Expected reading time: 10 minutes
Can AI Be Managed Like Software?
AI is moving fast — but organizations believe they already know how to manage it.
The tools come from familiar vendors, integrate into existing platforms, and are contracted in ways that resemble traditional software. On the surface, nothing fundamentally changes.
So, the response is predictable:
- Apply existing licensing models
- Rely on established budgeting practices
- Use familiar vendor governance
Control should follow.
The underlying belief is simple:
If software cost was manageable before, AI cost should be too.
The Reality: AI licensing is not one model. It’s at least four
AI cost is not controlled the way software cost was.
The assumption that familiar mechanisms will hold breaks down almost immediately.
AI licensing may look similar on the surface, but it does not behave like a single, predictable model. Instead, depending on the tool and vendor, it operates across multiple fundamentally different commercial structures, each with its own cost drivers, risk profile, and governance requirements.
Treating them as one category does not simplify management.
It creates blind spots.
Let’s zoom in on the 4 models.
Pure AI Cloud Services:
Pure AI Cloud Services such as Azure OpenAI or Amazon Bedrock charge directly based on token and API usage. Costs scale directly with the volume of requests and the size of inputs and outputs. There is no inherent hard spend limit in standard on-demand deployments. A development team experimenting with prompts at scale, without oversight, can generate a significant bill before anyone notices.
Embedded AI Functionality:
Embedded AI Functionality such as Microsoft 365 Copilot or Google Workspace with Gemini operates on a familiar user-license basis. The cost driver is headcount. This model remains relatively predictable and aligns well with existing SAM disciplines but it creates a new governance challenge: organizations must actively assign licenses to users who generate value, not simply provision seats on request.
Hybrid Models:
Hybrid Models such as SAP Joule or Adobe Creative Cloud with Firefly credits combine a user-license baseline with a consumption layer on top. Vendors include AI capacity units within license tiers and bill additional usage on top. This dual structure forces organizations to apply both SAM and FinOps simultaneously. Yet, most teams fail to understand how these two layers interact at the point of purchase.
Pure Consumption Models:
Pure Consumption Models such as Salesforce Agentforce on Flex Credits operate on a fully usage-based logic, tied to business actions: agent resolutions, automation runs, requests processed. Workflow design directly drives unit economics. Poorly designed processes drive consumption costs up. Most platforms impose no technical limit, so vendors charge expensive pay-as-you-go rates whenever usage exceeds pre-purchased credits, unless organizations negotiate additional capacity in advance.
Each model demands a different governance response. Applying SAM logic to a pure consumption model, or FinOps logic to a user-based tool, produces the wrong answer.
Why this keeps happening?
If AI behaves differently, why do organizations still manage it the same way?
Because everything around it reinforces the illusion that they should.
It starts with perception:
AI tools look and feel like the software organizations that are already known; same vendors, same interfaces, same procurement processes. The difference in cost behavior is not immediately visible.
Then organizational dynamics take over:
Procurement works with annual budgets and fixed-volume assumptions. Finance expects predictable invoices. Engineering drives usage decisions. Organizations split cost ownership, and assign accountability to no one.
And human behavior reinforces it further:
Teams default to familiar models because they work — or used to work. Changing governance requires coordination across functions, and most organizations optimize for speed of adoption, not control of outcomes.
The result: organizations govern AI with outdated assumptions, where long after those assumptions stop being valid.
The hidden risk in each model
Visibility into which model you are dealing with is only the starting point. The real exposure lies in the details. In each of the four models, those details have a habit of surfacing at the worst possible moment.
In each model, the same pattern repeats:
For pure AI cloud services, teams drive risk through unconstrained, ungoverned usage. Development and engineering teams often begin experimenting with AI APIs without formal procurement involvement. Spend scales with usage, and without alerting mechanisms or cost ownership assigned at the team or use-case level, significant overruns can accumulate before Finance sees the first invoice. The optimization levers, prompt rightsizing, model tier selection, deployment architecture, are technical decisions with direct financial consequences. The people making those decisions rarely own the costs they create.
For embedded AI functionality, the governance risk is subtler but no less real. License-based models feel controllable, but assigning licenses does not guarantee value.
For example, evaluating the actual impact of Copilot adoption, use-case by use-case, team by team, is an exercise most organizations have not yet built the capability to perform.
