TLDR: The companies profiting from AI scaled the work they had redesigned first, kept people on the decisions that carry risk, and grew only the parts that stayed reliable.
The speed trap
Speed was the first thing every leadership team noticed about generative artificial intelligence (AI), and speed is where the trap was hiding. A pilot stood up over a weekend. A support queue cleared overnight. A campaign that used to take a fortnight shipped in an afternoon. The dashboards looked spectacular, and the profit-and-loss statement (P&L) stayed flat.
The Massachusetts Institute of Technology’s Project NANDA put a number on the distance between motion and money. Its 2025 study, The GenAI Divide: State of AI in Business, found that only 5 per cent of enterprise AI deployments produced measurable P&L impact, while the other 95 per cent stalled before the books changed.1 The decisive factor sat in integration: how well a company folded AI into its workflows, its governance, and the way its teams actually decide. The same report found that pilots built by pairing internal specialists with outside expertise reached production 67 per cent of the time, against 22 per cent for builds left to the technology team alone.
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