Artificial intelligence is giving IT Asset Management (ITAM) an unexpected second act. Over the past decade, Software Asset Management (SAM) gradually lost strategic visibility as organizations shifted toward SaaS subscriptions and decentralized technology purchasing. Business units acquired applications directly. Developers consumed cloud services on demand. Employees adopted productivity tools without involving IT. Asset management often became a procurement exercise rather than an operational discipline.
AI changes that equation. Unlike previous waves of SaaS adoption, AI introduces new questions that extend beyond software licensing. Enterprises must understand who is using AI, what enterprise data AI systems access, where AI capabilities are embedded within existing software, which hardware platforms support AI workloads and how AI services align with security, compliance and financial governance.
This is no longer solely a SAM discussion. It is an IT Asset Management discussion that increasingly includes AI Asset Management.
AI Asset Management is not a separate discipline. It is the evolution of ITAM to include AI software, AI services, APIs, embedded AI capabilities and the infrastructure that enables
ITIL has long recognized IT Asset Management as a foundational practice supporting service delivery, risk management and financial accountability. AI does not invalidate those principles. Instead, it expands the definition of an asset. Enterprise IT teams now manage software licenses, SaaS subscriptions, endpoint devices, GPUs, cloud services, AI APIs and embedded AI capabilities that may already exist within licensed enterprise platforms but remain unused or unmanaged.
That broader scope requires organizations to balance Software Asset Management and Hardware Asset Management according to the operating model. A cloud-first enterprise may emphasize software, subscriptions and AI service consumption. Organizations building private AI infrastructure or supporting engineering workloads may place greater emphasis on GPUs, AI-enabled workstations, specialized servers and edge devices. Neither approach is universally correct. Effective ITAM reflects business priorities while maintaining consistent governance across software and hardware assets.
The challenge is compounded by the speed of AI adoption. Business users purchase standalone AI applications with corporate credit cards. Developers integrate AI services directly through APIs. Software providers embed AI capabilities into existing products, creating situations where organizations pay for functionality that is neither activated nor governed. Traditional inventory processes rarely capture these changes.
Today’s IT Asset Management should answer five operational questions:
Collectively, these questions define the foundation of AI Asset Management. They also reinforce, rather than replace, established ITIL practices. Asset Management continues to support Change Enablement, Information Security Management, Financial Management and Configuration Management by providing trusted information about enterprise technology assets.
A practical operational test can help determine whether your current ITAM approach is sufficient. If your organization cannot produce, within 30 days, a validated inventory of AI-enabled software, standalone AI services, AI APIs, AI-supporting hardware, associated software licenses and the enterprise data each can access, your Asset Management practice should expand. The objective is not simply better inventory; it is establishing the visibility, governance and accountability required to support AI adoption at enterprise scale.
Viewed through the broader lens of Enterprise Control Architecture (ECA), IT Asset Management has become more than an operational practice. It is one of the foundational control points that enables enterprises to govern increasingly autonomous technologies. An organization cannot effectively enforce policy, manage risk or optimize AI investments without first understanding its assets, the capabilities enabled by those assets and the data those assets can access.
As AI becomes another component of everyday enterprise technology, IT Asset Management returns to its original purpose: Providing a trusted operational foundation for informed technology decisions.
Regards,
Jeff Orr