9 AI project mistakes to avoid

From building isolated proofs of concept to not defining how to measure success, a wide array of gotchas can derail your AI project’s prospects for delivering business value.

9 AI project mistakes to avoid
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Business enthusiasm for AI continues unabated. IDC’s latest predictions say worldwide business spending on cognitive and AI systems — from chatbots to deep learning, plus the infrastructure to power them — will more than triple from the $24 billion forecast for this year to $77.6 billion in 2022.

More demonstrably, AI has gone from early adopters to mainstream business use cases, with a wide array of organizations across almost every industry exploring pilot projects and putting AI to work in production. But that doesn’t mean it’s foolproof to implement. If you don’t want to waste the money you’re going to spend on AI, here are some common mistakes to avoid.

Biting off more than you can chew

“Don’t try to boil the ocean on day one,” Lance Olsen, director in Microsoft’s Cloud AI team, tells CIO.com. You can’t transform your entire business decision-making process with AI overnight, so it’s best to start small and take evolutionary steps as you gain expertise.

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