Build capability through an AI Capability Dojo

Use coached, bounded work to develop independent AI judgement, effective intervention and learning that other teams can reuse.

Build AI capability through practice in real work. Connect role-based learning, human judgment and feedback to the wider AIDIVE transformation framework.

Make learning observable

AIDIVE's AI Capability Dojo is a coached environment where cross-functional teams practise discovery, experimentation, evaluation and human–AI work design on real cases. It can also develop the skills needed to supervise single-agent or multi-agent work.

The starting point is a consequential but bounded slice of work, a learning question and an explicit quality bar. A value-stream team owns the problem, supported by a capability coach and the relevant steward, champion, product, platform, risk or experience roles. Safe conditions and protected capacity make practice possible.

Practise the difficult decisions

Short cycles move through discovery, design, testing, evaluation and reflection. Successful use is one part of the practice. Teams also learn when to accept, edit, reject, override, intervene or choose appropriate non-use. Observe capability and confidence alongside workflow friction, cognitive load and supervision demands.

Completion means the team can demonstrate the targeted behaviour independently. The learning should also travel: publish a reusable pattern, an anti-pattern and the next capability gap, then test transfer into live work. A demonstration or attendance record alone cannot establish that transfer.

Illustrative example: a service response

A service team practises drafting responses using representative cases in an approved environment. Before generating text, it defines required facts, policy boundaries and escalation conditions. Participants compare outputs with that bar and explain why they would accept, change or reject each response.

Include a case with incomplete evidence and one where no AI-assisted response should proceed. Observe whether team members recognise the limitation and take the right action without relying on the coach. Return to live work with a maintained pattern and review whether another team can use it independently.

• Choose a real, bounded problem and a clear learning question.
• Coach quality judgement, non-use and intervention alongside use.
• Verify independent behaviour, live-work transfer and reuse.

Take this into your next conversation.

AI capability becomes durable when people can judge and intervene independently, and their learning improves work beyond the original team.

Framework source

Editorial adaptation from the supplied AIDIVE Version 1 master documents.

• AIDIVE Values - Practices & Playbooks Version-1.docx · AR-V24 AI Capability Dojo & Practice-Based Learning; AR-V25 AI Communities of Practice & Knowledge Flow
• AIDIVE Values Dimension Version-1.docx · V.1.6 AI capability & skills development; V.4.8 Working with AI agents: supervision, delegation and accountability; V.4.9 Learning in the flow of work and team capability

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