AI is changing the once predictable economics of software use. Costs are now frequently linked to consumption, and this is creating new management and governance challenges. The answer isn’t to slow AI adoption, but to ensure costs are aligned to business value – and the solution can be found in FinOps.
AI is introducing a new pricing model
Historically, Microsoft licensing has largely been user-based, but AI is introducing a growing mix of consumption-based services alongside this.
The traditional model continues for Microsoft 365, including Microsoft 365 Copilot, which is characterised by an annual commitment linked to user numbers. Adding users increases costs linearly, so expenditure is predictable and you can could make an accurate prediction of next year’s spend now.
With AI, organisations typically pay for activity rather than people. The organisation pays for prompts, responses, tokens, compute, storage, orchestration, retrieval, and automation.
Examples include Azure OpenAI tokens, Copilot Studio message consumption, Azure AI Foundry model inference, Microsoft Fabric capacities, Azure AI Search, AI agents making API calls, vector search, and embeddings. It’s like the difference between paying a fixed monthly gym membership, versus paying for the time you’re actually in the gym.
How costs can unexpectedly spiral
AI differs from traditional software because usage itself drives spending. Organisations typically start small, but as use becomes widespread the resulting costs can be an unwelcome surprise.
With a handful of users making twenty prompts a day, costs are barely noticeable. Scale that to a thousand employees, or hundreds of AI agents operating around the clock, and consumption increases dramatically.
It wasn’t a conscious increase in spending – the organisation simply embraced AI. Costs aren’t rising because something has gone wrong, but because adoption is successful.
Nevertheless, it creates a management problem.
Where costs spiral
There are four key areas where costs can soar.
Hidden usage growth
Employees start using AI for drafting, summarising, coding, meetings, and search. At this stage they’re probably using Microsoft 365 Copilot (which isn’t consumption based) and encouraged to experiment widely.
But more advanced services, such as Microsoft Copilot Studio, Power Automate, and Azure AI services often include consumption-based costs. While individual AI requests may cost very little, widespread experimentation and adoption can turn thousands of low-cost interactions into a significant operational expense.
Agent-driven consumption
This is probably the least understood issue. While human use tends to be limited to their working day, AI agents may be working around the clock – continuously monitoring inboxes, reviewing documents, triggering workflows, querying databases, and generating API calls.
One poorly designed agent can generate enormous consumption.
Lack of guardrails
Without the right governance in place, AI experimentation can soon become a production cost.
Established governance models aren’t usually AI appropriate. They don’t say who can – and with what approval – create agents, connect agents to data, use premium services, and deploy automations.
While you don’t want to stifle innovation, you do need governance.
Limited visibility
Perhaps the biggest issue is visibility. Most organisations know their Azure spend and the parts of the business that drive it, but few know which departments generated this month’s AI bill, which agents generated value, and which consumed resources unnecessarily.
The AI cost trap
The combination of hidden use, consumption pricing, insufficient governance, and poor visibility can feel like a huge cost trap.
But the trap isn’t high cost – it is invisible cost, or growing costs that are disconnected from growing value.
The issue isn’t that a department creates ten or twenty agents, but often that ownership is unclear, no one measures them, and no one retires non-performers. A year on, consumption continues and nobody knows why.
You probably recognise that this is exactly what happened with early cloud deployments. The answer wasn’t to slow adoption, but to improve management. Organisations adopted FinOps to provide greater cost visibility, clearer accountability, stronger governance and continuous optimisation to align spend with business value.
AI FinOps: 11 practical ways to keep control
If you’re unfamiliar with the term, FinOps is a contraction of Financial Operations. It is both a cultural practice and operational framework that brings together IT, operational teams, and finance to manage and optimise spending. Its aim isn’t simply to reduce cost, but to maximise business value by balancing speed, cost, and quality.
How might this work in practice? We’ll look at 11 practical mechanisms that can be put in place to support:
- Financial control
- Operational governance, and
- Business value realisation.
Remember: the objective isn’t to limit use of AI, but to ensure AI adoption remains predictable, measurable and aligned with business value.
Financial controls
1. Budgets: Set budgets for AI consumption at departmental and project level. These shouldn’t necessarily be hard limits, but they should be thresholds that trigger investigation if costs escalate. By configuring automated alerts you won’t have to wait for period-end to discover unexpected spending.
2. Usage dashboards: You can’t manage what you can’t see, so provide dashboards showing AI consumption by department, application, agent or workload, together with trends over time. Visibility makes it easier to spot unusual patterns, identify opportunities for optimisation and demonstrate value.
3. Chargeback or showback: It’s helpful if business units understand the financial impact of their AI usage. Some organisations chargeback costs directly to departments, while others report the costs without transferring budget (‘showback’). Both encourage more informed decision-making and discourage waste.
4. Portfolio reviews: Just as you regularly review software and other resources, you should proactively review your AI estate. Consider:
- Which AI services are producing the greatest business value?
- Are there duplicate AI agents solving the same problem?
- Are some workloads using more expensive models than necessary?
- Which production services should now be retired?
Guardrails and governance
5. Role-based access: Not everyone needs access to every AI capability – many may only need Microsoft 365 Copilot. Match access to need, with perhaps only selected power users able to build agents.
6. Premium model approval: AI platforms often offer a choice of models with different capabilities and costs and the most capable may not always be needed. Require approval before teams deploy premium or higher-cost models, especially where there are lower-cost alternatives. Both these measures are likely to build upon existing governance policies.
7. Agent lifecycle management: Like applications, agents shouldn’t exist indefinitely. Every agent should have an owner, a documented purpose and periodic review.
Otherwise, the organisation will accumulate ‘orphaned’ agents that consume resources long after their business value has disappeared.
8. Retiring unused agents: Many agents begin life as experiments, especially in the early days. While some will prove valuable, others will not. Regularly review agents and retire those that are no longer in use or delivering measurable benefit. Every unnecessary agent represents avoidable complexity and, possibly, ongoing cost.
Business value
9. Value measurement: This is probably the most important control of all. Don’t just measure consumption or spend, measure outcomes. Consumption and cost are only meaningful relative to business benefit. Examples include hours saved, reduced waiting times, improved customer satisfaction, faster response times, and fewer manual processes.
10. Departmental ownership: Establish this simple principle: no AI workload without a business owner. Every department deploying agents should understand what they’re spending, why, and the benefits expected in return.
11. Education: Just as important as controls is employee education. Well-informed users will make better decisions, improving both cost efficiency and governance. Ensure that employees understand:
- when AI should be used
- which tools are approved
- key policies and their reasons
- how different services incur costs, and
- why using AI efficiently matters.
Looking ahead
Historically, IT has managed infrastructure, licences, and devices. but increasingly IT will need to govern AI consumption. Not because it is a danger, but because it is becoming an operational utility much like electricity or cloud compute. Every prompt, every agent and every workflow may have a marginal cost, and although individually these costs are tiny, collectively they can become significant.
The organisations that thrive won’t necessarily be those spending the most on AI, they’ll be the ones spending smartest. They will understand exactly where AI is creating value and where it isn’t, characterised by:
- intentional, considered use of AI
- predictable, scalable adoption, and
- clear visibility of cost versus impact.
The organisations that develop these capabilities now will be able to scale AI confidently, sustainably and with far fewer surprises.
How Cloud Direct can help
Cloud Direct helps organisations put these principles into practice, with advisory, optimisation, and management services. Request a call with a subject matter expert, or view our AI Productivity services, to understand how Cloud Direct can help you support AI adoption with the right controls.
