AI Product Strategy & Adoption
Where AI belongs — and where it does not.
The challenge is rarely access to models — it is identifying where AI creates meaningful customer value, integrating it into existing workflows, and measuring outcomes that matter. We help leadership teams decide where AI belongs, where it does not, and how to move from experimentation to execution.
In short
AI Product Strategy Advisory is a 4–12 week engagement for founders and product leaders who need to decide where AI belongs on the roadmap — and where it doesn't. We run an opportunity assessment, prioritise AI bets against real customer workflows, recommend build vs buy vs partner for each, and hand back an adoption plan tied to business metrics. Independent of model vendors, cloud providers, and system integrators.
- Duration
- 4–12 weeks
- Best fit
- Founders, CPOs, Heads of AI Product
- Independence
- No model, cloud or SI affiliations
Why AI initiatives stall
The constraint is rarely the technology.
Unclear Business Value
Teams invest in AI before identifying a meaningful customer or business problem.
AI Before Workflow
Technology is introduced before understanding how users actually work.
Experimentation Without Direction
Proofs of concept accumulate while product strategy remains unchanged.
Success Is Undefined
Organisations struggle to measure whether AI is improving outcomes or simply increasing activity.
Questions we help answer
The decisions leadership teams bring to us.
01
Where can AI create meaningful value?
Identify opportunities that improve customer outcomes, operational effectiveness or competitive advantage.
02
Should we build, buy or partner?
Evaluate strategic options based on capability, speed, risk and long-term flexibility.
03
How should AI fit into our roadmap?
Prioritise initiatives that align with customer needs and business objectives.
04
What should success look like?
Define adoption, outcome and business metrics before investing further.
05
How should product and engineering teams work together?
Create operating models that support experimentation without creating organisational chaos.
06
What capabilities do we need internally?
Determine where expertise should be built, acquired or accessed through partners.
Areas of focus
Where the work tends to land.
AI Product Strategy
Identifying opportunities where AI creates measurable value.
AI Opportunity Assessment
Separating meaningful use cases from technology-driven experimentation.
AI Roadmap Development
Aligning AI initiatives with business priorities and customer outcomes.
LLM Product Design
Designing experiences that incorporate large language models responsibly and effectively.
AI Adoption Planning
Supporting organisational readiness, governance and change management.
AI Product Leadership
Helping leadership teams make informed investment and prioritisation decisions.
Relevant domains
Where AI decisions carry commercial weight.
Experience working with technology businesses where AI decisions carry meaningful commercial and operational consequences.
What we believe
A few beliefs that shape the work.
AI is not a strategy.
Most AI problems are workflow problems.
Adoption matters more than demonstrations.
Technology should follow customer value.
Experimentation requires clear success criteria.
The goal is outcomes, not AI features.
How the engagement runs
Four to twelve weeks. Three phases. One prioritised AI direction.
Weeks 1–3
Opportunity assessment
Workflow interviews, competitive scan, review of existing PoCs. We map where AI can plausibly move a metric — and where it can't — separating customer-value use cases from technology-driven experiments.
Weeks 4–8
Prioritisation & build/buy/partner
For each candidate bet: sizing, feasibility, data readiness, and a build vs buy vs partner recommendation. Trade-offs are made explicit — capacity, cost, and time-to-value.
Weeks 9–12
Roadmap & adoption plan
A sequenced AI roadmap tied to business metrics, an operating-model recommendation for how product and engineering collaborate, and an adoption plan covering workflow integration, success metrics, and change management.
Frequently asked
AI product strategy — answered.
- What is AI product strategy advisory?
- A time-boxed engagement that helps leadership teams decide where AI creates real customer value, how to sequence AI initiatives on the roadmap, and how to measure adoption and outcomes rather than model demos.
- When should a company hire an AI advisor?
- When AI proofs of concept are accumulating without shipping, when leadership can't decide build vs buy vs partner, when an AI feature has been shipped but usage is flat, or before committing to a large model or platform investment.
- How long is a typical engagement?
- Opportunity assessments run 4–6 weeks. Full AI product strategy and roadmap engagements run 8–12 weeks. Adoption programmes are scoped per initiative.
- Do you build AI models or write code?
- No. We are an independent product and strategy advisory. We help leadership teams make the right decisions and design the operating model; delivery stays with your engineering team or a chosen build partner.
- What is the deliverable?
- A written AI opportunity map, a prioritised AI roadmap tied to business metrics, a build-vs-buy-vs-partner recommendation per bet, and an adoption plan covering workflow integration, success metrics, and change management.
- Which industries do you work with?
- SaaS, fintech, gaming, enterprise software, knowledge platforms, and developer tools — technology businesses where AI decisions carry meaningful commercial consequences.
AI is becoming easier to access.
Making the right decisions about it is not.
Before investing in another proof of concept, expanding an AI team or redesigning your roadmap, get an independent perspective.
Product · Resource
Product Strategy Diagnostic
Twelve questions used in advisory engagements to surface where product strategy is breaking down before it shows up in revenue.