Your Product’s Biggest Discovery Problem Isn’t SEO. It’s What the AI Says About You.

Something has shifted in how people find products, and most product managers haven’t caught up to it yet. A growing number of potential users aren’t starting with a Google search or a colleague’s recommendation. They’re opening an AI assistant and typing something like: what’s the best tool for managing cross-functional projects? The AI gives them a list. If your product isn’t on it or worse, if it’s described incorrectly, you have a discovery problem that no amount of traditional marketing spend will fix, because the person who needed you never knew you existed.

I started paying attention to this when I noticed that AI systems were describing tools I knew well in ways that were vague, outdated, or subtly wrong. Not dramatically wrong; just wrong enough that someone evaluating options would form a slightly inaccurate picture of what the product actually did. The product itself was strong. Its explainability to machines was not. And I started wondering how much of the discovery gap that teams puzzle, over the qualified leads that never showed up, the demos that never got booked was quietly rooted in this problem rather than anything they could control through traditional channels.

Product explainability is emerging as a real discipline, and it’s worth understanding what it actually requires. At its core, it means ensuring your product communicates its purpose, value, behaviour, and limitations in a form that both humans and AI systems can accurately represent. That means structured metadata, clean and consistent documentation, logical naming conventions, and a knowledge base that isn’t scattered across five different marketing subdomains and a two-year-old help centre that nobody’s reviewed since launch. If an AI system has to piece together what your product does from a fragmented information architecture, it will. The picture it constructs probably won’t be the one you’d choose.

What fascinates me about this shift is how it changes the product manager’s frame of reference. We’ve always obsessed over the user journey, how does a human discover, evaluate, and adopt the product? Now there’s a machine layer sitting in front of that journey that we also need to design for. How does an AI system discover, evaluate, and represent the product to the person who asked about it? These are two different audiences with genuinely different needs. Designing for only one of them is starting to look like a strategic blind spot.

The products that win in this environment will be the ones that are legible, not just usable. They’ll have clean, structured, machine-readable information architectures built with the same care as their onboarding flows or pricing pages. The PMs who get there early will treat discoverability by AI as a core part of their go-to-market strategy. The ones who wait will keep building excellent products that nobody’s AI assistant ever recommends and keep blaming the algorithm for something that was actually a content and structure problem all along.