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Why 95% of AI Deployments Fail
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MIT's August 2025 study of 300 enterprise generative AI deployments found that 95% produced no measurable P&L impact. Gartner forecasts that more than 40% of agentic AI projects will be cancelled by 2027. McKinsey's State of AI 2025 identifies workflow redesign as the single strongest correlate with EBIT impact, yet only 21% of organisations have redesigned any workflows. The data converges on a structural conclusion: enterprise AI is failing because the operational substrate is inadequate, not because the models are. This episode examines the process-readiness gap, the misallocation pattern that concentrates investment in low-ROI front-office applications, and what the 5% of high performers do differently. It is an architectural argument, not a change-management one: AI is a linear amplifier acting on a pre-existing process, and the sign of the output depends on the sign of the input.
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If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!
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By David SuchSend us Fan Mail
MIT's August 2025 study of 300 enterprise generative AI deployments found that 95% produced no measurable P&L impact. Gartner forecasts that more than 40% of agentic AI projects will be cancelled by 2027. McKinsey's State of AI 2025 identifies workflow redesign as the single strongest correlate with EBIT impact, yet only 21% of organisations have redesigned any workflows. The data converges on a structural conclusion: enterprise AI is failing because the operational substrate is inadequate, not because the models are. This episode examines the process-readiness gap, the misallocation pattern that concentrates investment in low-ROI front-office applications, and what the 5% of high performers do differently. It is an architectural argument, not a change-management one: AI is a linear amplifier acting on a pre-existing process, and the sign of the output depends on the sign of the input.
Support the show
If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!