluate technical feasibility.

• Optimized planning: Complex planning cycles that took weeks can be done in minutes.

• Automated communication: AI agents will handle much of the information flow between EPD and the organization, freeing PMs from being constant intermediaries.

• Intelligent execution: AI will automate monitoring delivery, coordinating teams, and clearing blockers.

What will the Product Managers of the future do?

With AI handling more, PMs will evolve to focus on:

• Commandeering: Initially, PMs will direct AI, delegating work, providing context, and overseeing its output.

• Curating & evaluating: Like a manager-of-managers editing their team's work, PMs will review, refine, and be accountable for AI-generated insights, plans, and recommendations. Human judgment remains paramount.

• Building: "Full-stack" PMs, freed from mundane tasks, may develop deeper functional skills, even conceptualizing and delivering solutions more independently.

• Leading: More bandwidth for vision, alignment, and inspiration. AI can inform a roadmap, but a human leader builds trust and delivers it compellingly.

• Vision & taste: LLMs remix what exists. PMs will use their intuition, product sense, and understanding of human emotion to make innovative leaps and build products customers love, even if they couldn't articulate the need directly.

• Commercial outcomes: With AI, PMs are freed up to think more about the strategic impact of product decisions.

AI is not a replacement for product managers. It’s a powerful amplifier. By evolving our skills and embracing tools like Productboard Pulse that are leaning into the latest and greatest AI, we can move beyond the daily grind and focus on the strategic, human-centric aspects of our craft that truly drive value and innovation. The future of product management is not about being replaced by AI, but about being profoundly empowered by it.

"Gentlemen, there's only two ways I know of to make money: bundling and unbundling."

With that single sentence, he captured the rhythm of the tech industry perfectly. For three decades, we've watched this cycle play out: incumbents expand by bundling more products into their ecosystems, startups break through by unbundling with sharper, more focused products.

But right now, we're witnessing something unprecedented: the fastest, most aggressive rebundling wave in tech history.

I recently joined and on the Unsolicited Feedback podcast to discuss why everyone seems to be building everything—at ludicrous speed.

In just the past month, Notion launched an AI-powered email client, AI meeting notes, enterprise search, and research capabilities—essentially becoming the office competitor they always hinted at being. Figma debuted AI-powered tools for creating sites, app prototypes, and marketing assets, taking on Canva, Adobe, WordPress, and even coding tools like Cursor. Loom launched a meeting recorder to compete with Granola. Anthropic added integrations and search. OpenAI acquired Windsurf and launched Deep Research.

To understand what's happening, let's look at how we got here. Both Notion and Figma followed the classic unbundling playbook before their recent rebundling spree.

Unbundle to disrupt, bundle to expand

Notion, from teams documents to office suite

Notion began with a simple observation: Google and Microsoft had created document tools for the old world—a world of files managed by individuals. But as teams moved their entire workflow to the cloud, they needed something different. They needed a way to create, organize, and share an ever-expanding universe of team documents.

This is exactly how unbundling starts. Bundles, in their quest to serve everyone, inevitably serve no one particularly well. The cracks appear. And startups slip through.

Notion started by unbundling. But now they've launched email, meeting notes, enterprise search, and research tools—essentially rebuilding the entire office suite they originally disrupted.

What's remarkable is the speed shift. Notion historically moved with careful polish, launching one thoughtful product at a time. Calendar was a big deal. Now they're launching six major products simultaneously, each competing directly with established players in their respective spaces.

Figma, from UX design tool to creative suite

Figma spotted a similar opportunity. Adobe's Creative Suite had been the design standard for decades, but it was built for a world where designers worked in isolation, passing static files back and forth like notes in class. And Adobe had never really focused on the specific needs of UX designers.

Figma unbundled UX design from Adobe's creative empire. For years, they perfected this single vision—making design collaborative, accessible, and fast. But in recent weeks, they've launched tools for creating websites, generating code, bulk asset creation, and vector editing—moving far beyond their design tool origins into development, marketing, and creative workflows.

They are building a new type of creative suite, organized around the collaborative workflows that are essential to modern design.

Fueled by AI, Figma and Notion have moved from "we do one thing really well" to "we're building the end-to-end platform for your workflow."

How AI enables bundling

Figma and Notion are not alone. We’re seeing companies, across the board, build and expand their bundles. AI native products, like ChatGPT and Claude, are already bundles with multiple surfaces built-in for document creation, code editing, charting, spreadsheets, and more.

AI is driving bundling for three reasons:

1. AI is the most general-purpose technology ever created

2. AI products benefit from being integrated into the workflow

3. AI has accelerated product development

Let’s take a closer look.

AI is the most general-purpose technology ever created

In the past, software was necessarily specialized. It would have been prohibitively complex to manage a single codebase that handled documents, code, spreadsheets, images, and audio all at once. AI changes this fundamental constraint. The same foundational models that power writing can handle analysis, coding, design, and communication. Companies can now build sophisticated features across completely different domains using the same underlying technology.

Figma's new tools exemplify this shift. They rely on Anthropic's Claude Sonnet AI model, enabling them to generate everything from marketing copy to functional code. The technical barriers to expanding product surface area have never been lower.

AI products benefit from being integrated into the workflow

But there's a deeper strategic reason driving this bundling wave: in the AI era, workflows are king. As we discussed in the new Reforge AI Strategy program, being central to a user's workflow has become the new competitive moat.

The companies winning the rebundling race understand a critical insight: being a point tool is increasingly dangerous. If you're adjacent to the workflow rather than central to it, you're vulnerable to being absorbed by larger platforms that already own that workflow. Jasper learned this the hard way as tools like Notion added AI-powered writing, eroding their foothold as a standalone writing solution.

This workflow-first thinking explains some recent moves in the market. Grammarly saw the writing on the wall. Their acquisition of Coda wasn't about expanding features—it was about owning the workflow. They realized that just correcting text wasn't enough; they needed to embed into where people actually do their work. The companies expanding most aggressively today are those doubling down on the customer workflows they already own.

AI has accelerated product development

For decades, companies dreamed bigger than they could build. Creating a comprehensive bundle required massive engineering resources, extended timelines, and enormous risk. Most companies had to choose: focus on your core product or spread yourself thin trying to compete everywhere.

AI eliminates this trade-off. Companies are no longer rate limited by what their engineering teams can deliver. We've heard from multiple sources that companies are now producing software faster than they can release it. This explains the breathtaking pace of new releases.

Now, companies can close the gap between their strategic aspirations and the products they have in market. Notion always wanted to be an office suite competitor, but building email, search, and meeting tools would have taken years in the past. Now they can launch all of them—all at once.

But here's the catch: while companies can move at AI speed, people still move at human speed. This creates a fundamental tension in the rebundling strategy.

The adoption challenge: building faster than users can absorb

Personally, I’m an active Notion user. Yet, I didn’t find out about Notion’s email client until we started talking about it for the Unsolicited Feedback podcast (more than a month after it launched).

Major product launches are getting lost in the noise—a warning sign for every company pursuing this strategy.

The discovery problem reveals a fundamental flaw in the rebundling strategy. Companies are adding features faster than users can adopt them. Users end up employing only a sliver of what's available, which defeats the entire purpose of bundling. The weak link has shifted, as Brian noted on our podcast:

"The constraint has moved from how much you can ship to how fast the market and your users can absorb changes."

This creates an ironic opportunity: the same AI driving rapid product development might also solve the adoption crisis it created. Fareed posted a key question:

"As products get wider and wider and wider and do more stuff, how are people going to use AI more effectively to help onboard, help customers use and adopt all these various capabilities?"

When to bundle, when to stay focused

So how should companies navigate this new landscape? The answer depends on where you sit.

