8 reasons AI projects fail

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Why do artificial intelligence projects fail? Let’s examine some recurring issues – and expert advice on how to avoid them and increase your AI project’s chances for success


AI projects

AI projects- It’s a sobering stat: Seven out of 10 executives whose companies had made investments in artificial intelligence (AI) said they had seen minimal or no impact from them, according to the 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report.

“Many enterprises have inflated expectations from AI solutions.”

At the heart of the matter may be a general lack of understanding about AI capabilities and requirements. “At this point in time, many enterprises have inflated expectations from AI solutions,” says Anil Vijayan, vice president at Everest Group. “This can often create a mismatch between what is expected and what is achievable.”

But “get smarter about AI” is not the most nuanced takeaway. In fact, there tend to be some more specific recurring reasons why AI projects fail – and steps IT leaders can take to increase their chances for success. Here are eight of the most common mistakes and miscalculations that can portend AI project failure.

8 causes of AI project failure

1. Shiny things disease

“Most digital journeys begin with a technology-first orientation, going deep into a solution’s capabilities [such as] confirming which machine learning libraries it uses,” says JP Baritugo, director at business transformation and outsourcing consultancy Pace Harmon. “Instead, firms should first concisely articulate the key business imperatives it wants to address. Once defined, these objectives then drive and inform what digital and transformational interventions to pursue – including AI.”

Many leaders may be unclear about where AI will be best leveraged in their organization. Working closely with the business to identify where AI might solve an existing problem or focusing on areas where others have found AI to be valuable – like marketing, financial planning, or risk analysis – can be good places to start.

2. Insufficient training data

“AI solutions require meaningful, labeled training data sets.”

“AI solutions do require meaningful, labeled training data sets to achieve desired outcomes,” says Vijayan. “Often, lack of availability of data for training is a key reason for failure.” Depending on the type of AI being applied, that may mean anywhere from thousands to millions of data examples to train the model.

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Article Credit: Enterprisers Project