CONCEPT
The Execution Gap
Ikhlaq Sidhu’s diagnosis of why most AI deployments fail: not because the technology is wrong but because the human layer is—the gap between what a demonstration proves is possible and what an organisation can actually make work inside real workflows, with real data and real users.
The pattern is unnervingly consistent. A company sees a demonstration. The demonstration is impressive. An executive sponsors a pilot. The pilot is built by a small, enthusiastic team. It works on toy data. It is then handed to a larger team for production deployment, where it encounters real data, real users, real workflows, and real legacy systems. It dies. The post-mortem blames the technology, the team, the vendor, or the timing. It rarely blames the execution model. Ikhlaq Sidhu has been naming this as the root cause of most innovation failures—in AI and before AI—for twenty years. The execution gap is the distance between what is technically possible and what an organisation can reliably make work in production, and it is determined not by the quality of the technology but by the behavioural and organisational capabilities of the humans deploying it. AI has changed the distance—the gap is now
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