In a 2025 global survey by McKinsey, only 39% of organisations reported a measurable impact on earnings from their AI initiatives, highlighting a widening gap between AI investment and realised business value. Despite rising spend and board-level focus, many organisations struggle to translate AI ambition into sustained performance gains.

For many leaders, the constraint is not model capability or access to advanced technology. It is data. Enterprise AI systems often rely on static, backward-looking information that reflects hierarchy, headcount and cost, but fails to capture how work actually happens, how influence flows, or where skills, engagement and execution risks are emerging in real time. 

This Financial Times briefing, hosted in partnership with Workhuman, brought together senior leaders to examine how closing the human data gap can materially improve AI-driven decision-making and performance. The discussion explored what “good” looks like when it comes to human data, and why misalignment now represents one of the most under-recognised risks to enterprise AI success.

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