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Industry leaders confirm what the data shows: AI failure starts with data problems
"The single most detrimental factor leading to the failure of AI projects is poor data quality and governance. Organizations frequently misjudge both the amount and caliber of data needed for AI to operate effectively."
"Most AI projects are failing because companies are treating AI like a magic solution that can instantly replace human workers without understanding its fundamental limitations... Their data is often trapped in outdated siloed systems that cannot effectively feed AI models."
"At least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value."
"80% of AI projects fail--twice the rate of failure of IT projects that do not involve AI... Successful projects are laser-focused on the problem to be solved, not the technology to solve it."
"Many companies that have invested in AI are realizing that their tools necessitate extensive customization, lengthy implementation processes, and still fall short in delivering meaningful returns."
"AI doesn't fail because the models are wrong. AI fails because we're asking Ferrari-level questions of Pinto-level data. You can't train a racehorse on table scraps and expect it to win the Kentucky Derby."
Beyond the statistics: what failed AI initiatives actually cost organizations in lost dollars, wasted potential, and damaged credibility
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