Leading People Through Agentic AI Transformation: A No-Code Change Management Playbook

Every wave of technology ultimately succeeds or fails on human ground. Generative AI systems now draft documents, analyse data, and converse with customers, and a newer class of agentic AI systems can plan, decide, and execute multi-step tasks with limited human intervention. Industry evidence, however, is consistent: most AI initiatives stall not because the technology fails, but because people, culture, and change management do — employees resist tools they do not trust, middle managers feel bypassed, stakeholders are consulted too late, and adoption withers after the pilot. Leading AI transformation is therefore, at its core, an organizational behaviour and human resource challenge: one of trust, fairness, capability, communication, and change. This two-day Management Development Programme approaches AI adoption from that people-first vantage point. Using no-code tools, so that no programming background is ever required and participants experience the technology first-hand, and then learn to lead the human side of its adoption: mapping stakeholders, building change coalitions, managing resistance, redesigning roles, and ensuring that AI deployment is perceived as fair, transparent, and trustworthy by the workforce.

The programme unfolds over two intensive days, blending conceptual sessions, no-code laboratory work, and change-leadership practice:

Day 1: Foundations - AI, Work, and the People Leader

The AI landscape for decision-makers:

from chatbots and copilots to autonomous agents — what has changed, and what it means for organizations,

jobs, and skills.

How large language models actually work:

capabilities, limits, and the leaders mental model (no mathematics, no code).

The future of work with AI agents:

task redesign, role evolution, human–AI collaboration, and the shifting psychological contract.

No-Code Lab I:

participants create their first AI assistant and a working web page using prompt-driven, no-code tools.