Choose the right opportunity
Compare opportunities by the value of the work, readiness of the information, consequence of error, strength of ownership and ease of running a safe test.
Practical AI consultancy and team enablement
Use AI where it makes the work better.
You get help choosing a useful opportunity, designing a sensible test and building the ownership, information and human review it needs. No platform theatre. No assumption that AI is always the answer.
Move from “we should use AI” to one job worth doing well.
The useful question is rarely “Which AI tool should we buy?” It is “Which piece of work would benefit, what information does it depend on, what can go wrong and who owns the result?” Once those answers are visible, technology choices become easier to judge.
- Different people are already using AI with no shared boundaries.
- You have a long list of ideas but no way to choose the first one.
- A supplier demo looks impressive but the fit with real work is unclear.
- Your team needs confidence and practical guidance, not a generic awareness session.
- A prototype exists but nobody has defined review, ownership or adoption.
- The information required is scattered, sensitive or hard to trust.
What you can improve
Design the working system
Decide what AI may do, what a person must review, how exceptions are handled, where information comes from and what evidence will show whether the work improved.
Test at a useful scale
Run a bounded test with real users and realistic examples. Look for quality, effort, failure modes and unintended work-not only a successful demonstration.
Equip the people expected to use it
Give people practical guidance, examples and review habits that fit their work. Make ownership and escalation clear.
Typical engagement outputs
- An opportunity shortlist and one-page definition
- A workflow, prototype brief, guardrails and measure plan
- Test evidence, decisions and a recommendation to stop, change, extend or scale
- A working kit, enablement session, operating guide and handover
A practical sequence
Start with a real workflow.
Name the job, owner and useful outcome.
Make information and risk visible.
Check source quality, permissions, privacy and failure modes.
Design human review.
Keep judgement where consequence requires it.
Run a bounded test.
Use realistic examples and clear stop or change conditions.
Leave reusable capability.
Keep the canvas, examples, decisions, boundaries, measures and owner.
Ways to get help
- Use it: Complete the free AI Opportunity Canvas.
- Work it through: Use a Working Session to choose or define one opportunity.
- Build it in: Use a Capability Sprint for workflow design, prototyping, testing, enablement and handover.
Bring one piece of work, not an AI ambition statement.
Describe what people do now, where the effort or inconsistency sits and what a useful outcome would look like. That is enough to begin.