AIReadiness
For teams adopting AI tools faster than they are building the judgment to use them well.
Fig. 01 — the practice
The premisebehind the work.
Most organisations hand their teams AI tools and hope for the best. The result is confident, fluent, occasionally wrong output reaching clients and leadership. This programme builds the human layer around the tools — deciding what AI should touch, instructing it precisely, and evaluating what comes back.
Four pillars.Practised.
- 01
Task selection — which work suits AI assistance, and which demands human judgment
- 02
Instruction design — writing briefs and prompts that produce usable output
- 03
Output evaluation — spotting confident errors, gaps and hallucinated detail
- 04
Responsible use — confidentiality, attribution, and when AI use must be disclosed
“Scenario-based practice on your team's real tasks. Every exercise ends with an evaluation pass — not just a generated answer.”
How we checkit landed.
- 01
Participants review a flawed AI-generated client update and flag every claim that needs verification
- 02
Teams score real outputs against a shared review checklist — before the programme and after
- 03
Scenario judgment tests: five live tasks, and participants must defend which ones should go to AI at all
What changes.After.
Teams that direct AI instead of deferring to it
A shared standard for checking AI output before it ships
Fewer confident mistakes reaching clients and leadership
Next program
AI Workflow Design & AutomationStart a discovery conversation
One business problem is enough to begin.
Tell us where your people need to perform differently. We will work with you to define the audience, target behaviour, programme scope and evidence of success.