How to keep AI costs under control as agentic usage grows

Rapid AI adoption is making cost control more complex

AI is getting cheaper at the unit level. That does not mean enterprise AI bills are getting smaller.

The reason is straightforward: usage is expanding faster. Companies are deploying more models, giving more employees access to AI, and moving from simple assistants toward agentic systems that can perform multi-step tasks. Those systems may make several model calls, use external tools, retrieve data, and retry tasks before producing an acceptable result. A lower token price matters, but total consumption matters more.

AI sprawl compounds the problem. Different business units may adopt separate applications, models, and vendors without a complete enterprise view of what is being used. According to a Flexera study, roughly three-fifths of IT professionals said AI overspending had increased, while more than two-thirds said they lacked visibility into AI software usage. That combination should concern executives: spending is growing while many organizations still cannot clearly see what is driving it.

The management objective therefore needs to move beyond reducing model costs. Executives need to know what the organization is buying, who is consuming it, what business process it supports, and what result the company receives in return.

This becomes more important as agentic AI expands. An autonomous workflow can consume substantial resources while completing one business task. A cheap model that repeatedly fails and retries may ultimately cost more than a more capable model that finishes the task correctly on the first attempt. The relevant question is no longer simply, “How much does this model cost?” It is, “How much does a successful business outcome cost?”

For the C-suite, this changes the economics of AI investment. Cost discipline should not mean restricting useful adoption. It should mean directing capacity toward activities that create measurable value. Companies that understand usage at the workflow level can scale productive applications while reducing low-value consumption.

The long-term opportunity remains significant. Declining model prices can make sophisticated AI available across more of the enterprise. But lower prices can also stimulate much greater usage. Businesses that build financial and operational controls early will be better positioned to take advantage of that growth without losing control of the economics.

Companies need clear visibility into AI usage before they can control spending

You cannot manage AI spending effectively if you do not know what is consuming the budget. Visibility is therefore the starting point.

OpenAI, in the five-step AI cost-management approach, recommends that enterprise leaders identify who is using AI, which products and models they are using, how much capacity those systems consume, and what work the usage supports. As OpenAI put it, “Without that visibility, a growing bill is hard to interpret.”

The important part is connecting consumption to business activity. Knowing that a department consumed a certain number of tokens is useful for accounting, but it says little about business value. Executives should also be able to see whether that consumption supported customer service, software development, research, sales, finance, or another workflow, and what the organization achieved as a result.

This creates a much stronger basis for decisions. A large AI bill is not automatically a problem. Spending that increases because an AI system is successfully handling more customer cases or accelerating a critical engineering process may be economically rational. Conversely, relatively modest spending can still be wasteful if it supports duplicated tools, abandoned experiments, excessive retries, or applications with no measurable benefit.

Visibility should also cover the full cost of an AI workflow rather than just the headline model price. Agentic systems can make repeated model requests, call external tools, access data, and perform several attempts before completing a task. Executives therefore need enough information to understand consumption patterns and detect workflows where costs rise much faster than useful output.

Central oversight does not require centralizing every AI decision. Business units still need room to experiment and find productive applications. The objective is to create common measurement standards so leadership can compare projects, identify unnecessary duplication, and decide which applications deserve additional capacity.

This is where better cost visibility becomes strategic rather than administrative. Once leaders can connect AI consumption with individual workflows and accepted outcomes, they can distinguish expensive AI from unproductive AI. Those are not the same thing.

The result is a more useful approach to budgeting. Instead of imposing broad spending limits, companies can direct resources toward AI systems that produce measurable results and investigate those that do not. As adoption accelerates, that level of visibility becomes fundamental to scaling AI with financial discipline.

Measure the total cost of a successful AI outcome

The cheapest AI model is not necessarily the least expensive option for a business.

Model pricing usually shows the cost of processing tokens, the units of text or data a model consumes and produces. That metric is useful, but it does not capture the full cost of completing a task. If a cheaper model makes more mistakes, requires repeated attempts, or depends on additional tool calls, the final cost of producing an acceptable result can increase.

