A practical path to AI adoption
Move from a business problem to a tested solution through seven stages. Each stage ends with evidence for the next decision.
- 01
Awareness & education
Build a shared understanding of what AI can do, where it fails and who remains responsible.
- 02
Readiness assessment
Check data, processes, people, technology and budget before committing to a solution.
- 03
Opportunity discovery
Find repetitive work worth improving before deciding whether AI is the right mechanism.
- 04
Prioritization & business case
Compare business value with total effort, then check whether the assumptions survive a conservative estimate.
- 05
Pilot / proof of value
Test one defined use case on representative work before committing to a broader rollout.
- 06
Implementation & integration
Turn an evidenced pilot into an owned service that fits existing systems and daily work.
- 07
Scale, govern & measure
Keep useful systems useful as data, models, costs and responsibilities change.
Start where you are
The stages are decision points, not a mandatory waterfall. Return to discovery if the data or pilot results change your assumptions. Give each stage a named owner.
Keep the pilot small
Choose one process, a representative evaluation set and clear failure limits. Scaling is a separate decision after evidence, not the default outcome of a demo.
Assess your starting point yourself, or work through it together.
Take the free assessment →