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One of the world's largest PC manufacturers spent seven years collecting telemetry data from its devices. When Sachin Dharmapurikar's team at The Modern Data Company finally examined it, they discovered that two of the 70 fields had been recorded incorrectly the entire time. No one had checked. The data just sat there, accumulating volume and cost but not value.
That anecdote captures a pattern playing out across corporate America. Companies spent the last decade stockpiling data in the cloud, betting that more information would eventually yield better decisions. Then ChatGPT arrived, and executives assumed they could feed all that stored data into a large language model and watch insights appear. Dharmapurikar calls this the "ChatGPT curse."
The results have been bad. According to McKinsey's 2025 Global AI Survey, which included about 2,000 participants across 105 countries, 88% of organizations now use AI in at least one business function, but just 39% report any enterprise-level EBIT impact. A RAND Corporation study found that more than 80% of AI projects fail, roughly twice the failure rate of IT projects that do not involve AI. The technology is not usually the problem. The data is.
