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GeneralMicrosoft Copilot4 min read

How to Run an AI Workshop for Your Senior Leadership Team

A well-run AI workshop for a senior leadership team achieves something that individual onboarding cannot: it creates shared language, shared reference points, and a collective decision about how the organisation will approach AI. The workshop format that works for leadership teams is different from corporate training: it is designed for busy, sceptical, experienced professionals who will disengage if it feels like a product demo or a technology training session. This guide covers the format and facilitation approach that consistently works.

01Setting up for success: objectives and attendees

Define clear objectives before designing the session. Common objectives for a senior leadership team AI workshop: (1) give all leaders direct experience of AI tools so they can make informed decisions about adoption; (2) identify the two or three highest-value use cases for AI in the organisation's specific context; (3) agree on the governance framework for AI use by the leadership team; (4) identify the leaders who will be AI champions and those who will need specific support.

Invite the full senior leadership team including, critically, the sceptics. The leaders who are most resistant to AI adoption are often the ones whose engagement matters most for cultural change. Frame the workshop as a thinking session rather than a training session: 'We are here to think together about what AI means for us' rather than 'We are here to learn how to use AI tools.'

Half a day (3-4 hours) is the right length. Less time does not allow for meaningful exploration; more time is difficult to justify in a senior leadership team calendar.

02Session structure that works

Opening (30 minutes): honest framing of where AI capability actually is. Not hype, not dismissal. Show specific examples of what AI can do in 60 seconds that would take 30 minutes manually: summarise a document, draft a board paper section, analyse a competitor. Use examples relevant to the specific sector and roles in the room.

Hands-on exploration (90 minutes): every participant tries AI on their own real tasks. Provide tablets or laptops with enterprise AI tools configured. Give a simple prompt structure to start with. Have an AI-experienced facilitator available to help participants who get stuck. The rule: participants try AI on actual work they need to do, not contrived scenarios.

Small group discussion (30 minutes): what surprised you? Where did AI perform better or worse than expected? What use cases emerged that are relevant to your role?

Strategic discussion (45 minutes): facilitated conversation about the organisation's AI approach. What are the two or three highest-value use cases to prioritise? What governance do we need in place? What should change in our operating model?

Commitments and next steps (15 minutes): each participant commits to three specific AI tasks they will try in the next two weeks. Agree on the governance and deployment decisions that need to follow.

03Facilitation principles

The facilitator's most important job is managing the sceptics without dismissing them. Sceptical leaders often raise the most important questions: about reliability, about data governance, about what happens when AI gets things wrong. These questions deserve substantive answers, not reassurance.

Avoid product demo mode: the worst AI workshops are those that spend most of the time watching an expert demonstrate AI capabilities. Leaders need to be in the driver's seat, not the audience.

Acknowledge what AI cannot do. Leaders lose trust in the process immediately if they sense they are being sold rather than informed. Being honest about AI's limitations actually increases the credibility of the positive case.

Make the governance conversation practical, not theoretical. 'What data should and should not go into Copilot?' is a better governance discussion starter than 'Let's talk about our AI ethics principles.' Practical questions produce concrete commitments.

04After the workshop

The workshop is the start of a change process, not the end of one. Follow-up actions that determine whether the workshop produces lasting change:

Capture and distribute the AI use cases identified as highest-priority, with nominated owners for each.

Establish the governance commitments: which AI tools are approved, what data governance rules apply, who has oversight of AI adoption.

Create a mechanism for sharing what works: a shared prompt library, a regular AI update slot in leadership team meetings, or a designated AI champion role.

Check in at 30 and 60 days: are the commitments being followed? Are the nominated use case owners making progress? What barriers have emerged that need to be addressed?

The half-day workshop creates momentum; the follow-through converts momentum into durable organisational capability.

Key Takeaways

  • 1.Frame the workshop as a thinking session, not a training session; 'what does AI mean for us' rather than 'how to use AI tools' produces more genuine engagement.
  • 2.Hands-on exploration on real tasks (not contrived scenarios) is the most important element; watching demonstrations does not produce the calibrated first-hand reference point needed.
  • 3.Manage sceptics by taking their questions seriously and providing substantive answers; dismissing scepticism loses the leaders whose engagement matters most.
  • 4.Make governance discussions practical ('what data should not go into Copilot?') rather than theoretical ('AI ethics principles') to produce concrete commitments.
  • 5.The workshop creates momentum; 30 and 60-day follow-up, shared prompt libraries, and nominated use case owners convert momentum into durable capability.

References & Further Reading

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