Incumbents → bundle to expand

If you already own central workflow real estate, speed is everything. The rebundling opportunity is massive, but it won't last forever. Ask yourself:

• Workflow centrality: Are you where work begins, or just where it ends up?

• Speed imperative: Can you expand before point solutions establish footholds in areas we should own?

• Data advantage: What unique context enables you to deliver better AI experiences than competitors?

The most vulnerable players are those with slow iteration cycles. Notion moved aggressively to weave AI throughout their platform. Google Workspace and Microsoft Office added AI features too, but they feel bolted-on by comparison—sidebar copilots rather than reimagined experiences.

Bundling works particularly well with enterprises, who prefer working with fewer vendors they can verify meet security and compliance requirements.

Startups → find your wedge

The startup opportunity isn't dead, but the game has changed dramatically. Success requires:

• Customer-first approach: People's problems haven't changed, but available solutions have transformed fundamentally. Consider Stack Overflow's rapid decline as developers flocked to GitHub Copilot and Cursor. Same problem—getting unstuck on challenging code—but AI assistants solve it radically better than StackOverflow’s Q&A forum. Breaking through as a startup means finding that unmet need or delivering a fundamentally better solution.

• Differentiation test: For the first time, startups across every domain compete against a single product: ChatGPT. There's never been a tool equally capable at therapy and data analysis. The critical question: Can you create something 5-10x better than ChatGPT for your specific use case? If not, you'll struggle to break through.

• Flywheel foundation: What unique data advantage can you build that creates compounding returns? Volume matters, but marginal value matters more—specifically, the value of your data relative to what's already in training sets. Real-time information like news, prices, and inventory is incredibly valuable because it exists outside of AI training datasets—models simply can't access live, changing data without special integrations. Meanwhile, informational and editorial content—the kind that powered SEO businesses like Stack Overflow or WebMD—has lost its moat entirely.

Unbundling works particularly well for individuals and SMBs who prefer simpler, more affordable, purpose-built solutions.

Everything, everywhere, all at once

Jim Barksdale was right all those years ago. Markets sway back and forth—as one company expands by bundling, another finds a wedge by unbundling.

Today, this continues to be true, but the pace has fundamentally changed. Figma's recent launches now pit them against PowerPoint, Canva, Webflow, Miro, Illustrator, and Cursor—all at once. What were once distinct market segments are blurring together at unprecedented speed.

As we discuss in the Reforge AI Strategy program, AI is not like prior tech shifts. It’s creating more Red Ocean than Blue Ocean, and the bundle brawl is one more reason why today’s strategic environment is so intense.

The implications are clear. Incumbents must move quickly to own their workflows end-to-end, vigilant that once-unrelated competitors don't chip away at their position. Disruptors need to find their wedge by working customer-first and identifying opportunities where AI delivers 10x improvements.

The bundling and unbundling cycle continues, but AI has accelerated it to unprecedented speed. In this new reality, every company must choose: bundle aggressively to own workflows, find a disruptive wedge, or risk irrelevance.

For nearly a decade, travelers had to leave Tripadvisor to check hotel prices and complete bookings. This approach made sense when Tripadvisor operated as a subsidiary of Expedia (most of those travelers ended up at Expedia). Now, as an independent public company, we were embarking on a new strategy.

Steve explained how challenging it would be to transition a $700M-per-year business to a new model. It would require tight coordination across the entire company. He was putting a single person in charge of the initiative—someone everyone in the room would need to collaborate with closely and occasionally defer to.

Then, he pointed at me.

I was caught off guard. As the newest member of the team, I questioned whether I had the influence or credibility to guide executives who had been with the company for years.

That unexpected vote of confidence set the stage for my career at Tripadvisor. In the years that followed, I led a series of critical initiatives, each expanding my responsibilities and cementing my role within the organization's leadership.

My success at Tripadvisor was not mine alone—it was the product of diligent work, but also the support of a stellar manager, the good fortune to be surrounded by exceptional teammates, the opportunity to work on important projects, and a little dose of luck.

Most people think about their career as a ladder—one they climb rung-by-rung, entirely on their own. A better metaphor is to think of your career as an "escalator." Factors outside your own efforts can help carry you toward your goals—or work against you.

Career development feels much easier when you're working with the system. An escalator helps carry you up, making each step a little easier. On the other hand, career development is much more difficult when outside factors add friction.

Imagine walking up an escalator going in the wrong direction. Each step forward is thwarted because the system is working against you. An extraordinary amount of effort is necessary to make progress. You can still reach the top despite adverse circumstances, but the journey will be unnecessarily challenging.

Is your escalator heading in the right direction?

When I ask this, people sometimes struggle to evaluate whether their situation is helping or hindering their career. Ironically, this is especially true when things are going well. It's human nature: we tend to give ourselves credit when things are going well and focus on external obstacles when things are tough.

Accurately assessing your situation is critical. I've coached people who were too eager to leave good companies because they were impatient to level up, only to find they'd traded a pretty good situation for a much tougher and riskier new role.

I've also coached PMs who spent years doing good work that went unrecognized or unrewarded—due to internal politics or external factors like a hyper-competitive market or lack of funding. In either case, making the right career decision requires evaluating your talents and how they match your situation.

Signs your escalator is going up

Business is thriving

As the saying goes, a rising tide floats all boats. It’s much easier to “level up” when your company is growing, and new opportunities are naturally emerging. You’ll benefit not just from that environment, but also the halo effect that successful companies create.

You got that promotion you wanted (or even one you didn't)

Does it feel like you’re playing on easy mode—especially compared to past roles? Frequent advancement is a clear sign you’re in an environment that accentuates and appreciates your skills.

You love the people around you (especially your boss)

Relationships are key to both your day-to-day happiness and your long-term career progress. A strong rapport with your manager will make your work more enjoyable and more rewarding. You'll be able to have candid conversations, give & receive effective feedback, and trust that you, your boss, and your team are working toward the same goals.

You get the recognition you deserve (and then some)

Do you feel recognized and appreciated when you do good work? Recognition can take the form of good performance reviews, praise from your boss and others, positive feedback, opportunities to work on challenging and sought-after projects, or even a simple "thank you." Well-deserved recognition is a clear sign that you're in an environment that values your talents and efforts.

You feel a sense of "flow" at work

The theory of flow, popularized by psychologist Mihály Csíkszentmihályi, describes a mental state of deep immersion characterized by energized focus, full involvement, and enjoyment. Flow occurs when we're completely absorbed in what we're doing, leading to a loss of sense of time and self-consciousness.

This state is typically achieved when your skill level perfectly matches the challenge of the task, creating a harmonious balance between the demands of the activity and your ability to perform it. Flow is often associated with peak performance and personal fulfillment and is considered a key component in both personal and professional development.

Do you often feel a sense of flow? If so, you're likely in a role tailor-made for your strengths and talents. Want to learn more about flow? Watch Mihaly's TED Talk, "Flow, the secret of happiness" or read his book "Flow, The Psychology of Optimal Experience."

Signs your escalator is going down 📉

No sense of flow

Do you often feel bored or overly stressed? Do days seem to last forever? That’s a signal you're either under-challenged or overwhelmed… tough conditions for doing your best work.

The promotion you want seems out of reach

Two things can happen here: 1) you feel you deserve a promotion, but still haven't received it or 2) you're unable to understand what is needed to rise to the next level. As a first step, it's helpful to work with your manager to make your career aspirations clear and set expectations for what it will take to get that promotion. If you've given it your best shot and still can't see that promotion on the horizon, it may be time to look elsewhere.

