How AI is supercharging every phase of the PM lifecycle

AI is not just changing the products we build; it's reshaping how we build them. For product managers, this is less like a threat and more like rocket fuel for our capabilities.

It’s no secret… I think the death of Product Management is greatly exaggerated — but the job is going to change in profound ways. In this guest post, Christian Marek, VP of Product at Productboard, delves into how AI is going to change every aspect of PM, especially as AI agents become more powerful. We’re on the cusp of AI agents that can meaningful help in the most challenging areas of PM: product discovery and strategy.

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.