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While billions have been invested in AI projects, often sometimes with stunning results, the truth is that AI projects have a high rate of failure.
Gartner found that only about half of companies that invested AI are ever able to bring any projects into production and that 85 percent of projects fail. Dimensional Research found that 96 percent of Machine Learning (ML) projects suffered from data quality problems. A study by IDC looked at the success of new AI projects by businesses that already have at least one AI project already in production — the success rate was surprisingly low.
Anil Vijayan, vice president at Everest Group, said that “at this point in time, many enterprises have inflated expectations from AI solutions. This can often create a mismatch between what is expected and what is achievable.”
Part of the problem is that the AI project wasn’t sufficiently grounded in responding to a real business requirement. Jean Paul Baritugo, director at Pace Harmon, said that “AI projects deployed to address problems that are not aligned with the business imperatives and objectives, or are addressing underwhelming business questions, mute AI’s impact and affect its adoption. Instead, IT leaders should identify meaningful business problems that have a significant upside and are backed by substantial data.”
Ritu Jyoti, vice president at IDC, said that “for many organizations, the rapid rise of digital transformation has pushed AI to the top of the corporate agenda. 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.”