In an industry where failure is rarely discussed openly, this round table offers something genuinely valuable: a safe, pre-competitive space to share what has gone wrong with AI projects — and what was learned as a result.
From misaligned expectations and poor data quality to underestimated complexity and organisational resistance, AI initiatives fail for many reasons. Understanding those reasons — and how others have navigated them — is one of the most powerful ways to improve the success rate of future projects across the industry.
This session operates under Chatham House rules, enabling frank and honest conversation. Participants are invited to come prepared to share a failure, a near-miss, or a hard lesson — no matter how uncomfortable. The collective intelligence in the room will be the richer for it.
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