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AI Project Failure Rates

Are High

The Stats...

 

 

 

 

 

 

 

 

 

 

AI Project Failure Stats...

When you think how much time, effort and money has gone into introducing artificial intelligence, how many of these projects have failed? Back in late 2017, Gartner analyst Nick Heudecker estimated that the failure rate of analytics projects was 85%. Move the calendar forward two years and there's no solid evidence proving that the failure rate has improved in any meaningful way.

According to TechRepublic, 85% of AI projects eventually fail to bring their intended results to the business.

Most organizations reported failures among their AI projects, with a quarter of them reporting up to a 50% failure rate.

Of the projects that didn't fail outright...

  • 31% didn't meet their goals, 
  • 43% exceeded their initial budgets, and 
  • 49% were late. 

Changing project objectives are to blame for 36 percent of failed projects, but changes in the plan don't have to lead to failure.

Capgemini's study found that only 27% of data projects are regarded as successful, and only 8% of the big data projects are considered very auspicious.

After billions of dollars invested, IBM explores the sale of Watson Health.

But There are Successes!

What about the 15% that succeed? Well, 92% of AI success stories involved multi-disciplinary teams led by Chief AI, data, or analytics officers, and comprised of specialists such as...

  • data scientists
  • data modelers
  • machine learning
  • data, and AI engineers
  • visualization experts, and 
  • data quality, training, and communications specialists.

DecisionIQ has produced $16 million is savings for our clients over the last three years.

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