AI Product Strategy Consultant: Where AI Belongs in Your Product

A sequenced answer to where AI pays: which user problems in your product are prediction-shaped or generation-shaped, what evidence says users will adopt the assist, what the metric target is, and what to explicitly not build. The deliverable is a roadmap position, a defensible one, not a model recommendation.

The published example: "TouchStay", a TravelTech SaaS, where users stalled at content creation during onboarding. The AI answer was narrow, AI-assisted setup at the stall point, not a chatbot everywhere. Over two quarters onboarding completion moved from 52% to 81% and trial-to-paid from 35% to 49%. The strategy was choosing the stall point; the model was the easy part.

For where AI belongs, a product strategist with AI delivery experience beats a model specialist: the hard decisions are user-problem selection, adoption evidence and sequencing, classic strategy work. You need model specialists later, at build time, for architecture, evaluation and cost. Buying them in the wrong order produces beautiful demos.

The interview test: ask a candidate to name an AI feature they refused to build and why. A strategist has a story with a metric in it; a specialist has a story about capability. Both are professionals; only one of them should be deciding your roadmap.

As one thread, not a separate track. In my engagements, $4,950 a month for 25 hours, AI opportunities pass through the same evidence engine as everything else: problem, evidence, metric, sequence. That discipline is exactly what AI pressure erodes, and exactly what restores it.

Four artifacts. A map of your product's prediction-shaped and generation-shaped problems. An evidence read on which of them users would adopt an assist for. A sequenced roadmap position with a metric target per item. And an explicit not-building list, which is usually the most valuable page of the four.

The not-building list earns its place in board meetings. AI pressure arrives as a stream of plausible features, and a documented reason against each one that failed the metric test converts recurring debates into a one-line answer. The list also ages well: when a vetoed feature becomes viable, the written reason tells you exactly what changed.

Every proposed AI feature must name the number it should move and the date you will check. In the published "TouchStay" case, the number was onboarding completion, stuck at 52%; the feature was AI-assisted setup at the exact stall point; two quarters later the number read 81% and trial-to-paid had followed from 35% to 49%.

Notice what the test filtered out in that engagement: a general-purpose chatbot, which could not name its number, and broad content generation, which named one nobody would report to a board. The test is not anti-ambition; it is pro-accounting. Features that survive it ship with their own scoreboard attached.