Ninety-five percent of generative AI pilots return no measurable ROI while roughly $250 billion flowed into AI startups and only 5% of projects produced value. Investors are funding many ventures in a gold-rush environment without clear understanding of practical constraints. The primary failure mode is poor operational data—low quality, missing records, and absent ground truth—rather than weak algorithms. Complex physical industries remain heavily analog with undocumented processes, making it difficult for models to improve asset performance or efficiency. Executives are being sold transformative promises that ignore data readiness and necessary engineering work to deploy reliable AI solutions.
Last week, MIT published a report that should make every CEO sit up straight: 95% of generative AI pilots deliver zero return on investment. Let me put that in perspective, AI startups took in around $250 billion in venture dollars over the past 12 months, and only 5% of those projects created measurable value. This is the definition of a gold rush. Investors are throwing shovels at anyone who promises they'll strike digital gold.
Here's the hard truth: AI isn't failing because of weak algorithms. It's failing because of weak data. Every Fortune 500 boardroom has seen the pitch. Sleek slide decks. Dazzling demos. Sweeping promises about transformation. But scratch the surface and you'll find a fatal flaw, companies don't have the data these algorithms need to work. Poor data quality. Missing data. No ground truth. That's the real problem, not the math. An algorithm is only as good as the fuel you put in.
The hype is real. The fundamentals are wrong. Most people in tech don't realize how analog the physical world is, and how complex the systems are. How can an energy facility decrease BTUs and increase KWs if it doesn't even know why its assets are failing or how to improve heat rates? How can a manufacturer increase production or efficiency if three-ring binders are the operating system?
Collection
[
|
...
]