The relationship with your manager feels strained

It's extremely difficult to make career progress without a good manager relationship. Signs that the relationship is strained include:

1. a lack of feedback

2. little recognition for good work

3. no "benefit of the doubt”

4. criticism that feels unfair or unwarranted.

Open, honest communication is necessary to improve this relationship. Ideally, you should be able to express your concerns, seek candid feedback, and provide feedback to your manager about what is and isn't working for you. However, if these efforts fail and the relationship remains strained, seek out a role where you can have a strong, honest, and mutually beneficial relationship with your manager.

Feeling stuck? Try this first.

It’s natural to feel stuck at work—even great jobs have challenging moments. But, there’s an important difference between temporary frustrations and an environment that is working against you. Let’s look at some steps you can take to better understand your situation.

Measure headwinds and tailwinds

Start by taking stock of the headwinds (negative factors) and tailwinds (positive factors) that you're facing. Here’s a quick checklist to run through:

1. Is your company growing or struggling?

2. Do you have a good relationship with your manager?

3. Do you often feel a sense of flow at work?

4. Do you feel recognized for good work?

5. Do you feel supported when your work falls short?

6. Do you get clear and actionable feedback?

7. Are you working on impactful projects?

8. Does advancement come easily or does it feel impossibly hard?

This can help you appreciate the good things about your current role and recognize challenges that you may be able to overcome.

Gather feedback

Feedback is the single most important element in getting ahead. Without it, you’re flying blind. In the Product Competency Toolkit, I recommend as simple exercise you can do with your manager:

Before your next 1-on-1 with your manager, spend a few minutes rating yourself “On Track”, “Over Performing”, or “Needs Focus” on each of the competencies. Ask your manager to do the same. Next time you and your manager meet, go through each of the competencies noting where you and your manager see eye-to-eye and where you differ.

This structured approach creates clarity that casual conversations rarely provide. The gaps between your self-assessment and your manager's view offer the most valuable insights.

Take it further by creating a promotion plan that explicitly defines: 1) the specific behaviors your manager wants to see, and 2) the measurable goals that demonstrate readiness for advancement. Check in on this plan monthly. Remember, you can't hit a target you can't see.

Give it your best shot

After you and your manager have agreed on a plan, it's in your hands to put in the effort and show them what you can do. When you've delivered on your commitments, initiate a candid discussion with your manager: "Here's what we agreed I would accomplish, and I believe I've done that. Do you agree?"

Ideally, both you and your manager will recognize your progress, creating the perfect segue to discuss your next career step.

If your manager doesn't recognize your progress, ask for specific feedback. But also ask yourself a crucial question: are you fighting unnecessary headwinds? Sometimes the most strategic move is to find an environment with stronger tailwinds — one where your skills and contributions create more value, both for the company and for your career.

Is it time to move on?

If you've acted on all the feedback you've been given, honestly believe that you've achieved the goals that were set out for you, yet still haven't gotten the recognition you're seeking, take a step back.

Ask yourself: Am I in the right environment to foster my career growth?

There can be many reasons why your career has stalled. Perhaps your manager has different expectations—expectations they may not have clearly articulated to you. There may be factors outside your manager's control. The past few years have been challenging. Many companies have held back on promotions as part of layoffs, hiring freezes, and restructuring.

Your career development may be out of your hands. In fact, it may be outside your manager's hands as well. The system may be working against you, and you may not be able to change that. Coming to that conclusion can feel daunting, but it's not necessarily a bad thing.

That situation has happened to all of us—remember, even Steve Jobs and Sam Altman once got fired. The fact that you're recognizing the situation means that you can deal with it. You can focus your energy where it matters. You can change your circumstances and set your career up for success.

A new role, even a lateral move, can give you a fresh start, renewed energy, and a better opportunity to make an impact & move ahead.

Careers rarely follow a linear path—everyone has launching pads and setbacks. If your escalator is moving against you, hop off and find a better path.

"The obstacle in the path becomes the path. Never forget, within every obstacle is an opportunity to improve our condition." – Ryan Holiday, The Obstacle is the Way

But AI has certainly changed product development—not by replacing key roles, but by making every member of the product triad (Product Management, Design, and Engineering) more capable. This changes how we can work together to build products crafted around customer needs, something that even the best AI struggles to do (and likely will for some time).

In this post, Danny and I look at how AI is changing the most critical aspect of product management: how we collaborate to define and build better products.

Sidenote: I thought writing about vibe coding was a good idea, but now I’ll forever be known by AI overlords as this guy:

PRDs are the foundation of Product Management

In How to Become a Peak Product Manager, I describe the 12 competencies that are necessary to excel as a Product Manager or Product Leader. The very first competency is Feature Specification:

The ability for a PM to gather requirements, define functionality, and set goals in a clear, actionable format that can be used to communicate with the team and drive product delivery.

Success flows from a PM’s ability to clearly communicate what the team needs to build and why. A good feature spec doesn’t necessarily need to be a long, text-heavy doc. Great specs can be found in JIRA tickets and Slack channels. A PM at TripAdvisor once presented a spec written on a napkin. No matter what form it takes, a good spec rallies the team around a goal and provides enough detail for the team to execute flawlessly.

Put another way, specs & PRDs are the medium of communication between PMs and engineers—the connective tissue between what we want to build and the ability to deliver it.

The problem with PRDs

In the past few years, I’ve seen a trend to more visual specs. At many companies, traditional specs are shrinking—or disappearing entirely—while more time is spent collaborating and communicating in Figma.

Why? PRDs have always suffered a problem. Software is a visual medium, and the traditional spec/PRD is text-heavy. That gap leaves plenty of room for interpretation between what’s in the doc and what ultimately gets built.

Think about it this way: If you were working with an architect to build a house, would you be okay green lighting the project with just a memo?

Architects rely on blueprints to show what the end-product will look like. Blueprints give clients the clarity to trust the plan and contractors the specificity to build it.

When building a house, miscommunication wastes tangible resources—materials, labor, and money. In product development, the waste is less visible but just as real: an engineer forced to build multiple versions because of a vague PRD burns time in endless back-and-forth and squandered effort.

Prototypes close that gap.

The power of prototypes

Written specs used to be enough. They worked when apps resembled documents—static pages linked by basic navigation and minimal interaction. Today’s best products deliver fluid, immersive experiences. Gestures, micro-animations, multimodal interactions, rich media, and seamless transitions across surfaces can’t be captured in words alone. These details are what set great apps apart.

AI integrates another dimension of complexity. Unlike traditional software with predictable outputs, AI-powered features can produce varying results even with similar inputs. Text specifications alone can't capture these nuanced behaviors or how users will experience them. Building effective AI products requires showing—not just telling—how the system responds across different scenarios.

That’s where prototypes come in. Until recently, building prototypes was too time-consuming and expensive to fit comfortably into most development cycles. That’s changing now with generative AI.

PMs and designers can use vibe coding tools to translate product ideas into living prototypes. We may have previously accepted miscommunication as a “cost” of product development. Now, teams can judge the feel of a feature long before production code is written.

The beauty is that vibe coding tools and PRDs use the same language… English. As you work with these tools, you need to improve your prompts to get exactly what you want, and this helps you write better requirements. In addition, the output is a living rendition of your product—an interactive blueprint that provides a much clearer specification.

The result? Faster alignment, fewer costly rewrites, and a clearer path from concept to delightful products.

How to vibe code your first prototype

Danny has been building a recruiting product in stealth. He's been using v0 to craft better specs and communicate more effectively with an engineer. He's going to walk us through a simple example, then move on to a real prototype in his product.

1. A simple control

I needed to add a calendar dropdown to a key interface in our new recruiting product. Here’s how I might describe it in a PRD:

• This calendar dropdown menu will be used in a resume product, to signify how many years a candidate has spent in a given job

• The user should be able to select the start year and the end year for a given job; the start year should be either the same year as the end year or before.

