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AI Context Maturity Index

Market trends in AI-native IT Operations & Service Management

We asked IT, operations and platform leaders how ready their operational data really is for AI. Most have the tools in place. Far fewer know who owns the data, how much they can trust it, or what they've invested in it.

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AI agents don't just read your data. They act on it.

For years, your CMDB and operational systems were a record of what you had. Now AI agents and copilots are being asked to use that same data to make decisions: who owns a service, what breaks if a server goes down, which incident to fix first.

That changes the stakes. If the data is fragmented, outdated or nobody's responsibility, the agent won't know. It will act on it anyway, confidently.

The organizations that get this right treat their operational data as connected, owned infrastructure. We call that a context layer. Our benchmark shows how far most organizations still are from having one.

What we found

The technology is mostly in place. The ownership, trust and investment behind it aren't.

  • Agents are already in the data. Many organizations are already piloting or running AI agents on their operational data.
  • Nobody's in charge. A significant share have no clear owner for their context data strategy.
  • Investment is a blind spot. The most common answer to “have you invested in data quality?” was “not sure.”
  • Nobody fully trusts it. Not a single organization rated its data at the highest level of reliability for AI decisions.
  • The core data comes last. Agents see CRM and tickets first. Infrastructure and configuration data, the foundation of it all, lags far behind.
  • A concept many haven't heard of. Many leaders had never heard of a context layer, and very few are building one.

In short, organizations are giving AI agents access to data before deciding who owns it or whether it can be trusted.

What this means for you

Automation isn't trust.

Most organizations already discover much of their data automatically. But collecting data automatically doesn't make it correct. Reliability needs its own attention.

Ownership comes before scale.

When no one owns the data, every new agent adds risk instead of value. Settle who's accountable before you expand access.

Compliance gets easier when data is connected.

DORA, NIS2, GDPR and the EU AI Act all ask versions of the same question: do you know what your systems depend on, and what your AI does with the data? A connected context layer lets you answer once, instead of rebuilding the picture for every audit.

“The technology is broadly in place. What’s missing is the connection between it.”

From scattered data to an AI-ready context layer

You don't need another tool. You need to connect what you already have.

  • One source of truth. Bring your CMDB, ticketing and infrastructure data together, so there's one version everyone trusts.
  • A clear owner. Name the person or team accountable for your context data, and close the “nobody’s in charge” gap.
  • Connected context. Link services, infrastructure, dependencies and costs into one model you can query, instead of a set of silos.
  • Smarter agent access. Give agents the data they actually need, like CMDB and billing, not just CRM and tickets.
  • Measure trust, not just automation. Track how reliable your data is for AI decisions, separately from how much of it is automated.
  • Visible investment. Make it clear what’s been spent on data quality, so “not sure” stops being the answer.

Our advice: start with ownership and visibility, not new tooling. That's where the biggest gaps are today.

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