OpenAI recommends evaluating models based on the work they need to perform and measuring the complete cost of reaching the required outcome. That includes model and tool usage, the number of attempts, and the completion rate. This moves the discussion from price per unit toward cost per useful result.

The distinction becomes increasingly important with agentic AI. An AI agent may perform several steps before completing an assignment. It can retrieve information, call other software, generate an answer, evaluate its progress, and try again when something fails. Every additional operation can increase consumption. A model with a higher initial price could therefore deliver better economics if it completes the workflow with fewer attempts and higher reliability.

Executives need metrics that reflect the actual job. OpenAI gives two clear examples. In customer support, a company could calculate AI cost per resolved case. In software engineering, it could measure the cost of producing a tested change that successfully passes review. These measures connect technical performance with outcomes business leaders can evaluate.

Cost should then be considered alongside value. OpenAI recommends measuring benefits including time saved, shorter cycle times, protected revenue, avoided risk, and additional capacity created. Organizations can examine improvements in decision-making and the number of tasks completed.

This approach changes model selection. Procurement teams should not automatically choose the lowest-cost model, while technical teams should not automatically select the most powerful one. The appropriate model is the one that delivers the required quality, reliability, speed, and risk profile at an acceptable total cost.

For C-suite leaders, the practical goal is unit economics for AI: determine what a successful outcome costs and what that outcome is worth. Once those numbers are credible, decisions about scaling, changing models, or ending a workflow become considerably clearer.

Executives need a common measurement of AI’s business value

Enterprise confidence in AI is increasing, but confidence is not evenly distributed across leadership teams.

According to a Protiviti study, CIOs and IT decision-makers reported greater confidence in AI’s ability to drive revenue growth than CEOs and board members. What matters is the gap itself: executives responsible for implementing technology can see its potential differently from those accountable for broader corporate performance and capital allocation.

That difference is understandable. A CIO may see faster development, automated processes, or higher employee productivity. A CEO or board member is more likely to ask how those improvements affect revenue, operating costs, risk, margins, or strategic capacity. Both perspectives can be valid, but they require a common measurement framework.

This is why AI programs need to progress beyond adoption metrics. The number of employees using an AI application or the number of prompts submitted tells leadership how much the technology is being used. It does not establish whether the company is receiving enough value from that usage.

OpenAI recommends looking at outcomes such as time saved, reduced cycle time, revenue protected, risk avoided, and capacity created. Improved decision-making and tasks completed are potential measures of value. The appropriate metric will depend on the workflow. Customer service can focus on successfully resolved cases, while engineering teams can evaluate tested changes that pass review.

Executives should also be careful with productivity claims. Saving employees several hours does not automatically create an equivalent financial return. The economic benefit depends on what happens to the saved capacity. If employees use it to increase output, improve quality, serve additional customers, or accelerate valuable projects, the benefit can become meaningful. If working practices do not change, nominal time savings may have limited financial impact.

The same discipline applies to revenue attribution. AI may contribute to a sale, accelerate a product release, improve retention, or reduce service delays without being the sole cause of the financial result. Leadership teams need measurement methods that recognize AI’s contribution without overstating causality.

A shared set of outcome metrics can reduce the confidence gap between technology leaders and the rest of the C-suite. CIOs gain stronger evidence for scaling successful systems. CEOs, CFOs, and boards gain clearer information for allocating capital and evaluating returns.

The objective is not to prove that every AI initiative succeeds. Some experiments will fail, and that is part of developing the technology. The more important capability is identifying failure early, measuring successful applications consistently, and directing greater investment toward workflows that demonstrate durable business value.

Governance is essential for controlling AI cost and risk

AI governance is becoming a practical requirement for scaling enterprise AI. As systems gain greater access to company data, applications, and tools, organizations need explicit rules governing what AI can access, what it can do, and when human approval is required.