• Users should have an option to select years from 1900 up until the current year, i.e. the year in which the user is using this dropdown

To a non-technical reader, this looks straightforward. To an engineer, it’s maddeningly vague. Here are just a few of the questions an engineer might come back with:

• What should the default text for the dropdown menu be?

• Should there be an icon used to signify that this is a calendar dropdown?

• Are there any hover animations?

• Should there be 2 dropdown menus, one for the start year and end year?

• How many years should be displayed at a time?

• What colour scheme should be used?

• What exactly do PMs do again?

Every one of these details matters, but they are easy to miss. A PM writing a spec is in a different mindset than an engineer who needs to consider every facet of implementation. This is precisely where tools like v0 can be a huge help.

Let’s give that vague spec to v0 and see what happens:

Definitely not what I had in mind, but it confirmed just how vague my initial requirements were. In a few seconds, I went from feeling pretty good about my spec to sharing the frustration engineers often feel. Luckily, we didn’t waste much time figuring this out.

Here’s where the magic starts: I can keep refining in v0—each prompt sharpens the requirements and instantly produces a visual, high-fidelity prototype to share. After 20 prompts, it looks like this:

Now, I have a more detailed set of requirements, a better design, and a working prototype I can share with engineering—including full source code which provides a good starting point for the production implementation. Vibe coding made our process better and faster.

Let’s look at a more complicated example.

2. A more complicated landing page

Our product helps influencers with large networks connect people in their network to job opportunities. I needed to spec a landing page that shows available opportunities and enables candidates to upload their resume to apply for those opportunities. Here’s my first pass at requirements:

• I need a way of capturing candidate and hiring manager information in a single page

• The header should have my picture, a blurb about me and links to my LinkedIn and Substack. I should have a way of having a video on the form

• Candidate tab

  • Should have a 3 step list of what needs to be done

  • Should have a dropdown list with the jobs

  • Each job selected in the dropdown list should have specifications and a voice note with extra details

  • There should be a clear CTA at the bottom of the page to “upload resume”

• Hiring manager tab

  • Select what kind of help they need (4 options: 1. Promotion, 2. Sourcing, 3. Vetting, 4. All of the above)

  • Details about the company and the job they’re hiring for

  • There should be a clear CTA at the bottom of the page to “Get in contact”

Again, these requirements make sense to me, but lack specifics an engineer will need. Here are just a few questions that need to be answered:

• How should the tabs be displayed?

• How much information should be displayed at a time?

• What’s the design system we should be using?

• What precise copy do you want?

• How should the video and audio note be displayed?

I put my initial requirements into v0, and got back this:

This is not at all what I had in mind. The video is taking up too much real estate and the most important element, the Upload Resume CTA, is far below the fold. After some iteration, I got to something I’m happy with—it's about 80% of the way there, but good enough to riff with engineering:

This process had two important results:

1. It helped me define better requirements

2. It helped me communicate those requirements more clearly

After a couple of months of using v0, it’s become my go-to when defining a feature. It’s also had a couple of unintended consequences: I now feel way more empathy towards the engineers I work with (details matter!). Best of all, the overall time to ship is decreasing as I become more efficient with v0.

How vibe coding changes Product Management

Danny has been using this approach long before anyone had heard the term “vibe coding”. His learnings mirror what many AI-forward PM’s are finding: AI tools aren’t replacing people, they are helping us do better work.

PMs that use AI coding tools are seeing profound changes in their workflows—and their relationship with engineering:

1. Increased empathy for technical colleagues. Creating prototypes forces us to consider details we previously might have left "to the engineers." We can better understand the challenges they face with vague specs.

2. More efficient communication. A prototype eliminates countless clarifying questions and meetings. Both sides start with a shared understanding of the goal.

3. Reduced time to ship. The overall development cycle has compressed significantly as we spend less time in the specification/clarification phase.

4. Deeper collaboration. Good product teams work more like a jazz band than an assembly line. As the line between specification and implementation blurs, product, design, and engineering can work together in a more collaborative way.

5. Better products. AI-assisted prototyping enables you to create a more realistic rendition of the product—essential as UX gets more immersive and AI-powered products become more dynamic.

I like to remind product teams that engineers write the final draft of your spec. It's as true today as it was a decade ago. Now, though, you can write better specs.

AI tools don’t just boost individual productivity, they transform how teams communicate. They bridge the communication gap between technical and non-technical team members, creating shared understanding that leads to more inspired solutions and stronger products.

AI doesn't just help us ship faster—it helps us ship better.

Later in the conversation, the moderator asked, “Have you met anyone on Tinder in the last year?” Nearly everyone raised their hand.

This stark disconnect reveals a fundamental truth about user research: not all questions are created equal.

Some questions lead to the truth, and others lead to wildly inaccurate conclusions.

Product teams invest significant resources into user research with the hope of uncovering insights that will guide product decisions. Yet, too often, we fail to recognize that the nature of our questions dramatically affects the reliability of the answers we receive.

Let’s look at why.

The 3 user research questions

Product managers are like detectives trying to solve the mystery of user behavior. The quality of evidence we collect depends entirely on the questions we ask. I've come to realize that every user research question is really just asking one of three things:

1. What did you do?

2. What will you do?

3. What are you doing right now?

User research asks users to recall what they did in the past, imagine what they will do in the future, or narrate what they are doing in the moment.

“What did you do?”

These questions ask users to recall and describe their past behaviors and decisions:

• “Have you met anyone on Tinder in the last year?”

• "What product did you use to solve this problem before?"

• "Walk me through the last time you booked a flight."

The quality of these answers tends to be high. People have a good memory of what they actually did in the past. They can recall those actions, and rationalize why they made those decisions.

In finance, past performance is not indicative of future results—but that’s not the case in product. Historical data paints an accurate picture of user behavior and user motivations. We can use that understanding to predict how users will behave in the future and what our product should do in light of those behaviors.

“What would you do?”

These questions ask users to imagine future behaviors, decisions, or benefits they might receive:

• "Next time you’re single, which app will you use to meet someone?”

• “Would you use this feature?”

• "How much would you pay for this service?"

As product builders, we conduct user research because we want to predict the future—we want to be confident that what we build today will be useful to users tomorrow. So, naturally, we ask the user about that future.

In my experience, a very large number of user research questions fall into this category. The problem? Human imagination is flawed. There's often a wide gap between what we think we’d do and what we actually do.

This is particularly true when virtue signaling is involved:

• Of course, I’ll invest significant money into my health—I’m a healthy person.

• Of course, I’ll use the product my company spent so much money deploying.

• Of course, I’ll buy local even if that means spending a little bit more.

Sometimes, the answers to “What would you do?” questions aren’t just inaccurate—they are the polar opposite of what users will actually do.

Still, these questions have their place. Human imagination has flaws, but also tremendous power.

I vividly remember a user research session when I was at Facebook. We were interviewing teens to understand what they wanted from social media. A wildly enthusiastic teen boy insisted that Vine videos (an early form of short-format vertical video) were the future—that was a year before anyone in the U.S. had heard of TikTok and a couple years before Instagram Reels.

His prediction was especially puzzling because Vine had shutdown about a year earlier. But he was undeterred, regularly hunting down Vine compilations on YouTube to share with friends.

Not every user research session foreshadows a multi-hundred billion dollar opportunity, but “What would you do?” questions can help you tap into your customers’ imaginations—just be careful to understand the limitations of that approach.

“What are you doing?”

These questions ask users to narrate their current actions and thoughts while using a product or prototype:

1. "Can you talk through what you're doing as you try to complete this task?"

