Artificial intelligence (AI) software is the fastest-growing cost in many IT estates, and it’s the one IT asset management (ITSM) and IT service management (ITSM) teams can see the least clearly. It shows up in budgets, in cloud bills, and in tools staff bought without involving IT. The Flexera 2026 State of ITAM Report, based on a survey of 512 professionals with IT asset management responsibilities in early 2026, shows how wide the gap has become. Tracking or adopting new AI applications is now the top challenge overall, named by 84% of respondents. Almost half (47%) expect to increase their focus on AI software over the next few years. Only 31% say they have accurate visibility into AI software today.
The 31% figure is what concerns me. It’s a low number, indicating that 69% of organizations lack accurate visibility into the AI software they’re running. You can’t control spend you can’t see, and 59% of them say their wasted AI spend went up over the past year. Most teams are adding AI tools faster than they can track them.
Asset management is being rewritten around FinOps and cloud
Where IT asset management sits on the org chart is changing. Fewer teams report to the CIO or CTO than a year ago, down 6 percentage points, while teams reporting to cloud management or FinOps rose by 5 percentage points. Almost four in five organizations (78%) now run a dedicated FinOps team, and the day job increasingly involves cloud and financial work alongside traditional license management. Three-quarters of ITAM professionals manage cloud software licenses, 64% handle SaaS licenses, and 51% support visibility into AI spend.
The blurring between asset management and FinOps is clearest in who owns cloud savings. Responsibility for generating software savings in the public cloud used to belong mostly to ITAM and software asset management (SAM) teams. A year ago, 59% of organizations said asset management owned those savings. This year it’s 47%, a 12-point drop that leaves ITAM only a percentage point ahead of FinOps. Two functions are now working on the same cloud spend, and the report notes that their day-to-day contact dropped even as their reporting lines moved closer together. That concerns me, because when two functions work the same spend with less contact between them, you get duplicated effort and gaps that no one owns.
Cost optimization has pulled away from everything else
For all the attention on AI, the strongest theme this year is cost. Optimizing software spend is the top SAM priority by the widest margin Flexera has recorded since the survey started five years ago, more than 19 percentage points ahead of the next item on the list.
Most of it is the usual cost levers: reusing licenses, renegotiating contracts, and rightsizing across on-premises, SaaS, and cloud. Planned investment is falling across most software categories, except AI, suggesting asset management teams are bracing for tighter budgets while still having to fund the one area they can’t afford to ignore.
Audits haven’t gone anywhere
The older pressure hasn’t eased either. Nearly half of organizations (48%) received a software audit in the past year. Of those, 64% came from Microsoft and 50% from Oracle. Over three years, 44% of respondents spent more than $1 million on audits. As software use rights get more tangled, accurate inventory remains at the top of the asset management to-do list, according to 78% of teams.
And the same challenge reaches into service management. Your CMDB is only as good as the asset data feeding it, and incident, change, and problem management all depend on knowing what you have and how it connects. When asset data is patchy, change assessments miss dependencies they should have caught, incidents take longer to trace, and an unsanctioned AI tool can end up embedded in a business process with no one accountable for it. Shadow AI is both a cost issue and a service risk.
Where asset management goes from here
The report describes a function under real strain. Asset management is being handed cloud and FinOps work, told to hold the line on audits, and is now expected to bring a fast-moving new cost category under control, usually without extra headcount or tooling to match.
The teams that come through the next year in decent shape will be the ones that fix visibility first, because accurate and current data on what software exists, who uses it, and what it costs is what everything else depends on, from audit defense to service reliability. The teams still running on stale spreadsheets and partial discovery will continue to find out about their spend and risk after the fact.
Read the full asset management report
The Flexera 2026 State of ITAM Report is free to read if you own any part of software or cloud spend.
Asset Management FAQs
AI spend visibility is the ability to identify, track, and manage all AI-related software subscriptions, licenses, and cloud services used across an organization. Without accurate visibility, ITAM teams struggle to control costs, reduce waste, and manage compliance risks.
AI applications are being adopted rapidly across business departments, often without IT involvement. This creates “shadow AI,” making it difficult for IT asset management teams to monitor usage, optimize spending, and ensure governance.
Shadow AI refers to AI tools and services that employees or departments use without formal IT approval or oversight. These tools can introduce security, compliance, licensing, and support risks while increasing untracked software spending.
When organizations cannot accurately see AI software usage, they often pay for duplicate subscriptions, unused licenses, or overlapping services. This leads to unnecessary software spend and makes budgeting more difficult.
IT Asset Management focuses on governing software assets throughout their lifecycle, while FinOps concentrates on optimizing cloud spending. As organizations invest more in SaaS and AI, ITAM and FinOps increasingly need to collaborate to manage software costs effectively.
Cloud software spending has grown significantly, making financial optimization a strategic priority. Many organizations are giving FinOps greater responsibility for cloud cost management, which overlaps with traditional ITAM responsibilities.
Accurate asset data improves the quality of the Configuration Management Database (CMDB), helping ITSM processes such as incident, problem, and change management.
A CMDB relies on accurate asset information. If AI software is missing from asset records, organizations may overlook dependencies, create inaccurate change assessments, and introduce unmanaged business risks.
Software audits require organizations to demonstrate accurate licensing and software usage. Maintaining comprehensive asset inventories helps reduce compliance risks, minimize unexpected costs, and prepare organizations for vendor audits.
Large enterprise software vendors, including Microsoft and Oracle, regularly audit customers to verify software licensing compliance. Effective software asset management helps organizations prepare for these audits.
AI applications are still software assets. Organizations need accurate inventories to understand what AI tools are being used, who owns them, what they cost, and whether they comply with security and licensing policies.
Industry reports usually suggest ITAM teams should prioritize improving software visibility, tracking AI applications, collaborating more closely with FinOps, maintaining accurate asset data, and strengthening governance around SaaS and cloud software.
Organizations can reduce AI waste by identifying all AI applications in use, eliminating duplicate subscriptions, reclaiming unused licenses, monitoring adoption, and establishing governance policies before deploying new AI tools.
Software asset management has evolved beyond license compliance. Today it helps organizations optimize costs, support AI governance, improve cybersecurity, strengthen ITSM processes, and make better investment decisions across cloud and SaaS environments.
Sophie Danby
Sophie is a freelance ITSM marketing consultant, helping ITSM solution vendors to develop and implement effective marketing strategies.
She covers both traditional areas of marketing (such as advertising, trade shows, and events) and digital marketing (such as video, social media, and email marketing). She is also a trained editor.
