There is a statistic worth sitting with.

According to Gartner, 63% of organizations either do not have – or are unsure if they have – the right data management practices for AI. That number comes from a 2024 survey of more than 1,200 data management leaders. People whose entire job is to think about this.

And Gartner’s prediction is starker still: through 2026, organizations will abandon 60% of AI projects because the data simply was not ready to support them.

This is not a story about AI failing. It is a story about data.

What “AI-ready data” actually means

When most teams begin preparing for AI, they focus on tools — which model to evaluate, which vendor to pilot, which use case to prioritize. What the research points to is something more foundational: the quality and governance of the data those tools will run on.

AI-ready data is not just clean data. It is data that is actively aligned to a specific use case, governed at the asset level rather than just in policy documents, supported by automated pipelines with quality gates built in, managed through live metadata that reflects what the data actually means today, and continuously quality-assured as it evolves.

Most organizations have pieces of this. Very few have all five in place — and even fewer have connected those practices intentionally to the specific AI initiatives they are trying to launch.

Why marketing and CX teams feel this most

The stakes are highest for teams building personalized, connected customer experiences. When you are trying to power real-time decisioning, audience activation, or journey orchestration, gaps in your data infrastructure do not just slow things down. They produce experiences that feel wrong to customers.

A recommendation that fires on stale data. An audience segment that no longer reflects who the customer is today. A model trained on data that was not representative to begin with. The AI layer amplifies whatever is underneath it — including the gaps.

This is why data infrastructure has to be part of the AI conversation from the start, not something that gets sorted out after the model is built.

The shift that matters

The organizations making meaningful progress on AI are not necessarily the ones with the most sophisticated models. They are the ones who have invested in treating data as a living asset — something that requires ongoing governance, continuous quality work, and deliberate alignment to the use cases it is supposed to serve.

This is a practice, not a project. Gartner is explicit on this point: AI-ready data is not “one and done.” It requires constant improvement based on existing and upcoming use cases. The metadata needs to be active, not archived. The pipelines need quality gates, not manual spot checks. The governance needs to be operational, not aspirational.

Where to start

If you are trying to assess your own readiness honestly, three questions tend to cut through the noise:

Do you know what data is feeding your AI initiatives — and can you vouch for its quality? Not in general terms, but specifically: what data, from where, governed how?

Are your data governance practices keeping pace with how fast your use cases are evolving? A policy written six months ago for a different initiative is not the same as active governance.

Is metadata management live and trustworthy? Metadata that describes what data used to mean — rather than what it means now — is a quiet risk that becomes loud when a model starts producing bad outputs.

The answers do not have to be perfect. But they should be honest.

The AI opportunity is real. The organizations that capture it will be the ones that did the quieter, less glamorous work of getting their data foundations right first.

Source: Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 26, 2025. Survey of 1,203 data management leaders, Q3 2024.

What would your AI strategy look like if data readiness were treated as a practice, not a project?

---

If you have questions, or just want to talk analytics, data strategy, and anything in-between, reach out to me at Katie@DataOnTrend.com.