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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