Why AI investments fail to deliver

The success or failure of AI initiatives has more to do with people than with technology. If you want to put AI into practice in a way that improves business outcomes, you must avoid these 6 mistakes.

According to two recent Gartner reports, 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from prototypes to production. Yet the same reports indicate little sign of a slowdown in AI investments. Many organizations plan to increase these investments.

Many of these failures are avoidable with a little common-sense business thinking. The drivers to invest are powerful: FOMO (fear of missing out), a frothy VC investment bubble in AI companies with big marketing budgets, and, to some extent, a recognition of the genuine need to harness AI-driven decision-making and move toward a data-driven enterprise.

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