Course Format and Delivery

Delivery Method: Instructor-led LiveOnline

About this course

Your team already uses AI. Some of them told you. Some did not. The deliverable looks the same either way, and that is the leadership problem. The risk is that AI use stays private, uneven and unexamined, while you remain accountable for work you can no longer see being made. The evidence cuts two ways. In a peer-reviewed study of 5,172 customer-support agents, AI assistance raised productivity by 15% on average. Less experienced workers improved in speed and quality. The most experienced saw small declines in quality. Separately, four preregistered experiments with 4,439 participants found that people who use AI at work expect to be judged as less competent, and the judgment is real.

This course covers how AI changes the work you assign, how to make AI use visible without penalizing it, how to review work when the draft is no longer evidence of effort, and how to develop junior staff when AI can do the tasks that used to build their judgment. The team is the unit of design. What you build is the operating model around it: norms, assignment, review gates, development paths and accountability that hold up when you are not in the room.

What you will learn

• Explain how AI changes the distribution of performance across a team, and who gains or loses

• Recognize the signals of hidden, uneven or unexamined AI use on a team

• Build a team AI working agreement that makes use visible without penalizing it

• Assign work by consequence, visibility of error and development value, not by speed alone

• Review AI-assisted work for premises and reasoning rather than polish

• Protect the development of junior staff when AI performs the tasks that built judgment

• Hold a direct conversation about over-reliance, under-use or undisclosed use

• Structure accountability so a named person owns every AI assisted output

• Explain to a client how your team used AI, and defend it

This course is great for

Engagement leaders and people managers who direct consulting teams, Managers and Senior Managers through Managing Directors, plus practice and talent leadership responsible for how the firm develops its people.

Topics Covered

1. Frame and Baseline.  Accountable for work you can no longer see being made. The disclosure problem. Exercise: The Visibility Map.

2. What Changes in the Work, and What Changes in the Team. Effort is no longer visible in the draft. The compressed middle. The uneven edge. Exercise: Same Task, Three Ways.

3. Making AI Use Visible Without Punishing It. Psychological safety as the precondition. The team AI working agreement. Exercise: Drafting the Working Agreement.

4. Leading AI-Enabled Teams in Professional Services. Using Claude across the engagement team lifecycle, from staffing to client delivery. Team case: The AI-Enabled Engagement Challenge.

5. Reviewing AI-Assisted Work. Review the premise before the product. Fluency lowers the guard. Exercise: The Review Drill.

6. Developing People When AI Does the Early Work. The apprenticeship problem. Guardrails for learning. Exercise: Designing the Development Path.

7. The Hard Conversations. Over-reliance, under-use and undisclosed use. Emotional intelligence under uncertainty. Exercise: Conversation Rehearsal.

8. Building Your Team Standard. The arc, and one change on a named team within two weeks. Exercise: The Team Commitment.