AI Failure Example: Widespread Across The Enterprise

Understanding Why AI Projects Fail



Steven Lerner
07/17/2019

ai failure example

If you’re searching for an AI failure example in the enterprise, apparently you don’t have to look very far. A July 2019 study from International Data Corporation (IDC) found that a quarter of organizations using artificial intelligence experienced a failure rate of up to 50%.

Any time an organization consistently fails at the same task, there is bound to be significant repercussions. This is especially true in AI, which had demanded more internal resources in recent years.

How do organizations today view artificial intelligence? What are the biggest obstacles to successfully adopting AI? Finally, what’s next for the technology in the enterprise?

Root Causes Of Failure

According to the IDC survey, two of the biggest contributors toward AI failure include unrealistic expectations and internal staff that lacked AI skills. These results echo the AI skills shortage in the enterprise. Half of all organizations admit that their workers don’t have the right skills that align with AI.

These results also mirror a Deloitte survey from October 2018, where 69% of companies reported a skills gap that was “moderate, major or extreme.” Additionally, over 80% believe that AI will result in moderate or substantial changes in job skills within the next three years. There is a clear recognition in the industry that in order to close the AI skills gap, there needs to be a new influx of developers and data scientists who have the expertise to handle this technology.

Perhaps the bigger issue behind the failure of AI projects is the absence of an enterprise-wide AI strategy. The IDC findings revealed that only 25% of enterprises have developed these blueprints for success.

“For many organizations, the rapid rise of digital transformation has pushed AI to the top of the corporate agenda,” said Ritu Jyoti, program vice president, artificial intelligence strategies. “However, as AI accelerates toward the mainstream, organizations will need to have an effective AI strategy aligned with business goals and innovative business models to thrive in the digital era.”

Adoption Challenges

On the surface, adoption of AI seems to be growing. A January 2019 study from Gartner found that 37% of companies already deployed AI, which is up from 10% in 2015. That represents a 270% increase over the past four years. One of the big drivers of AI in the enterprise is because of the fear of competitors already adopting this technology.

Although the failure rate for AI seems high for these companies, there are other organizations that haven’t even implemented any AI projects. The high price tag and bias in the data were identified as two of the issues holding companies back from AI adoption.

Despite the increased adoption of AI, there are several obstacles prohibiting adoption of the technology. Deloitte found that 20% of enterprises ceased AI projects due to pressing cyber security concerns, which is largely seen as one of the biggest complications of AI. Legal and regulatory risks were also seen as challenges in the process.

Implementing AI Across The Enterprise

Although this AI failure example seems troublesome, most enterprises at least understand the value of the technology. Two thirds of enterprises are attempting to build an “AI First” culture, and half see the technology as a necessity.

There are several objectives behind the growth of AI. Enterprises want to increase time to market, productivity, and customer satisfaction with the technology. Although AI can be used by different departments, the respondents said that IT operations should be the leading area for deployment.

What’s Next For AI?

As the market for AI increases over the next few years, it is critical that enterprises recover from any project errors. Even if an organization has experienced an AI failure example, it is imperative that IT teams learn from their mistakes. One area to consider improving on is data management, which is critical to AI success.

How are organizations responding to the challenges of AI? Read our exclusive report on The Evolution Of AI to learn from leading experts.

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