How is ITAM being influenced by AI?
AI is transforming IT asset management - from contract ingestion to spend optimisation. Learn how ITAM teams can govern AI, reduce risk and drive value.
Published on 2nd June 2026
AI-powered contract and entitlement ingestion
One of the most time-consuming parts of ITAM, specifically software asset management (SAM), has always been data intake. Contracts, entitlements, purchase orders, license metrics, and use rights arrive in every imaginable format including PDFs, spreadsheets, portals, emails, and scanned documents. Historically, turning this chaos into normalised, trustworthy data required hours, days, or even weeks of manual work. And if this work wasn’t outsourced, often it wasn’t done with the exception of the few most important IT contracts.
AI changes that equation.
With AI-driven automation, contract and entitlement ingestion becomes faster, more accurate, and far less dependent on human effort. Modern AI models can:
- Read and interpret contracts using natural language processing (NLP)
- Extract key terms like quantities, metrics, renewal dates, and product use rights
- Normalise entitlements across vendors and licensing models
Instead of spending my time manually keying in data or validating line items, ITAM and SAM practitioners can focus on exceptions, decisions, and strategy. The result isn’t just efficiency, it’s scale. As software portfolios grow and licensing models become more complex, AI-enabled ingestion is the only way to keep up without becoming overwhelmed and producing costly errors.
Taking Action with AI: From Insight to Outcomes Using Agents
One of the most meaningful shifts AI brings to ITAM isn’t just better analysis; it’s the ability to translate insights into execution through agent‑based automation.
AI agents can act continuously on behalf of ITAM and SAM teams, monitoring environments, evaluating conditions, and initiating actions based on defined policies and guardrails. Instead of relying on dashboards that require constant human review, agentic AI enables ITAM to move from reactive response to proactive control.
In practice, AI agents can:
- Monitor contract, entitlement, and usage data for anomalies or risk conditions
- Identify upcoming renewals, compliance gaps, or optimisation opportunities
- Trigger workflows, notifications, or approvals when thresholds are met
- Recommend or initiate corrective actions, such as reclaiming unused licenses or flagging risky AI usage
This doesn’t remove human oversight, and it shouldn’t, but it does enhance it. ITAM professionals remain responsible for decisions and governance, but AI agents dramatically reduce the time between signal and action. The outcome is a more responsive ITAM function that can operate at scale, even as software portfolios and AI usage grow more complex.
Over time, agent‑driven workflows will become a natural extension of ITAM operations: a digital workforce that handles routine execution so humans can focus on judgement, negotiation, and strategy.
Synthesising Data with AI to Enable Better Collaboration
ITAM has never been isolated. It sits at the intersection of procurement, finance, FinOps, security, legal, cloud, and business stakeholders, and that complexity often leads to siloed data, fragmented conversations, and slow decision‑making.
AI changes this by acting as a synthesis layer across systems, domains, and teams.
Instead of forcing stakeholders to interpret raw reports or reconcile conflicting data sources, AI can aggregate, contextualise, and translate information into shared, decision‑ready insights. Contract terms, entitlement data, consumption patterns, financial impact, and risk signals can be surfaced together in the language each team understands.
This enables:
- Finance to understand cost drivers and ROI without decoding license models
- FinOps to assess cloud environments and overall cloud strategy
- Procurement to negotiate with clarity around usage, entitlements, and risk
- Security and legal to assess AI exposure and compliance implications
- Executives to see a unified view of technology value, cost, and risk
For ITAM and SAM professionals, this positions the function as a hub for cross‑functional alignment, not just a source of data. AI doesn’t just analyse; it connects people, decisions, and outcomes.
As AI continues to mature, the most successful organisations won’t be the ones with the most dashboards. They’ll be the ones using AI to turn complex technology data into shared understanding and coordinated action across the business.
AI software will become a core category of spend and optimisation
AI itself is rapidly becoming one of the fastest-growing categories of enterprise technology spend. This isn’t just about buying AI tools, it’s about a new layer of cost complexity that ITAM and SAM teams must manage.
AI-related spend shows up in many forms:
- Standalone AI applications and copilots
- AI features embedded into existing SaaS products
- Usage-based consumption tied to tokens, prompts, and API calls
- Premium licensing tiers for AI-enabled capabilities
Unlike traditional software, AI costs are often variable, opaque, and difficult to forecast. A team experimenting with generative AI today can quietly become a major contributor to software costs tomorrow.
This is where ITAM and SAM must evolve. Optimisation will no longer focus solely on unused licenses it will require:
- Monitoring AI usage patterns and consumption drivers
- Identifying over-provisioned AI entitlements
- Identifying AI separately in software
- Aligning AI spend to real business outcomes
- Allocating use of AI, and its cost, to respective business units
- Establishing guardrails to prevent “runaway” consumption
In short, AI optimisation will more closely resemble cloud cost optimisation than classic on-premises license management and ITAM/SAM professionals along with FinOps professionals are uniquely positioned to lead and contribute to this effort.
Shadow AI will create new governance and compliance risks
Years have been spent talking about shadow IT. Now, we’re entering the era of shadow AI.
Employees can sign up for AI tools in seconds, embed generative AI into workflows, or connect external models to corporate data and do so often without IT, security, or procurement ever knowing. The risks are real:
- Sensitive data exposure
- Unapproved AI models trained on proprietary information
- Licensing and usage violations
- Regulatory and compliance gaps
AI-driven discovery will become essential. Just as ITAM tools evolved to detect SaaS sprawl, AI-aware asset management must identify:
- AI-enabled applications already in use
- Embedded AI features within licensed software
- Data flows between AI tools and enterprise systems
For ITAM/SAM managers, governance will mean enabling innovation safely, with visibility, policies, and controls that scale as fast as the technology itself.
AI Will Elevate ITAM/SAM from Operational to Strategic
- Advising the business on AI investment trade-offs
- Supporting procurement with AI-specific licensing insights
- Partnering with security, legal, and finance teams on AI risk and compliance
- Providing executives with clear visibility into AI value, cost, and exposure
AI will not replace ITAM or SAM it will simply amplify it, and organisations that get this right will treat ITAM/SAM as a strategic function, not a back-office necessity.
Closing
AI is already automating the work that slows us down, introducing new categories of spend we must manage, and elevating ITAM/SAM roles across the organisation.
For ITAM and SAM professionals willing to adapt, AI represents an opportunity: to move faster, think bigger, and play a more strategic role in how organisations adopt and govern technology.
The job isn’t disappearing. It’s becoming more important than ever.
Source
Image Credit
Imam Fathoni via Vecteezy