7 reasons your AI strategy isn't working

Where did it all go wrong? Instead of achieving success, many new AI adopters are tripping into common pitfalls that derail the technology's benefits. Here's how to get back on track.

7 reasons your AI strategy isn't working

Artificial intelligence (AI) promises to help enterprises boost productivity, business agility, and customer satisfaction while shortening the time required to bring new products and services to market. Yet as more IT leaders plunge their organizations deep into AI science, many are finding disappointment rather than success. A 2020 IDC study, for instance, found that 28% of AI and machine language (ML) initiatives have failed.

Creating an effective AI strategy requires careful planning, setting well-defined goals, and building a strong management commitment, plus the ability to deftly avoid common mistakes. If your organization's current AI strategy is failing to deliver its expected results, here are seven likely reasons why.

1. Insufficient staff training

Failing to adequately address user needs is one of the biggest obstacles to a successful AI deployment.

"Unless businesses prepare people to use an AI solution, it will not scale," warns Charla Griffy-Brown, professor of information systems and technology management at Pepperdine University's Graziadio Business School. This isn’t just about training, she adds. "It requires updating policies and putting business support in place, not just technical support."

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