AI projects rarely fail because of the models themselves. Instead, they fail because of messy data, flawed assumptions, and the unpredictable ways people use the technology. In this session, you'll hear how Cushman & Wakefield's AI strategy evolved through very real, and sometimes humorous missteps.
We will dive into what went wrong, why it happened, and how each failure directly improved Cushman's approach to governance, adoption, and delivery speed. Attendees will walk away with practical lessons for deploying AI in complex enterprise environments where accuracy, trust, and scalability matter most. Expect clear takeaways you can use to move faster in the future by treating failures as fuel, not setbacks.
