Course Format and Delivery

Delivery Method: Instructor-led LiveOnline

About this course

AI adoption is a change program, whether anyone calls it one or not. Most organizations run it as a technology deployment. The tool goes live, the training runs, and usage stalls. The risk is not that organizations fail to buy AI. The risk is that they install it without managing the change, and leave the people who decide whether it works out of the design. The evidence is specific. People who watch an algorithm make a mistake become less willing to use it, even after seeing it outperform a human. When they can modify its output, even slightly, they use it more, perform better and are more satisfied. Trust in AI collapses on visible error and recovers through control.

This course works on two fronts. AI as the change: why AI adoption breaks the assumptions behind conventional rollouts, how resistance to AI differs, and how to design adoption that survives the first public mistake. AI as the tool: using Claude to map stakeholders, test messages, synthesize feedback and pressure-test a change plan before it meets the people it affects. People adopt what they help shape.

What you will learn

• Explain why AI adoption behaves differently from a conventional technology rollout

• Diagnose readiness for AI-driven change across the five readiness beliefs

• Separate the technical work in an AI program from the adaptive work, and lead each differently

• Predict how trust in AI will respond to visible errors, and design for recovery

• Design adoption that gives people meaningful control over AI output

• Map stakeholders by what they stand to lose, not only by influence

• Use Claude to draft, test and adapt change messages, and to synthesize feedback while preserving dissent

• Build adoption measures that show sustained use and better work, not just access

• Advise a sponsor on AI-driven change in terms they can act on

This course is great for

Consultants who lead or advise on transformation, restructuring and performance improvement work, Senior Associates through Managing Directors, plus practice leaders responsible for the firm's own AI adoption.

Topics Covered

1. Frame and Baseline. AI adoption as change. Why AI change touches identity, expertise and job security at once. Exercise: The Adoption Autobiography.

2. Why People Resist AI, and When Resistance Is Right. Algorithm aversion, the expert problem, the social penalty. Resistance as information. Exercise: Reading the Resistance.

3. Readiness and Diagnosis. Five readiness beliefs. Technical work versus adaptive work. Diagnose before you design. Exercise: Readiness Diagnostic.

4. Change Management with AI in Professional Services. Using Claude across the change lifecycle, from case for change to sustainment. Team case: The AI Change Challenge.

5. Designing for the First Visible Error. Control as an adoption lever. Calibrated trust. Error protocols. Exercise: The First-Month Failure.

6. Using AI to Run the Change. What Claude does well and badly in change work. The synthesis risk. Data rules. Exercise: Message Lab.

7. Systems Thinking: Where AI Change Ripples. Workload, handoffs, roles and incentives move together. Second-order effects. Exercise: The Ripple Map.

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