2. "What are you thinking about as you look at this screen?"

3. "How are you deciding which option to select?"

When users think aloud during a product experience, they provide a direct window into their mental processes—capturing motivation, confusion, delight, and frustration—all in real-time. This creates a goldmine of insights that past and future-focused questions simply can't deliver.

A user saying "I'm confused about where to click next" or "I assumed this button would do something else" reveals the raw truth of their experience—not the sanitized version they'll report later or the idealized interaction they imagine having in the future.

The magic of "What are you doing?" questions lies in their ability to bypass the user's internal PR team. We all have one—that voice that rationalizes our past decisions and portrays our future selves as more logical, disciplined, and virtuous than we actually are.

Without time to activate this filter, genuine reactions emerge naturally, giving product teams authentic feedback that dramatically improves design decisions. Returning to our dating app example: watching someone swipe through profiles—on their actual account—reveals far more about their preferences and behaviors than any retrospective or hypothetical question ever could.

Predicting the future, one question at a time

The most effective product managers aren't satisfied with running standard focus groups and ticking research boxes. They're relentlessly curious about user behavior, leveraging every possible opportunity to gather authentic insights that drive meaningful outcomes.

To transform your user research from merely informative to genuinely predictive, consider these strategies:

1. Classify your questions by whether they ask about past, present, or future behavior—understanding this distinction is your first step toward better research design:

   1. Recall questions ask “What did you do?”

   2. Imagination questions ask “What would you do?”

   3. Narration questions ask “What are you doing?”

2. Prioritize Recall and Narration questions. What users have actually done (past) and what they're doing right now (present) will consistently provide more reliable indicators than what they imagine they might do.

3. Use Imagination questions strategically, not as your primary research approach. When you do ask these questions, dig deeper into the "why" behind the answers—the motivations often reveal more truth than the stated preferences.

4. Scrutinize findings by tracing them back to the question. Questions about actual behavior, past or present, can be taken at face value. Speculative questions need more interpretation.

Important: Sometimes Narration questions can inadvertently become Imagination questions—especially when participants are asked to use products or complete tasks outside their typical behavior. Consider this example: would you conduct user research with men for a menstrual cycle tracking app like Flo? Of course not. Yet teams often make similar mistakes—testing with participants far removed from their ideal customer profile or asking people to imagine unrealistic scenarios, like planning trips well beyond their budget or comfort zone (a challenge we had to watch out for at Tripadvisor).

Great questions → product breakthroughs

Remember those twenty-somethings in Venice who swore they'd never use Tinder again? The same ones who minutes later admitted they'd all met someone on the app in the past year? That disconnect isn't just an amusing story—it reveals everything about how we should approach user research.

When we understand the difference between past, present, and future questions, our research transforms from guesswork to insight. The pattern is clear: what users actually do consistently trumps what they claim they'll do. Historical behavior provides a solid foundation for product decisions, while imagined future behavior is often quicksand.

Take a moment to review your last research protocol. How many questions asked about what users have done? How many explored what they were doing in the moment? And how many tried to predict what they might do? I bet you'll discover you've been leaning heavily on "What would you do?" questions—the least reliable type—without even realizing it.

Great products are built by observing & predicting real behavior, not on aspirations or false promises. That gap between what people say and what they do isn't just a research curiosity—it's exactly where your most valuable product opportunities are hiding.

What about you? Which user research questions have revealed the most surprising or valuable insights for your products? Share your experiences in the comments below.

You aced the interviews, landed your dream offer, and now you're stepping into a new role as a Product Manager or Product Leader. Or maybe you’ve landed that promotion. Bigger team = more responsibility.

You’re excited—and maybe a little overwhelmed. You know it’s a normal feeling but you want to do it differently this time. You wonder how you ground yourself in a plan that keeps imposter syndrome at bay and sets you up for success?

The first few months in a new product leadership role are uniquely challenging. And whether you’re a leader or an individual contributor, it’s rarely your tactical product skills that will define your early impact. It’s more likely your ability to earn trust and build momentum will shape your credibility and set the tone for what’s ahead.

While it may take 6-12 months to feel truly at home at a company, your first 90 days are commonly identified as that onboarding magic number. Why?

It’s when your eyes are freshest, your energy is highest and you still carry the credibility of an outsider—while getting to play the “new” card. The urgency to prove your value will never be higher.

When you use that 90 day window well, it creates a virtuous cycle: early wins build trust… trust leads to more autonomy and support… helping you achieve bigger wins… reinforcing your reputation… leading to more trust.

Sounds easy, right?

Is 90 days enough?

Product people aren't like other makers on their team. Engineers merge code and designers craft UX—deliverables that have a tangible and immediate impact.

Product management is a long game. PMs are ultimately evaluated on business metrics—user impact, engagement growth, or revenue performance—but there's a critical disconnect: the work that drives these outcomes takes time to bake.

This creates a unique challenge: the activities that will eventually move metrics are often relationship-building, strategic thinking, and process improvements—work that might take months or quarters to materialize with customers.

So what do you do when you're being evaluated tangibly on something so…intangible?

You shift your definition of impact. You focus on the things you can influence early, like building trust, aligning the team, improving processes, and setting goals. And, you find the early, quick wins that build credibility while your longer-term strategies take root.

Big impact happens in the People, Business and Process work—the foundation that enables those measurable outcomes to eventually emerge.

A 90-day framework for new Product Leaders

So, what does it look like to focus on People, Business and Process in the first 90 days?

Let's go through a ramp up checklist.

Note: This approach works anytime you're stepping into a new domain, team, or challenge so try it on for size even if you’re not onboarding.

Pillar #1: The People

Product work is people work. More than roadmaps and releases—it’s negotiation, influence, and often politics. Your success as a Product Leader depends on cross-functional alignment, collaborative execution, and a shared understanding of goals.

Relationships are your leverage. Culture is crucial. Understanding both is your first real win.

In the end, it’s your connections that will make or break your impact. In the first 90 days, your top priority is to build trust and gather context.

To do that, prioritize these people-centered tasks:

• Get to know your team

• Establish alliances with key stakeholders

• Build a personal board of directors

Get to know your team

You can't lead a team you don't know. Approach your new team with authentic curiosity, maintaining neutrality as you absorb their history and context. You'll have plenty of time to evaluate them later. Now is the time to listen and learn.

As a new leader, you’ll often come into a volatile situation. Individuals will have a range of responses – from enthusiastic to resistant. They will be particularly sensitive to leaders who come in with baggage – preconceived ideas of the right way and the wrong way to do things. It’s important to navigate this terrain with both confidence and sensitivity.

These candid, open conversations will help you accurately assess the team's current state and build the relationships you’ll need.

Consider asking team members:

• What’s working well? What do we need to preserve as we seek to grow?

• What’s not working? Where should we focus on developing?

Consider asking yourself:

• What’s the team’s trajectory? Are they set up to succeed?

• Where are the strengths and gaps across skills, experience, and confidence?

• What is the team not telling me but showing me?

Uncover the informal power dynamics, decision loops, and trusted voices within the team. Aside from 1:1 conversations, this can be done by attending meetings and reading cues.

And most importantly, make yourself available and visible to your new team.

Establish alliances with key stakeholders

Product is cross-functional by definition. In your first few weeks, map out the ecosystem of stakeholders who influence your work. Just like with your team conversations, approach these interactions with curiosity and an open mind.

As a new leader, you have a unique opportunity to position yourself as more than just a product team advocate. You can serve as a bridge-builder focused on helping the entire company work better together. This neutral stance is powerful for identifying and repairing strained relationships between departments that might be limiting your team's effectiveness.