OpenAI describes governance as the “operating layer that determines which AI work can scale.” In practice, this means setting boundaries around the context and data available to large language models, identifying which applications and tools they can use, and defining the actions they are permitted to perform. Organizations should also specify who approves higher-risk activities and when additional computing or usage capacity can be granted.

These controls have direct financial implications. Agentic systems can perform multi-step processes with less human involvement, potentially increasing the number of model calls, tool interactions, and automated actions. If permissions and consumption limits are poorly designed, costs can expand without a corresponding increase in business value. Governance gives companies a structured way to determine which workflows deserve greater autonomy and resources.

The level of control should reflect the level of risk. Everyday applications that improve routine productivity may justify broad employee access. Workflows involving sensitive corporate information, regulated data, financial decisions, customer actions, or material operational consequences need stronger permissions, monitoring, and approval requirements. This allows enterprises to expand useful AI adoption without applying the same restrictions to every application.

For executives, governance should connect financial management with security, compliance, accountability, and operational performance. Cost limits alone will not address whether an AI system is accessing inappropriate information or taking unacceptable actions. Likewise, security controls alone do not establish whether a workflow remains economically worthwhile. Both questions need to be considered when deciding whether to scale.

Clear ownership is particularly important. Every material AI workflow should have accountable business and technical owners, defined permissions, measurable outcomes, and escalation procedures. When higher-risk actions require approval, organizations should establish in advance who has that authority. The same principle applies when a successful workflow needs additional capacity.

Good governance does not require restricting AI experimentation across the organization. OpenAI suggests enterprises can provide broader access to everyday functions that improve productivity while making more selective strategic investments in higher-value opportunities. Executives can therefore maintain experimentation while applying stronger oversight as AI systems become more consequential.

The objective is controlled scale. As AI becomes capable of performing more work autonomously, organizations that establish clear permissions, accountability, and spending controls will be better equipped to increase adoption while keeping financial and operational exposure within acceptable limits.

Manage AI investments as a portfolio and scale what demonstrates value

Enterprise AI investment should become more selective as adoption matures. Running experiments is useful for discovering opportunities, but funding every initiative at the same level is unlikely to produce efficient results. Companies need a process for deciding which workflows should expand, which should remain experimental, and which should be discontinued.

OpenAI recommends managing AI investments as a broader portfolio. One important distinction is between widely available productivity applications and more strategic uses of AI. General tools can improve common activities across the workforce, while targeted investments can use proprietary company information, specialized processes, or unique organizational capabilities to create more differentiated value.

The strongest opportunities share several characteristics. According to OpenAI, “The strongest candidates are workflows that repeat at meaningful scale, have clear ownership, and can be measured for quality, risk, and business value.” These conditions matter because an AI system that performs a frequent task has more opportunity to generate material returns, while clear ownership and measurable outcomes make it easier to determine whether those returns actually exist.

Executives should apply consistent criteria when allocating capital. A workflow might be evaluated by completion rates, quality, operating cost, employee time saved, cycle-time reduction, revenue protected, risk avoided, or additional capacity created. The metrics should fit the business objective rather than forcing every AI project into the same return model.

Proprietary information can also influence investment priorities. OpenAI suggests companies make strategic bets based on information specific to their organizations. An AI workflow informed by internal knowledge, processes, or data may address business requirements that generic applications cannot. However, access to proprietary information also creates additional security, privacy, governance, and data-quality considerations. Strategic value does not remove the need for controls.

Scaling should come after evidence of value. OpenAI’s final recommendation is that once an organization establishes a valuable workflow, IT leaders should “match the product, capacity, and support model to its demand.” A successful pilot may require different infrastructure, service levels, model choices, monitoring, and support when used across a department or an entire company.

This also means avoiding unnecessary capacity. Not every successful use case requires the most capable model or the highest service level. Organizations can select different models and support arrangements according to the complexity, importance, volume, and risk of each workflow. This creates opportunities to optimize costs without reducing the quality of outcomes that matter.

