A recent study by MIT researchers has shed light on the often-overlooked reality of AI deployments in businesses. The study tracked 300 real AI implementations against profit metrics, revealing a striking funnel: 60% of companies evaluate AI tools, 20% run a pilot, and only 5% reach full production deployment.

The study found that companies that successfully deployed AI had a clear pattern: they identified bounded tasks with specific inputs, defined outputs, and contained failure modes. They also measured success criteria before deployment, not after. In contrast, the 95% that didn’t make it to production were characterized by haste, lack of defined success metrics, and the assumption that efficiency gains would be obvious once the tool was in the workflow.

The research highlights the importance of distinguishing between headcount metrics and efficiency metrics. As one notable example, Klarna is already rehiring humans after the AI efficiency numbers didn’t hold up at scale. The study’s findings serve as a warning to businesses to carefully evaluate their AI deployments and set clear success criteria before investing in these technologies.

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