Begin by connecting with key partners outside your immediate team: marketing, sales, customer support, research, data, and finance. Later, expand to others like legal, trust & safety, and operations—these groups often have valuable product insights that may have been overlooked.

Consider asking stakeholders:

• What are your biggest goals this year? What’s getting in your way?

• What does the product team do well today? What do we need to improve?

• How do you work with the product org - what’s working well and what could be improved?

• Who else should I be talking to as a key stakeholder?

Consider asking yourself:

• What incentives do this stakeholder and their team have?

• Are this person’s goals similar to my team’s goals?

After these initial conversations, establish regular check-ins. As you gain a deeper understanding of the organization, you'll discover new questions that need exploring with these key partners.

Build a personal board of directors

When you start a new job, you’re often at the end of a long road of networking conversations. So it might seem counterintuitive to keep your foot on the networking pedal in your first 90 days. But, keeping your networking intention strong is one of the best things you can do.

How do you do it? A personal board of directors will give you the grounding and perspective you need as you get started in a new role. What’s a personal board of directors?

explains the Personal Board of Directors concept well and champions building your personal board:

“A personal board of directors has people you trust, a diversity of perspectives and brutal honesty and accountability” —

No matter how senior you are, you’ll need support. In fact, many leaders find they need more support as they get more senior and have fewer peers to rely on. Early on, start curating your personal “board of directors”—a mix of trusted leaders inside the org, mentors outside it, and coaches or peers who can help you see around corners.

Consider meeting with these folks on a regular basis to round out your perspective early.

Pillar #2: The Business

You can't make good product decisions without understanding the business context. Early on, your “outsider” perspective is actually your superpower. Jori experienced this first hand:

During my first week at a new job, a leader said, “I want you to think about how everything is working—how things operate around here. What’s working? What’s not? What could we be doing better?” At first, I was caught off guard. I was new to the industry so his request was surprising, but empowering.

Ultimately, he taught me the power of early impressions and fresh perspective as part of the onboarding process. The way I saw things in those first weeks was actually strategic input for him. It helped shape how he thought about the team, our systems, and the business overall. And it was something I carried with me to future roles and in leading future teams.

Understand the Product Strategy Stack

As a new product leader, one of your greatest assets is your fresh perspective. You’re not yet embedded in the way things have “always” been done, which means you’re more likely to notice where things don’t quite add up—whether that’s in strategy, execution, or communication.

Start by mapping your Product Strategy Stack:

In the first 90 days:

1. Get to know each layer of the Product Strategy Stack

2. Identify underlying assumptions that connect the layers

3. Assess the layers of the stack that you and your team own

As you examine each layer, consider looking for holes in the stack. Aside from assessing consistency with what you’re seeing and hearing, it’s on you as a leader to spot if teams feel empowered to build for their customers.

An important question to ask yourself: Do teams feel confident in how they’re contributing to the bigger picture?

Here are some of the most common gaps we’ve seen:

• A product team “taking orders” from the sales team or leadership

• A vision-less roadmap full of features that don’t tie back to a clear product strategy

• A lack of a cohesive customer strategy – vague customer profile or trying to be all things to all people

• Top line metrics that don’t clearly tie back to products or clear decision making

• Goal metrics that are trailing indicators, not levers the product team can pull

• Too much focus on revenue, too little focus on how customer value is created

Leverage your outsider’s eye, competitively

Now is also the time to challenge underlying business assumptions and look at competitors with fresh eyes. Asking basic questions and pushing team members to defend decisions made in the past is all fair game in the first 90 days.

As the “new person,” you have a limited-time window to ask powerful questions. Use it. Your perspective is clean, and you’re not yet weighed down by institutional knowledge or internal politics. When you receive muddy or unclear responses, chances are there are some gaps in strategy. You can use the Product Strategy Stack to debug those gaps.

Pillar #3: The Process

How an organization handles operations reveals what they value. The first 90 days is the optimal time to observe what's happening – to understand if it's working or not.

Now is not the time to change things or copy and paste your former processes onto your new team. Now is the time to take notes and collect data.

Dig into the product life cycle

Understanding how the product development process works today is a critical first step to improving and optimizing it.

Work on a single feature yourself or shadow a Product Manager through a feature journey. This hands-on approach will let you experience ideation, spec writing, design, and delivery firsthand.

Assess how team’s learn about customers and generate insights

Talking to customers and learning from experiments is a critical part of improving product decisions. You must understand how the team makes sense of these insights and incorporates them into product initiatives. Ask questions like, “What user research methods are used?”, “How often do teams talk to customers?”, “How does the team learn from its experiments or product launches?" and "What mechanisms does the team use to incorporate learnings into future product initiatives?"

Make it a priority in your first 90 days to personally observe customer interactions—whether through research sessions, support calls, or user interviews. If these opportunities are scarce, you've identified both a red flag and your first opportunity for impact. Deepening customer understanding is perhaps the highest-ROI investment a product team can make.

Map out collaboration

Identify how the product team collaborates, with whom, and how often. Ask about daily, weekly, or monthly communication cadences within and across teams to understand the rhythm of collaboration.

Learn how goals are set

Get a clear understanding of the process for setting goals and the specific metrics used. If you encounter a metric that is central to the company but you don't understand it, prioritize getting clarity on it.

Get feedback on what's working

Conduct a retrospective with your team to learn what's going well or poorly about the process. This will help broaden your understanding of the product development process beyond what you uncovered in your own assessment.

After understanding the current process, then you can decide where to rely on the system that is already in place and where improvements are needed. Prioritize fixes based on the ease to implement and the value it brings.

Get those early wins

As you gain the lay of the land – as you understand the People, Business, and Processes at play – you’ll build a perspective that is rare & fleeting. You’ll have the benefit of fresh eyes, but also an increasingly deep understanding of how the team works.

This is the perfect perspective from which to identify opportunities for early wins – the meaningful progress you can make today as you establish the foundation for tomorrow.

These early wins are critical – because they establish you as an effective, action-oriented leader who is able to move the needle. Every leader gets placed into a new role because of your potential—early wins transform the promise others saw in you into visible proof of value.

Earlier, we talked about the virtuous cycle you can create in your first 90 days. Early wins build trust which earns you the support & resources to deliver even bigger wins which earn even more trust.

So, early wins are critical to getting that flywheel turning. Often, leaders get tripped up by focusing on big wins rather than early wins. They wait too long to get the flywheel turning – and credibility erodes with each day that passes without visible progress. If your team and stakeholders can speak to your early value, you know you’re on the right track.

What does a good early win look like? It starts with reframing how you think about progress. Don’t get bogged down shipping product features. Instead, look for other ways to create impact.

Here are some ideas. Not all of these will apply to your role or your product, but they highlight the type of thinking necessary to achieve early wins.

You'll notice many of these ideas are hands-on, but that's okay—even for Product Leaders with a large team. Now’s the time to dive into the details. Getting granular, whether directly or through your team, sharpens your insight and sets you up for lasting success.