A portfolio approach also gives leadership permission to stop projects. Experiments that fail to produce sufficient quality or business value should not continue indefinitely because of previous investment. Capital and technical capacity can be redirected toward applications with stronger evidence.

For the C-suite, the broader objective is disciplined expansion. AI investment can increase significantly while financial control improves at the same time. That requires measuring outcomes, comparing opportunities consistently, scaling demonstrated value, and continually reallocating resources as the technology and the organization’s needs evolve.

Enterprises are making AI cost management a strategic business discipline

AI cost management is moving beyond the IT budget. As companies expand their use of generative and agentic AI, executives need to understand not only how much they are spending, but also what that spending produces. Prudential Financial and Shutterstock are companies implementing stringent AI cost-management strategies, showing how financial oversight is becoming part of enterprise AI strategy.

The shift matters because AI spending behaves differently from many traditional software costs. Consumption can increase as more employees adopt AI, models handle more tasks, and agentic systems perform multiple operations to complete a workflow. Lower token prices do not guarantee lower total expenditure when overall usage is growing rapidly. An organization can therefore deploy cheaper technology and still face a larger AI bill.

This makes FinOps increasingly relevant to AI. FinOps is the practice of bringing finance, technology, and business teams together to understand and optimize variable technology spending. Applied to AI, the goal is not simply to reduce usage. Companies need to connect consumption with business outcomes so they can distinguish productive investment from unnecessary expenditure.

Courtney Totten, CTO and CISO at Shutterstock, captured the strategic importance of this issue during the FinOps X 2026 conference. Totten said understanding AI costs is “no longer optional, it is foundational to business strategy.” The statement reflects a broader management challenge: companies need financial visibility before they can confidently scale AI across important operations.

For C-suite executives, that visibility should extend from individual models to complete workflows. Leadership needs to understand which departments and applications consume AI resources, how much successful outcomes cost, whether consumption is increasing, and what value the company receives. Measures such as time saved, shorter cycle times, protected revenue, avoided risk, and additional capacity can make the economic contribution easier to evaluate.

Cost management should also operate alongside governance. AI systems can access data, invoke tools, and increasingly perform actions with limited human involvement. Expanding an AI workflow without appropriate permissions, monitoring, ownership, and spending controls can create financial and operational exposure at the same time. Mature programs will evaluate cost, value, quality, and risk together.

Prudential Financial and Shutterstock are relevant examples because both pursue stringent AI cost-management strategies. It does not provide detailed financial results or specific savings from those programs, so no conclusions about their return on investment should be inferred from the examples alone. Their significance is that established enterprises are treating AI economics as a strategic management issue rather than waiting for spending to become a problem.

The opportunity remains strong. Better cost controls do not require companies to slow productive AI adoption. They can make expansion more sustainable by identifying high-value workflows and directing investment toward them. As AI usage grows, companies that can measure cost and value together will have a stronger basis for deciding where to experiment, where to scale, and where to stop spending.

For executives, the priority is clear: AI cost management needs ownership, measurement, and regular review at the business level. The companies that develop this discipline early will be better prepared to increase AI usage while keeping investment aligned with measurable business results.

In conclusion

AI economics are changing fast. Model prices may continue to fall, but lower unit costs will not automatically produce smaller enterprise bills. Agentic AI can perform more work, make more model calls, use more tools, and ultimately drive significantly more consumption.

For executives, the answer is not to slow adoption. It is to make the economics visible. Track AI spending at the workflow level, measure the cost of successful outcomes, and connect those outcomes to business value. Governance should determine what can scale, while portfolio management should direct investment toward workflows with clear ownership and measurable returns.

The companies that manage this well will not necessarily spend the least on AI. They will know why they are spending, what they are getting for it, and when additional investment makes sense. As agentic AI scales, that discipline will separate uncontrolled consumption from sustainable business value.