AI Product Strategy & Adoption
Most AI initiatives fail long before the technology becomes the constraint.
The challenge is rarely access to models.
The challenge is identifying where AI creates meaningful customer value, integrating it into existing workflows and measuring outcomes that matter.
We help founders, product leaders and executive teams make informed decisions about where AI belongs, where it does not and how to move from experimentation to execution.
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.
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.