Customer experience improvements

• Run an A/B test on high-traffic pages to quickly validate messaging or design changes

• Analyze the top 5 customer support tickets and implement one quick fix that reduces volume

• Create a "quick wins" backlog highlighting small UX improvements with outsized impact

• Implement targeted onboarding improvements for a specific user segment with low activation

• Identify and fix the most common drop-off point in your conversion funnel

Revenue & retention optimization

• Use the CRM system to test a new campaign or refreshed copy on an existing segment

• Optimize your dunning strategy to reduce involuntary payment churn

• Use targeted email or push campaigns to re-engage recently churned users

• Use the merchandising system to highlight an in-demand or high-margin product

• Implement tactical pricing adjustments that don't require major development work

Process & productivity enhancements

• Map and remove a significant bottleneck in the team's development process

• Create a streamlined prioritization framework to reduce decision-making friction

• Improve meeting effectiveness by implementing a "decision-tracker" template

• Audit and consolidate your team's tools to eliminate redundancies

• Create templates for common deliverables (PRDs, specs, experiment briefs)

Team & stakeholder alignment

• Define and articulate a clear product vision that inspires the team

• Run a collaborative design sprint to solve a meaningful customer problem

• Rebuild a broken stakeholder relationship

• Implement a regular "voice of customer" review with key stakeholders

• Create a "skill-sharing" program where team members teach others their expertise

Technical & strategic focus

• Sunset a feature that's consuming resources without delivering value

• Conduct a technical debt audit and prioritize the highest ROI improvements

• Define success metrics for existing features that lack clear performance indicators

• Identify and delegate ownership of a neglected but promising product area to an emerging leader

Focus on early wins that tap into your strengths. If you’re great at facilitation, run a killer planning session. If you’re strong in research, synthesize insights that unlock new directions. If you see a zombie project dragging everyone down—end it. Even something as simple as improving meeting hygiene or fixing a small internal tool can show people you’re paying attention and making things better.

Remember that impact often comes from unexpected places. A thoughtfully redesigned meeting structure can reclaim hundreds of team hours annually. A simple fix to an internal tool might unlock productivity across departments. These seemingly small improvements demonstrate your attentiveness and commitment to making things better immediately, not just eventually.

For leaders taking on greater scope, sometimes your most valuable early contribution won't be what you add, but what you subtract. Having the discernment and confidence to descope an overly ambitious initiative can instantly reduce team stress, improve focus, and demonstrate your judgment – sending a powerful signal about your leadership approach from day one.

Keep calm and the momentum up

Above all else, don't forget to be patient.

Momentum doesn't always look like rapid movement. Sometimes, it's about creating space for deeper understanding to develop.

Because no matter how amazing your first 90 days are, sometimes it's just time that's needed for real roots to grow. The relationships you build, the insights you gather, and the changes you initiate all need room to flourish.

And it's quiet discipline—not rushing to "fix" everything—that ensures your quick actions are guided by long-term thinking. Trust the foundation you're building; the most meaningful impact often reveals itself after those first 90 days have passed, when your early wins evolve into lasting transformation.

Sound familiar? I've witnessed this scene play out across many companies, and it highlights a fundamental flaw in how we think about guiding product teams.

What is a North Star Metric?

A North Star Metric is “a single metric that best captures the value customers derive from your product”. Amplitude's North Star Playbook defines the approach:

"A team using the North Star Framework identifies a single, meaningful metric and a handful of contributing inputs. Product teams work to influence those inputs, which in turn drive the metric. The North Star is a leading indicator of sustainable growth and acts as a connective tissue between the product and the broader business."

This framework is powerful in its simplicity. It provides a clear target to aim for, aligns teams around a common goal, and offers an objective measure of success. When done well, it removes ambiguity and helps teams focus their efforts.

When does the North Star Metric fall short?

In my experience, it’s difficult and sometimes misguided to try to channel an entire team's efforts into a single metric. The North Star Metric approach suffers from a few critical problems:

One metric can’t rule them all

The North Star Metric approach assumes that everything valuable a team can do ladders up to the same metric — which provides an inflexible way of thinking about how to deliver value to the customer.

Remember, a good metric guides the team. It’s a leading indicator — a lever that the team can pull to create value. A good metric helps us understand trade-offs and focuses our work in a specific direction to achieve a particular outcome.

A good metric is an opinionated decision-making tool.

In some businesses, it's possible a single metric to be equally useful — as a decision making tool — for all teams. Amplitude is a good example: their product has a single overarching value proposition for users.

But other businesses aren't so straightforward.

For example, marketplace products face a unique challenge: they serve both suppliers and consumers, each with fundamentally different needs and success metrics. A single measurement fails to capture the progress required on both sides of the market. Rather than enhancing decision-making, a single metric can undermine a team’s ability to make clear, opinionated choices about where to focus their efforts.

There are other circumstances where a single metric won't work well:

• Products with multiple different modes of usage

• Products with user cohorts that have very different needs

• Companies with a portfolio of products

All of these resist boiling down everything a team can do to a single North Star Metric.

The curse of Goodhart’s Law

Second, it suffers from Goodhart's Law: when a measure becomes a target, it ceases to become a good measure. People optimize for the metric, rather than the value it's supposed to represent. They game the system.

Teams often optimize their North Star Metric in ways that prioritize short-term gains over strategic growth. I've seen product teams concentrate heavily on the U.S. market while neglecting international expansion, simply because domestic improvements move the metric faster. Similarly, many teams focus on increasing revenue through price increases for existing customers rather than working to expand their market share. Both approaches may improve the metric today, but at the expense of building a stronger foundation for tomorrow.

The North Star Strategy approach

These limitations of North Star Metrics reveal a fundamental problem: we've been focusing on the wrong kind of north star altogether. While a single metric provides simplicity, it lacks the context and flexibility teams need to navigate complex product decisions. What if instead of anchoring teams to a number, we anchored them to a story that gives meaning to their work?

Instead of fixating on a single metric, product teams should express their goals as a strategic narrative — what's the couple of sentences that describe what we're trying to achieve and why?

From there, the team can define the handful of metrics that are the evidence that the team is making progress on that strategic narrative. This approach focuses on the strategic thinking, not just the number — and it gives the team the responsibility and flexibility of solving for the true nature of the problem, not just hitting a quantitative goal.

Vision over metrics — the biggest successes often go against the metrics

The biggest product successes often come from decisions made based on vision, not metrics — in fact, the most vision-aligned strategies often result in near-term risk or cannibalization of metrics.

Apple put iPod revenue at risk when they launched the iPhone, a high-priced cell phone that bucked the conventional wisdom that powerful, expensive phones needed to have an integrated keyboard. Had they been guided purely by a North Star Metric of iPod sales or music revenue, they might never have risked the category-defining move that transformed their entire business.

Netflix risked cannibalizing their business when they turned away from rentals to streaming — especially when Netflix Originals put them at odds with the studios they depended on. A North Star Metric of subscriber growth or retention wouldn't have captured the strategic vision that led them to fundamentally reinvent their business model.

Facebook had to jettison a promising social app/platform strategy when they embraced mobile as their future. The shift to mobile wasn't a metrics-driven decision — it was a strategic imperative driven by where the world was heading and how Facebook needed to position itself to remain relevant.

A real-world example from Tinder

At Tinder, we went through a North Star Metric exercise: what one metric should the product team use to evaluate impact? At first, matches seemed like the right metric. But, there are good matches (that result in conversations) and bad matches (that get ghosted).

Maybe active conversations? This seemed like a good metric for some teams, but it was a terrible metric for the trust & safety team — scammers are very good at starting conversations when left unchecked.

We searched some more. By the time we were done, our ideal North Star Metric was so generic that it no longer served any useful purpose for guiding the team.

Tinder has a very clear North Star Strategy — make single life more fun by helping people match and talk with the people they want to meet. But, there is no single metric that can measure that strategy. Each team needs to define their work relative to that strategy, and each team needs different metrics to measure that progress.

We needed to ladder up to a single strategy, not a single metric. We needed to align our efforts with logic, not math.

This is where a North Star Strategy — a story of what the company is trying to achieve — becomes crucial. It serves as the guiding light that helps people make trade-offs and understand whether they're heading in the right direction.

Let's remember that the "North Star" concept is all about a guiding light. It's about understanding if you're going in the right direction or the wrong direction. In some cases, a single overarching metric can help. But in many cases, it can't — in fact, trying to fit all valuable work into a single metric can send teams in the wrong direction. That's where strategy becomes incredibly important as a way to provide alignment and clarity.

