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Overview of AI and IT Asset Management

In today's rapidly evolving technological landscape, the adoption of Artificial Intelligence (AI) by enterprises is gaining momentum. However, a recent study conducted by Enterprise Management Associates (EMA) has revealed a concerning divergence between the rate of AI integration into business operations and the readiness of IT asset management (ITAM) systems to support this transformation.

The Research Findings

The report titled ITAM in the Age of AI: EMA's 2026 Maturity Benchmark analyzes data from a survey of IT professionals across North America with organizations having at least 1,000 employees. According to the findings, while a notable 71.9% of respondents reported that their companies have adopted AI to a broad extent or have reached advanced levels of AI deployment, only 22.2% feel that their ITAM practices are adequately prepared for AI.

This disparity highlights a crucial reality: simply having advanced AI technologies does not guarantee that organizations are equipped with the asset management strategies necessary for effective integration. The survey puts into perspective the challenges that arise when organizations assume that mature ITAM practices will automatically extend to cover AI's unique demands. Among the surveyed professionals, approximately 26.1% described their ITAM approaches as optimized. However, the subset of companies labeled AI-ready showcased significantly more robust capabilities across six essential dimensions, all of which showed significant statistical relevance.

Key Insights from the Study

One particularly startling finding from the research indicates that 62.2% of AI-ready organizations utilize runtime asset data compared to mere 9.4% in those with optimized practices. This statistic points to the urgent need for organizations to elevate their IT asset strategies beyond conventional frameworks and ensure they meet the dynamic requirements posed by AI technologies.

1. The Complex Demands of AI on ITAM

As AI continues to evolve, the expectations for ITAM frameworks are becoming more complex. Organizations must account for a variety of interconnected resources including cloud services, Software as a Service (SaaS), containers, APIs, datasets, and AI models. To support the operational demands of AI, companies will need more than just visibility; they will require accurate, timely, and normalized data along with an understanding of how these assets relate to one another to inform critical operational decisions.

2. The Need for Trusted Asset Data

The report emphasizes that trustworthy asset data is not just an IT concern; it has become a crucial requirement across various functions such as IT service management (ITSM), security, governance, and procurement. Thus, organizations are tasked with reconciling fragmented data sources to ensure that their teams have access to consistent and reliable asset information. This shared information will bolster cross-departmental collaboration and enable efficient decision-making across the board.

3. Expanding AI Readiness Beyond Simple Inventory

Indicators of AI readiness extend beyond mere asset visibility, falling into significant relationships within mature practices. These include improved integration with financial operations, heightened confidence in the governance of AI, and the more frequent implementation of automated analysis prior to making changes. These elements collectively underscore the need for organizations to connect trusted asset intelligence directly to their operational decisions, reinforcing the necessity for governed action.

Moving Forward

As AI assumes an increasingly central role in enterprise operations, organizations must evolve their IT asset management strategies from traditional optimization to frameworks capable of supporting the timely and informed decision-making required in today's AI-driven environment. To further explore these findings, EMA will host a free webinar entitled ITAM in the Age of AI: How AI-Ready Organizations Stand Out on November 3, 2026, where key insights will be discussed, along with practical strategies that organizations can adopt to enhance their asset intelligence in alignment with AI-driven operations.

With the support of independent research funded by organizations like Virima and Xurrent, the complete study can offer more insights about transforming IT asset management practices to meet the demands of a digital-first world. For those interested in the findings, registration for the insightful webinar can be found on EMA's website.

Conclusion

Organizations stand at a crossroads, where technological adoption must align with effective management practices. The findings from EMA’s 2026 Maturity Benchmark not only reveal the disconnect between AI adoption and IT asset management readiness but also serve as a clarion call for businesses to innovate their processes. As the demand for better asset intelligence continues to grow, the strategic moves made today will determine the success of tomorrow’s AI initiatives.