Why So Many AI Projects Fail – And How to Make Yours Succeed

AI projects often fail when driven by hype rather than solving real business problems, making thoughtful integration essential for success.

Business leaders are increasingly impatient to deploy artificial intelligence (AI) in their operations, with many having high expectations for what the technology can deliver.

Tech leaders are willing to spend to reap what they hope will be game-changing business improvements and streamlined operations, with a 61% rise in planned spending on AI in 2024, according to new research. But business leaders should strike a balance between their excitement for AI with the needs of the business. For all the promise of this technology, many companies have already ended up with AI proof-of-concepts which have not delivered the results they hoped for. Attaining tangible results from AI investment requires both careful thought, and attention to detail in execution.

With the huge hype swirling around AI technology over the past 18 months, business leaders have been tempted to call their IT teams and demand, ‘Why are we not using generative AI right now?’ But the truth is that often, in those businesses, both the leaders and their teams don’t actually know how to gain an advantage from AI. Leaders need to ensure that AI is rolled out for the right reasons, not just because their competitors are doing it.

There is a huge gap between exciting tech built in the laboratory and the day-to-day reality of business applications. It’s all too easy to take a short-sighted view and become over-excited by technology that has not yet crossed this gap. This is exactly how AI investment is wasted.

Why AI can fail

Even the very best technology is just a science experiment if it cannot be adopted and used in the real world. The single biggest reason AI ‘doesn’t work’ for businesses is that people try to ‘do AI’ rather than identifying where problems or inefficiencies exist. To find such problems, business leaders should first talk to partners, and listen to consumers and front-line employees. Does the business lack staff to talk to customers? Does the business need to find a way to cut fuel emissions? Beyond the hype, the real excitement of this technology comes not from thinking about AI as a standalone solution, but by adding AI into the solution to a real business problem.[…]