I’ve seen a frequent pattern with AI deployments. A team blames the model or the vendor when their AI rollout stalls, but the real problem was sitting in their data the whole time: scattered across systems, poorly governed, never structured for AI to use. This piece I wrote for TechRadar walks through three specific steps to get your data AI-ready.
Originally published in TechRadar
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 another adds “ACCT” as a prefix. That small inconsistency creates an extra reconciliation step every time you join those data sets.
Data governance compounds the problem. Without a system intentionally designed to control who accesses data, where it moves and what protections are in place, gaps emerge fast. PII exposure is the most obvious risk: an email address that ends up in the wrong hands can trigger a serious breach. Raw, unstructured data also yields mediocre AI outputs and is more expensive to process.
Clean, structured data yields better results at lower cost. A third gap, explainability, is quickly becoming a legal requirement. Many countries and several U.S. states now require organizations to demonstrate how AI-driven decisions were reached. Cut corners on the data foundation and you may not be able to show that chain of reasoning.
At that point, you’re either in compliance violation territory or your model is producing outputs you can’t defend.
Three Steps to Get Your Data AI-Ready
Define governance before you deploy. Classify your data: what is it, where did it come from and who can touch it. Separate the roles of technical decision-making and compliance oversight. Keeping those responsibilities with different people prevents a compromising situation where the same person sets the rules and monitors compliance.
Run cross-functional AI governance as a standing function. Assign a representative from every department and meet monthly to discuss what teams are working on, what concerns have surfaced and what support they need from one another.
Approach larger AI-readiness initiatives like any other business project: assign a project manager, designate an executive owner, set a weekly cadence, build a task list and work through it.
Collect behavioral data even before you need it. The outcomes you get from AI vary enormously depending on how skilled the operator is, ranging from using it as an expensive search engine to developing autonomous workflows. Without visibility, you might be pouring money into AI licenses and getting Google-level output in return.
You don’t know who needs training, whether they have the right tool in front of them or what outcomes they’re achieving. The risk is that you make the wrong strategic call as a result—abandoning a rollout, for example, when the real fix was better training or a different tool.
Further considerations
Here’s another layer to consider. When an experienced worker completes a task, with AI assistance, they leave more skilled than when they started. The output and the learning happen together. That’s what behavioral data should demonstrate over time – not just task completion, but upward skill trajectories.
When someone at the beginning of the learning curve accepts whatever AI produces without critically engaging with it, you get the output but not the growth. Behavioral data is how you catch that gap early, before it becomes a long-term cost you can’t unwind.
Stay curious and look for the easy wins. Focus your data readiness efforts on the workflows where work actually happens, and prioritize tools that let you get at that data.
A recent example illustrates the payoff. A product manager ran an AI-powered analysis of quarterly bug patterns using data from the department’s most commonly used tools. The results were unexpected. One team carried a disproportionate share of incoming tickets, most of them requests for manual workarounds to a missing product feature.
While other teams split their time roughly 75% on new work and 25% on incoming bugs, that team was closer to 50-50. By not building a single feature, the organization was effectively operating 1.5 people below capacity.
The entire analysis took about 45 minutes. None of it would have been possible without data that was organized, tagged by team, connected to individual contributors, accessible via existing AI connectors and protected by role-based access controls.
The organizations that get the most from AI are the ones that empower their people to ask “I wonder if there’s something here” — and have data to diagnose in an afternoon. That only happens when the foundation is already in place.
