Turn AI opportunities into decisions you can defend

01From direction to evidence

The sequence is a decision loop, not a one-off project. New evidence can send an initiative back to an earlier step.

1. 01

   Discover

   Find relevant AI opportunities in business capabilities, processes and actual work.

2. 02

   Understand

   Describe what changes in the workflow, who is affected and where human judgement remains.

3. 03

   Assess

   Test value, readiness, feasibility, risk and decision confidence separately.

4. 04

   Prioritise

   Compare opportunities on the same dimensions and choose what deserves attention now.

5. 05

   Experiment

   Run a bounded test with a baseline, acceptance criteria, cost ceiling and stop rules.

6. 06

   Measure

   Record what changed, how it was measured and what remains uncertain.

7. 07

   Decide

   Continue, change, scale, defer or stop with a visible basis and a named owner.

03Build the capability one decision at a time

The structure is deliberately modular. More topics can be added without changing the user journey or turning the navigation into a catalogue.

1. 01From business strategy to AI opportunitiesTranslate priorities and process friction into a focused map of AI opportunities.
2. 02AI opportunity prioritisationCompare opportunities on value, feasibility, effort, risk and evidence without collapsing the trade-offs.
3. 03AI readinessAssess readiness for a specific scenario across process, data, systems, people, security and governance.
4. 04Experiment and validationDesign a bounded test that can disprove the value hypothesis before production investment.
5. 05Measuring AI valueConnect AI activity to measurable changes in revenue, cost, productivity, quality, experience or risk.
6. 06Human and AI adoptionDesign the future workflow, responsibilities and controls around real work rather than tool access.
7. 07Build, buy, configure or reuseChoose the solution path against business requirements, constraints and operating responsibility.
8. 08AI portfolio and roadmapBalance exploration, delivery capacity, dependencies and evidence across multiple initiatives.
9. 09From prototype to productionSet the evidence and operating threshold an experiment must meet before broader use.
10. 10AI operating model and skillsDefine ownership, decision rights, lifecycle controls and the capabilities required around the portfolio.
11. 11Scenario or evidenceTell a possible use of AI apart from a claim, a reported implementation and a measured result before you rely on it.
12. 12Reading an AI scenarioUse the ten questions every scenario answers to judge whether it is worth investigating in your own work.