Why messy data will make your company’s AI bill much higher than expected

Healthier, cleaner data makes AI more efficient, trustworthy, and sustainable

by · TechRadar

Opinion By Paul Wnek published 6 May 2026

(Image credit: Getty Images)

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For all the talk about AI infrastructure, chips, and the staggering amount of electricity now required to support large-scale model training and inference, there is still a quieter part of the story that rarely gets the same level of attention inside enterprises, and that is the state of the data those systems are actually running on.

The International Energy Agency projects that electricity generation to supply data centers will grow from 460 TWh in 2024 to more than 1,000 TWh in 2030 and 1,300 TWh by 2035 in its base case, underscoring how quickly the energy demands around AI are rising.

Paul Wnek

Founder and CEO of Coalescence Cloud.

In the United States, the pressure is already visible. The U.S. Department of Energy says data centers consumed about 4.4% of total U.S. electricity in 2023 and are expected to consume approximately 6.7% to 12% by 2028.

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Those numbers are important, but they can also make the problem feel distant, almost as if AI sustainability is something that happens only at the hyperscaler level.

In reality, a meaningful part of the cost and waste associated with AI starts much closer to home, inside the CRM, the PSA platform, the finance system, the spreadsheet someone still keeps on the side because they do not trust the main dashboard, and the duplicate records no one has made time to clean up.

Digital load

Far fewer companies are asking a more immediate question about their own environments, which is how much unnecessary digital load they are creating simply because their data is messy.

AI does not arrive inside an enterprise and begin operating on some idealized set of perfectly structured information. It inherits whatever is already there.

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