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A connected AI fabric, woven across the Xurrent platform, Sera AI brings together signals, service data, and workflows to reduce noise, focus teams on what matters, and accelerate action across IT and the enterprise.

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New EMA Research | October 2026

ITAM in the Age of AI: EMA's 2026 Maturity Benchmark

71.9% of enterprises run AI in operations, but only 22.2% call their ITAM AI-ready. Get EMA's 2026 benchmark on CMDB accuracy, asset truth, and AI readiness.

AI is already making decisions inside your service desk. It routes tickets, flags risks, and suggests changes. Every one of those decisions leans on asset and CMDB data.

So here's the uncomfortable question. Would you let a machine act on your CMDB today, without a human double-checking first?
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The Verdict: Optimized ITAM isn't the same as AI-ready ITAM.

Nearly half of respondents describe their ITAM as either optimized or AI-ready. On paper those stages look close. In practice they aren't. Organizations that call their ITAM AI-ready were far more likely than those that call it optimized, the stage just below, to report 95%+ CMDB accuracy (55.6% versus 7.5%). They were also more likely to report a single authoritative source of asset truth (86.7% versus 54.7%).

What's Inside the Report

The AI readiness gap, measured. How fast AI adoption is moving compared with the asset data foundation underneath it, across 203 organizations with 1,000+ employees.

Where asset data actually breaks down. Coverage isn't the main problem. Integration (31.0%) and accuracy (27.1%) are the most-cited single biggest gaps in discovery and asset data. Coverage ranks lower at 12.8%.

The state of the CMDB in 2026. How organizations populate it (65.5% still do it at least partly by hand), how they reconcile conflicting sources, and the top challenges to keeping it accurate.

The AI readiness threshold. EMA's six statistically validated capabilities that distinguish AI-ready organizations from optimized ones, organized into three concepts.

What's holding teams back. Siloed tools or teams (28.1%) and skills or staffing constraints (27.6%) top the list of factors limiting ITAM's support for AI.

EMA's path to AI-ready ITAM. Six practical recommendations for assessing your own readiness, from establishing authoritative asset truth to raising accuracy for automated decisions.

Frequently Asked Questions About AI-Ready ITAM

What is AI-ready ITAM?
AI-ready ITAM is an asset management function that delivers trusted asset information as a single source of truth across asset types, so people and automated systems can act on it. In EMA's 2026 benchmark, it's the highest of five self-reported maturity stages. 22.2% of respondents placed themselves there.
What's the difference between optimized and AI-ready ITAM?
Optimized ITAM is efficient at traditional asset management. AI-ready ITAM adds accuracy and authority that automation can rely on. EMA found AI-ready organizations were far more likely than optimized ones to report 95%+ CMDB accuracy (55.6% versus 7.5%) and a single authoritative source of asset truth (86.7% versus 54.7%).
How accurate is the average CMDB?
Most CMDBs fall short of the level of accuracy that automations need. Only 18.5% of organizations rate their ITSM and CMDB asset data at 95% accuracy or higher. 60.0% land in the 80% to 94% range, and 20.0% sit at 60% to 79%, per EMA's 2026 benchmark.
What is the biggest gap in IT asset and discovery data?
Integration is the most-cited gap at 31.0%, meaning data lives in silos and can't be reconciled into one source of truth. Accuracy follows at 27.1%. Coverage, or simply missing devices, ranks lower at 12.8%. Finding assets isn't the hard part. Trusting the data is.
Why does CMDB accuracy matter more when you use AI?
People can investigate a data discrepancy before they act. Automated workflows can't, so they need explicit rules about which information to trust. EMA notes that AI and automation can magnify the consequences of conflicting asset data, which raises the bar for accuracy.
How do most organizations populate their CMDB?
Most still rely on some manual work. EMA found 53.5% use a mix of automated and manual entry, and 12.0% populate the CMDB manually. Combined, 65.5% build their CMDB at least partly by hand, which makes it harder to keep current as environments change.

AI is moving faster than the asset data it depends on. EMA's benchmark shows exactly where that gap sits and what AI-ready organizations do about it. Get the full report and see where your organization stands.

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