This is the Electe Review. Today, why your AI problem is probably a data problem. The article's main claim is that the biggest thing holding back useful AI is often not the model. It's unreliable information. Big data and AI can't make bad records trustworthy. If dates are missing or two departments define active customers differently, an AI system can still give confident answers. Those answers just mislead. The surveys back this up. In a 2025 KPMG survey of large US organizations, 85% of leaders said data quality was the biggest challenge they expected for their AI plans. Privacy and cybersecurity came second at 71%. A separate survey of mid-sized companies found 41% ran into data quality problems while putting AI in place. Meanwhile, adoption keeps speeding up. Stanford's 2025 AI Index says 78% of surveyed organizations used AI in at least one business function in 2024, up from 55% the year before. Use of generative AI rose from 33 to 71%, but adoption is not readiness. Those figures say nothing about whether these companies fed their models clean, consistent data. The data keeps piling up too. IDC projected that the world's total data would grow from 33 zettabytes in 2018 to 175 by 2025. More data doesn't close a readiness gap, it makes it bigger. Cost is changing as well. Running a model at roughly GPT 3.5 level got more than 280 times cheaper between November 2022 and October 2024. Cheaper, yes. But token prices aren't the full cost of a workflow that someone still has to check. Then there's privacy. In Cisco's 2025 privacy study, nearly half of the professionals surveyed admitted putting personal employee data or other non-public data into generative AI tools. Warnings aren't enough. Staff need an approved, safe way to do this. So what does the article recommend? A test. Pick one decision your business makes again and again. Check whether the records behind it are complete, consistent, and current. If they aren't, your limit is the data, not the model. Then run a small, limited pilot. Connect only the data you need, and keep people in charge of decisions with real consequences. Treat every AI insight as a lead to look into, not an order to follow. Before you buy a smarter model, fix the records you feed it. That's the review.