Why 85% of AI projects fail

Why 85% of AI projects fail

Senior management is still creating barriers to AI adoption and implementation, according to a Pactera Technologies report.

The next step for machine learning and AI
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Despite increased interest in and adoption of artificial intelligence (AI) in the enterprise, 85% of AI projects ultimately fail to deliver on their intended promises to business, according to a Thursday report from Pactera Technologies.

A major source of AI challenges is found in senior leadership, the report, titled Artificial Intelligence Localization, Winners, Losers, Heroes, Spectators, and You, found. Some 77% of those surveyed said they face barriers to entry from senior management not seeing value or wanting to make the investment in the emerging technology.

These findings are in line with those from a recent Dimensional Research report, which found that eight out of 10 organizations engaged with AI and machine learning said those projects had stalled, and 96% said they have run into problems with data quality, data labelling, and building model confidence.

Pactera presented the report to a group of tech industry leaders including those from Facebook, Adobe, Amazon, and Microsoft at a recent private event in Seattle. At the event, 100% of leaders said they want to use tools like AI-powered Neural Machine Translation (NMT) because it would allow them to quickly localize content in at least 72 languages. However, only 23% said they currently use the technology, demonstrating that organizations are still cautious about adopting new AI-related technologies, the report noted.

“Interestingly enough, human vision, guidance and input ultimately play a big part of an AI project’s success,” Jose Martinez, vice president of digital innovations and solutions at Pactera, said in a press release. “Identifying business goals that AI can readily achieve, like Neural Machine Translation, and managing the teams that scrutinize data is what ultimately improve a business’s leveraging of AI.”

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