Stefanie Teichmann explains why the old playbook must change to include AI use in change management.
In the C-suite the debate has shifted. The question is no longer whether AI will affect the business, but how deeply. AI signals a new era of work: faster, less predictable, and without clear end points.
For decades leaders leaned on established models of change. Lewin’s Unfreeze–Change–Refreeze offered a simple three-stage structure. Kotter’s 8 Steps laid out a leadership playbook, from creating urgency through to embedding change in culture. These approaches proved useful for large but finite initiatives, like rolling out a new CRM, centralizing shared services, or integrating after a merger. Each had a defined destination. AI is different. The technology advances every month, assumptions expire quickly, and change needs to be managed as a permanent capability rather than an executional milestone.
Why the Old Playbook Falls Short
Many traditional programs leaned toward a linear, top-down approach, with heavy emphasis on plans, budgets, and deadlines. People were sometimes described in terms of resistance to be managed rather than as partners to engage. Success was often declared at go-live, when the system or process was delivered.
For AI, that framing is too limited. Technology adoption is only part of the challenge. The deeper work is helping people trust, use, and integrate AI into their daily decisions. That means reshaping roles, building new skills, and addressing concerns about the future of work. Resistance is rarely about the application itself; it more often reflects concerns about careers, competence, control, or trust in how AI will be used. Addressing these concerns directly – with empathy, transparency, and practical support – helps shift energy from resistance toward constructive adoption.
The risk profile has also expanded. Beyond delivery concerns, AI introduces exposure through data quality, bias, privacy, security, and accountability. Poor handling of these issues can erode trust with employees, customers, and regulators. Ethics and governance are most effective when designed from the start, not bolted on later.
Key points include:
- Kotter updated for AI
- ADKAR for individual adoption
- What Really Matters with AI Change
Read the article, Changing Change: Rethinking transformation in the age of AI, on LinkedIn.