AI is messy: here's how to clean up your data before it derails your strategy
Getting AI-ready while building your data infrastructure is like learning to drive a manual transmission on the wrong side of the road.
It’s complicated and requires potentially dangerous multitasking.
Organizations with immature data-handling processes that are adopting AI are trying to solve multiple technology problems at once, and risk stalling out.
Unsurprisingly, 48% of enterprises cited data-related issues as their top challenge to AI adoption in NVIDIA's 2026 State of AI report.
Most enterprise AI programs don't fail because of the model or solution selected. They fail because underlying data is fragmented, inconsistent and poorly governed.
Get Your Data Foundation in Order
Enterprise data is messy in layers. It’s scattered across many systems, making it hard to pull together into a coherent picture. Even when you can consolidate it, you often will run into granularity or identifier mismatches. One application may store account numbers as plain digits, while...
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