For hybrid models, the danger is the interaction between layers. SAP distributes AI units across multiple packages and automatically triggers overage invoices through its “Excess Use PPU” mechanism. If organizations fail to limit consumption to selected users and pre-approved use cases during the pilot phase, they enter negotiations with SAP from a position of weakness and not clarity.
For pure consumption models, the critical risk is overcommitment without evidence. Vendors often push organizations toward large pre-purchase credit packages with compelling unit economics. But those economics only hold if consumption volumes materialize as projected. Piloting before scaling, and negotiating a mix of pre-committed volume and pay-as-you-go for fluctuations, requires a clear view of actual usage patterns, which most organizations do not have at the point of contract signature.
What mature organizations do differently?
AI licensing does not fit cleanly into either SAM or FinOps; it sits at the intersection of both.
Mature organizations recognize this early and design their governance accordingly.
They do not treat SAM and FinOps as separate disciplines.
They use both deliberately, and at the right moment.
Average organizations:
- Apply SAM practices to all AI tools, even when cost is driven by consumption
- Apply FinOps practices in isolation, without linking them to contractual structures
- Run SAM and FinOps in parallel, with limited coordination
- Leave gaps between license ownership and consumption ownership
The result: visibility without control, or control without insight.
Mature organizations operate differently:
They explicitly separate where SAM applies, where FinOps applies and where both are required.
- For embedded AI tools, they apply SAM rigor
→ license lifecycle management, usage tracking, and value realization - For pure AI cloud services, they apply FinOps discipline
→ real-time consumption monitoring, cost allocation, and engineering accountability - For hybrid and consumption-based models, they integrate both
→ linking contractual entitlements with actual usage behaviour
But the difference is not just in applying both.
It is in connecting them.
- They align procurement decisions with consumption behaviour
- They ensure that engineering teams understand cost implications
- They assign ownership where decisions are made, not where invoices arrive
Most importantly: they bring SAM and FinOps into the same conversation at the right moment in the AI lifecycle.
How to regain control: A phased approach
Governance does not come from tooling alone. It comes from embedding the right practices at each stage of the AI lifecycle.
During Purchase:
Organizations must set the foundation early. Procurement teams still rely on RFx templates designed for traditional software, and those templates fail for AI licensing. Teams need to update them to capture AI-specific parameters: metric definitions, consumption tracking mechanisms, overage triggers, and contractual protections for budget predictability.
Organizations often accept vendor claims like “you only pay for what you use.” They should challenge that assumption. Moreover, they need to understand how vendors measure consumption, trigger overages, and enforce protections before signing the contract, not after.
During Onboarding:
Organizations must design the governance architecture before they scale.
They should map each AI solution to one of the four licensing models. They should assign cost ownership at the use-case level to product owners who understand how their technical decisions drive financial outcomes.
Piloting before scaling is not a best practice, it is a commercial necessity. The organization that commits to large volumes before it understands its actual usage patterns will inevitably negotiate from a weaker position at renewal.
During Ongoing Monitoring:
Teams must continuously cross-check consumption data against both vendor-provided and independent usage reports.
They should actively track the gap between expected and actual utilization and use that gap to recalibrate governance structures, roles, and responsibilities.
Organizations must actively use vendor tools to set limits and control spend, otherwise, those tools remain passive dashboards with no real impact.
What Leadout Does
At Leadout we closely work with organizations. We bring independent expertise, structured processes, and cross-client pattern recognition to close critical governance gaps.
We work across the full asset lifecycle. From supporting during RFx activities, ensuring your contract language reflects commercial reality, to running periodic reviews. We keep your compliance position visible before a vendor makes it visible for you.
At the core is our periodical vendor check service. It is an independent review of how vendors measure your consumption against your contracts. Not a repackaging of portal data. A genuine, data-driven second opinion that makes the difference between spotting an anomaly and recognizing a pattern.
Key Takeaways
- AI licensing is not one model, treating it as one guarantees loss of control
- Cost is driven by consumption behavior, not just licenses
- Familiar governance models create blind spots, not stability
- Ownership is the difference between visibility and control
- Piloting before scaling in AI licenses is not optional, it is your strongest commercial advantage
Curious how this applies to your organization? Let’s talk, schedule a call with us.
Matthias@leadoutsolutions.com Nick@leadoutsolutions.com
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