North Star Strategies succeed where North Star Metrics fail because they provide context and meaning, not just a number to hit. They tell the team what mountain they're climbing and why, not just how high they need to go.

What’s your take?

Do you find North Star Metrics helpful, or do you prefer a more strategic approach? Has your team struggled with finding the "perfect" metric? I'd love to hear your experiences and perspectives.

"What happened?" I asked.

She sighed and said, "I don’t know exactly… it just didn’t feel right. Everything I tried hit resistance. I didn't see it until I was in it.”

Her story isn't unique. I've watched brilliant people join companies only to leave shortly after because of cultural misalignment. These departures are costly for everyone involved — for the promising leader who loses time and confidence, and for the company that invests in recruiting and onboarding.

But how do you evaluate something as seemingly abstract as culture? Most companies talk about their values in similar ways: innovation, customer-focus, integrity. Yet we know that companies with identical stated values can operate completely differently.

I've found one question that brings culture into sharp focus:

"All things being equal… what isn't?"

This single question — what I call the Culture Litmus Test — can reveal more about a company's priorities and decision-making process than hours of traditional interviews.

Why culture matters

Most people think about their career as a ladder — a ladder that they climb, rung-by-rung, entirely on their own. A better metaphor is to think of your career as an “escalator”. Factors outside of your own efforts can help carry you towards your goals or work against you.

In “Impact = Environment x Skills: How to Make Career Decisions”, Bangaly Kaba describes how your environment dramatically influences your ability to make an impact. Your environment sets the constraints and opportunities through which your skills translate to impact.

There's another word for the system you're operating within — culture.

Culture isn't just about free snacks or whether people socialize outside work. It's the largely unwritten rules about how decisions get made. It's what gets prioritized when everything can't be. It's how conflicts get resolved. It determines whether you'll thrive or merely survive in a role.

When you're swimming with the current of a culture, work feels energizing and meaningful. When you're swimming against it, even small tasks become exhausting.

The Culture Litmus Test, explained

The beauty of "All things being equal, what isn't?" is its simplicity. The question forces clarity by asking what actually takes priority when tough decisions need to be made.

This question is easy to answer for companies with a strong culture. At Amazon, when you ask: "All things being equal, what isn't?" the answer is resoundingly clear: the customer. Amazon's first and most important leadership principle is Customer Obsession. Amazon says, "Leaders start with the customer and work backwards."

Customer Obsession is why Amazon will try seemingly crazy ideas, like the Treasure Truck. It's also why they keep iterating on failed ideas, like the Fire Phone, to eventually home in on transformational ideas like Alexa.

This question is equally easy to answer for Apple. "All things being equal, what isn't?" Design. Apple’s culture isn’t solely about what customers say they want; it’s about envisioning and designing an unparalleled user experience. When faced with tough decisions, Apple prioritizes a seamless fusion of hardware, software, and services. This commitment to integrative design often leads to choices that might seem counterintuitive from a pure customer-obsession standpoint, yet are essential in building a cohesive and elegant product ecosystem.

Let's look at a few more examples:

• All things being equal at Google, what isn't? Information. Google prioritizes initiatives that aggregate, understand, and harness the power of information.

• All things being equal at Booking.com, what isn't? Bookings. Put another way, Booking.com prioritizes giving travelers the confidence to book. That means leading on prices and having the most accurate/comprehensive metadata about accommodations.

• All things being equal at Meta, what isn't? Time spent. Facebook looks entirely different over time — morphing from year to year based on where users are spending their time. Today, Facebook is video-centric — not because video is central to Facebook’s mission “to give people the power to build community and bring the world closer together”, but because it drives significant minutes watched.

The power of the Culture Litmus Test is that it cuts through aspirational values to reveal operational values — not what the company says it values, but what it actually optimizes for in practice.

Applying the test

How do you use this test effectively when evaluating a potential role, team, or company?

Start by asking the question directly. "If you had to make a tough decision and all things seemed equal, what factor would break the tie?" Follow up with: "Can you give me an example of when this happened recently?" Ask about a recent difficult decision where two options seemed equal. How did they decide? What ultimately tipped the scales?

Listen carefully to the answers you get from different people in the organization. Consistency indicates a strong, clear culture. Inconsistency suggests either a weak culture or one in transition.

Here are some patterns to watch for:

Green flags:

• Consistent answers across different levels and departments

• Concrete examples that demonstrate the priority in action

• Values that align with sustainable business practices

• Answers that resonate with your personal values

Red flags:

• Vague, inconsistent responses

• Examples that contradict the stated value

• "Quarterly revenue" as the primary tiebreaker

• Different answers from leadership versus individual contributors

Building your own cultural clarity

If you're a leader, the Culture Litmus Test isn't just for evaluating potential employers — it's a powerful tool for strengthening your own team's culture.

Start by asking yourself: When you and your team face tradeoffs, what consistently wins? Is it customer delight? Innovation? Speed? Quality? This isn't about what you wish would win, but what actually does in practice.

Then ask your team the same question. If you get inconsistent answers, you have an opportunity to create clarity. Remember: if the answer isn't clear, then the culture isn't clear. This often leads to decision paralysis, inconsistent prioritization, and team frustration.

The most effective leaders are explicit about their tiebreakers. They communicate clearly what wins when all things seem equal. This clarity helps teams make aligned decisions independently, which is the hallmark of a scaled organization.

REI provides a powerful example. Their Culture Litmus Test answer is clear: environmental stewardship. It's why they launched their "Opt Outside" campaign, closing stores on Black Friday and encouraging employees and customers to enjoy the outdoors instead of shopping. This clear prioritization attracts employees and customers who share these values, creating a virtuous cycle.

REI is willing to trade off the biggest day for retail sales to send a clear signal about its culture and values.

Revenue is a byproduct, not a cultural value

What's the most common failure mode for culture? It's when the answer is “revenue”.

Of course, every company needs to generate revenue. However, strong cultures see revenue as a byproduct of how they understand the world — of the trade-offs they're willing to make to create value. When "revenue" is the primary tiebreaker, it's an early warning signal. The strongest companies optimize for something that drives sustainable revenue as a byproduct, not revenue directly.

Companies preoccupied by revenue will likely over-focus on the short-term at the expense of long-term value. Amazon doesn't optimize for quarterly profits; they optimize for customer experience, knowing that delighted customers come back to buy, over and over again.

A revenue-first culture doesn't mean a company is doomed, but it suggests cultural challenges that will impact your experience there. Teams in these environments often find themselves constantly shifting priorities, chasing short-term wins, and sacrificing what they believe is right for what will immediately boost numbers.

From question to action

Next time you're evaluating a workplace culture—whether in your current role or considering a new one—deploy the Culture Litmus Test. Simply ask: "All things being equal, what isn't?"

This deceptively simple question reveals operational truth rather than aspirational values. It uncovers what actually happens when tough decisions need to be made.

For job seekers, this test can prevent the painful experience of my colleague who left her "dream job" after just eight months. It's a tool to see beyond recruiter talking points and empty value statements.

For leaders, it offers an opportunity to create the clarity your team craves. When everyone understands what truly matters, decisions become faster, more consistent, and require less escalation.

Culture isn't just a list of values on a wall—it's the invisible current that either propels your career forward or forces you to swim upstream every day. The Culture Litmus Test helps you see that current before you dive in, so you can find or build an environment where your work feels meaningful and your impact is multiplied.

In a world where companies say “everything is important”, knowing what isn't equal might be your most valuable career insight.

What's your experience with cultural fit? Have you used similar approaches to evaluate a company's true priorities? I'd love to hear your thoughts in the comments.