Business of Apps has published a guide aimed squarely at the gap between AI ambition and AI delivery: "Beyond the Hype: How to Build a Working AI Product Roadmap."

The piece addresses a problem most product teams now recognize. Roadmaps filled with AI initiatives are easy to write and hard to execute, and the failure pattern is familiar: a roadmap item labeled "add AI" with no defined user problem, no success metric, and no fallback when the model underperforms.

What does the guide actually cover?

The core argument of the title itself signals the approach: strip away the hype and treat AI capabilities as product features that must earn their place on the roadmap like anything else. That means:

• Anchoring every AI initiative to a concrete user problem, not to model availability.
• Defining what "working" means before build starts — the accuracy, latency or quality bar the feature must clear to ship.
• Sequencing work so the team validates the riskiest assumption first, typically whether the model can perform the task reliably enough for real users.

Where does this approach break down?

A hype-free roadmap process has its own failure modes worth naming. AI features carry uncertainty that traditional roadmap planning handles poorly: you often cannot estimate delivery dates because model behavior is discovered, not specified. Teams that force AI work into fixed-date quarterly commitments tend to either ship underperforming features to hit the date or slip silently.

The tradeoff is real. Treating AI items as experiment-driven bets buys realism but costs predictability, and stakeholders who need dates will push back. The practical middle ground most practitioners land on is a now-next-later structure for AI work, with explicit kill criteria for each bet.

Why this matters now

The volume of AI roadmap claims keeps rising while the number of shipped, retained AI features lags behind it. Guides like this one matter because they move the conversation from "should we have AI on the roadmap" to "what conditions make an AI feature worth building at all."

Expect roadmap practice to keep shifting in this direction: smaller bets, explicit quality thresholds, and evaluation work treated as first-class roadmap items rather than post-launch cleanup.