I'm excited to share this guest post from the product team at Productboard. As they've evolved their platform around AI (first with Pulse, now with Spark), they've accumulated hard-won lessons about building AI-powered products customers love. Two insights stood out to me: first, their distinction between guiding and automating (users say they want AI to do tasks for them, but the real value is in AI that helps them think better). Second, their framing of AI quality as a "third dimension" that breaks traditional product development: you might build the UI and backend in two weeks, then spend two months iterating on AI quality. It's one of the more practical pieces I've seen on building AI that consistently delivers.
Building AI products: Lessons from Productboard Spark
A detailed look at how one team navigated the hard calls: figuring out where AI actually fits, shipping ahead of customers, and building quality through rigorous evaluation & iteration.
