Who it's for
managers and teams actively redesigning how work is divided between people and AI tools.
What participants can do by the end
Describe the three human-AI collaboration models, AI as tool, AI as partner, AI as autonomous agent, and identify which applies to their own current work. Apply a task delegation framework, based on volume, structure, exception rate, accountability, and data sensitivity, to determine what’s appropriate for AI, what needs collaboration, and what needs human-only judgement. Differentiate effective from ineffective human-AI collaboration by examining five documented GCC case studies against a structured quality rubric. Examine their own workflow for collaboration design gaps, over-reliance or under-utilization, and propose two specific adjustments. Identify who owns the decision when AI contributes to a consequential outcome
How the three days build:
Day one covers the three collaboration models in practical terms, what humans do that AI genuinely cannot, moral judgement, lived experience, cultural empathy, and builds a task delegation framework the participant tests against their own team’s edge cases. Day two examines five GCC collaboration case studies against a five-dimension quality rubric to see what worked and what failed, then dives into why humans over-trust AI, automation bias, the authority effect, cognitive offloading, and equally, why humans sometimes ignore valid AI recommendations they shouldn’t. Day three tackles accountability directly, who owns a decision when AI provided the recommendation, and how to document the human contribution to an AI-augmented decision in a way that holds up under scrutiny.
Teams leave with a shared, defensible answer to a question most are still improvising
Why this matters beyond any single tool:
as AI tools get more capable, the collaboration question only gets harder, not easier, because the temptation to hand over more of a task grows even where accountability shouldn’t move with it. Teams that never build a shared delegation framework end up with inconsistent practice, one person double-checking everything AI touches, another trusting it blindly, and no shared standard for either. This program gives the team a common language for that decision, so collaboration design becomes deliberate instead of accidental, and repeatable across every new AI tool the team adopts afterward.