AI Product Strategy

Who this is for

Product and engineering leaders at companies between Series A and growth stage who have shipped at least one AI feature, or who are about to, and have realised the prototype-to-production gap is bigger than the demo suggested. Usually the trigger is a customer who has noticed the feature is unreliable, an internal team trying to use it for something it wasn’t designed for, or a board question about the AI roadmap.

The problem

LLM-backed product features look easy until they meet real users and real data. The model that worked in the demo hallucinates against the actual document corpus. There’s no agreed definition of “good enough” output, so QA is permanent. Cost per request is unpredictable. There’s no evaluation harness, so every prompt change is a gamble. And the product team and the engineering team disagree on whether the AI roadmap is bound by what the model can do or what the customer asks for.

This is the same product problem as any other feature shipping — but the failure modes are unfamiliar, the tooling is immature, and most of the writing on the topic is either marketing or research. The sprint builds an operating model around AI features that lets your team move with the same confidence they have on any other part of the product.

What you get

• AI product strategy aligned to the business roadmap — what to build, what to integrate, what to leave alone
• Use-case prioritisation framework with cost, risk, and impact scoring
• Evaluation harness design — golden datasets, automated eval pipelines, human-in-the-loop review thresholds
• Model and provider selection guidance for the use cases in scope (frontier model, open-weight, hybrid)
• Reference architecture for retrieval-augmented generation, agentic workflows, or fine-tuning patterns as needed
• Cost monitoring and unit economics model — cost per user, cost per request, projection at scale
• Safety and policy framework — prompt injection, PII handling, model output review, customer transparency
• Roadmap and team-shape recommendations for the next 12 months

How it works

8–12 weeks
Weeks 1–2	discovery, use-case mapping, stakeholder alignment
Weeks 3–5	strategy, prioritisation, evaluation framework design
Weeks 6–8	reference architecture, cost model, safety framework
Weeks 9–10	first use-case implementation pilot (if in scope)
Weeks 11–12	roadmap, team-shape recommendations, handover

Duration depends on the number of use cases in scope and whether a live pilot is part of the engagement.

Companion to AI Adoption Strategy

This program is the product-and-engineering counterpart to the AI Adoption Strategy program. Where Adoption is about how your people use AI internally and the cultural change that comes with it, Product Strategy is about how AI shows up in what you sell. The two often run in parallel — sequenced so the internal capability lands before the external feature does.