Who it's for
Managers and change leads are responsible for driving AI adoption through a team that isn’t fully on board yet.
What participants can do by the end
Describe the specific change dynamics AI adoption introduces, resistance types, adoption patterns, and workforce impact, in a GCC organizational context. Apply a structured AI change readiness assessment to identify adoption risks, capability gaps, and resistance sources within a team. Diagnose the root causes of resistance to AI adoption in a GCC workplace scenario, distinguishing fear-based, skills-based, and culture-based resistance. Examine AI change communication strategies across three channels and identify which is most effective for a given GCC audience. Build a 60-day AI adoption roadmap for one team function, incorporating readiness findings, communication strategy, and capability-building steps
How the three days build:
Day one covers what makes AI change genuinely different, pace, ambiguity, job displacement fear, patterns of AI adoption resistance specific to GCC workplaces, then runs a four-dimension readiness assessment, awareness, skills, culture, infrastructure, before diagnosing which of three resistance types, fear-based, skills-based, culture-based, a specific team is actually showing and matching the right intervention to each. Day two covers communication strategy across leadership, peer, and digital channels, crafting messages that address resistance without creating panic, then moves into building AI change champions, who should take the role, how to brief and support them, and how to measure whether they’re actually working. Day three is the roadmap itself, using readiness, resistance, and communication findings to build a 60-day plan structured around awareness, skills, pilot, and review, ending with how to present it to leadership for buy-in.
This is change management built for the specific fears AI adoption creates, not a generic template with “AI” added to it.
Why this matters specifically for AI rollouts:
a change program built for a system migration or a policy update doesn’t map cleanly onto AI adoption, because AI adoption uniquely triggers job security fears that a new expense system never will, and a manager who treats that resistance as generic pushback typically picks the wrong intervention and loses the team’s trust. This program gives change leads a diagnosis-first approach, so the intervention actually matches the resistance instead of guessing and hoping engagement improves