Enterprise AI Adoption Outpaces ITAM Readiness, New EMA Research Finds
Survey of 203 IT professionals highlights a disconnect between organizations' operational AI adoption and their ability to support AI with trusted asset data
The study, authored by
The research also challenges the assumption that mature ITAM practices automatically prepare organizations for AI. Although 26.1% of respondents characterized their ITAM practices as optimized, the AI-ready group reported substantially stronger capabilities across six separately measured dimensions. All six associations met the research's statistical significance criteria after correction for multiple comparisons. For example, 62.2% of respondents in the AI-ready ITAM group reported using runtime asset data, compared with just 9.4% in the optimized group.
"AI is raising the standard for what enterprise ITAM must deliver," Hathcock said. "Accurate asset inventories remain essential, but they are no longer enough. Our research identifies substantial differences between optimized and AI-ready ITAM practices, especially in the quality, timeliness, and integration of asset intelligence. As organizations expand AI-driven operations, they need information that people and automated systems can reliably use to make informed, accountable decisions."
Key findings include:
- AI demands more from asset intelligence. ITAM must increasingly account for dynamic, interconnected resources, including cloud services, SaaS, containers, APIs, datasets, AI models, and other AI-related assets. Supporting AI-driven operations requires more than visibility; it depends on accurate, timely, normalized data and the relationships and dependencies needed to inform operational decisions.
- Trusted asset data has become a cross-functional requirement. ITAM information increasingly supports IT service management (ITSM), IT operations, security, FinOps, governance, procurement, change management, and AI-enabled operations. Organizations must reconcile fragmented data sources and provide consistent, reliable asset information that teams and systems can use.
- AI readiness extends beyond inventory and visibility. Six statistically significant relationships distinguish the respondent-reported AI-ready maturity group, including stronger FinOps integration, greater confidence in AI governance controls, and more frequent automated pre-change impact analysis. Together, these findings highlight the growing importance of connecting trusted asset intelligence to operational decisions and governed action.
As AI assumes a greater role in enterprise operations, organizations must move beyond traditional ITAM optimization toward asset intelligence that supports timely decisions, cross-functional coordination, and governed automation.
EMA will host a free webinar, ITAM in the Age of AI: How AI-Ready Organizations Stand Out, on
This independent research was sponsored by Virima and Xurrent.
The complete findings are available in "ITAM in the Age of AI: EMA's 2026 Maturity Benchmark."
About EMA
Founded in 1996, EMA is a leading IT research and consulting firm dedicated to delivering actionable insights across the evolving technology landscape. Through independent research, market analysis, and vendor evaluations, we empower organizations to make well-informed technology decisions. Our team of analysts combines practical experience with a deep understanding of industry best practices and emerging vendor solutions to help clients achieve their strategic objectives. Learn more about EMA research, analysis, and consulting services at www.enterprisemanagement.com and follow them on X and LinkedIn.
Media Contact:
Raleigh Gould
Enterprise Management Associates
[email protected]
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SOURCE Enterprise Management Associates
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