There’s a pattern many organizations know well. A new technology arrives, adoption accelerates faster than governance can keep up, and a few years later, the finance team is staring at a spreadsheet, wondering how the bill got so large and who signed off on it.
The SaaS era exposed what happens when technology adoption outpaces financial oversight. And with agentic AI embedding itself into everyday workflows, organizations risk heading down a similar path.
SVP of EMEA & APAC Operations at Flexera.
Recent reporting on Amazon employees ‘tokenmaxxing’ – gaming internal AI metrics to inflate adoption figures, is an early signal of what that looks like in practice.
But the dynamic is not specific to Amazon. When AI usage isn’t fully visible, and the incentives favor showing more activity rather than less, accountability tends to disappear quietly. It points towards a governance failure – and governance failures require structural solutions.
The opportunity is clear. The governance isn’t.
The need for visibility
AWS’s Banking on the Cloud 2026 report makes the strategic case for agentic AI in financial services clearly and compellingly. Cloud computing infrastructure and AI agents are positioned as the foundation of next-generation banking, which means faster decisions and more responsive customer experiences.
What the report focuses on is what AI can save. The other part of the equation is what AI itself costs to run at scale, and who’s accountable for that.
The cost model for AI agents behaves differently from anything most enterprise finance teams have managed before. When you license a conventional software tool, there’s usually a fixed price and a user count. The spending is visible even when it isn’t well-controlled.
AI agents work differently as they run continuously, calling on external services and triggering actions across systems as they go. Each step consumes resources, and because agents operate autonomously, often handling tasks that would previously have required human judgment, that consumption can scale quickly and unpredictably. There’s no contract line that captures it cleanly and no renewal date that forces a review.
Getting ahead of this requires visibility that most organizations are only now beginning to build.
The sprawl problem
The SaaS management challenge is a familiar one to most IT and finance leaders. Application estates grew faster than procurement could track them, governance lagged behind adoption, and many enterprises spent years rationalizing software stacks they never intended to build. It’s an ongoing problem that businesses are still managing, years down the line.
AI agent sprawl will likely develop differently, but the underlying problem is similar. The critical difference is pace.
A SaaS tool that gets deployed and forgotten sits there, quietly billing at a fixed rate. An AI agent generating outputs in real time is actively consuming resources from the moment it runs, and that financial exposure, left unmonitored, compounds in ways a forgotten software subscription simply doesn’t.
Organizations that get the right comprehensive visibility in place early will be in a significantly stronger position than those treating cost governance as something to formalize later.
There’s also a regulatory dimension that’s coming into sharper focus, particularly in financial services. AI agents frequently depend on external model providers and third-party data sources. Each dependency introduces a potential point of failure – and in regulated industries, potential compliance exposure.
Regulators are paying attention, the EU AI Act’s full obligations for financial services AI land in August 2026, and DORA audits are already underway, which means the question of who owns that chain of accountability will need a cleaner answer than most organizations currently have.
What good governance actually looks like
The encouraging part is that none of this requires building new disciplines from scratch. It requires applying familiar ones to a new context and doing it early.
The right starting point is understanding cost in relation to outcome. What does it actually cost to complete a task using an AI agent, and what is that task worth to the business? Answering it means connecting AI spending data to the broader picture of how technology is used and what it delivers, so that finance and engineering are working from the same information rather than talking past each other.
Controls also need to be built into the infrastructure rather than layered on top of it. As agent deployments grow, no team can realistically review individual workflows by hand. Policies that depend on someone remembering to check a dashboard aren’t really policies; they’re suggestions. When a budget review turns difficult or a regulator asks questions, suggestions don’t hold up.
Most importantly, ownership needs to be established from day one. Which budget carries this deployment? Who reviews it when consumption shifts? Right now, many AI agents are being deployed by engineering teams without meaningful involvement from finance. That gap is entirely closable. Closing it before the bill arrives, rather than after, is where the real advantage gets built.
The organizations that navigate agentic AI well will be the ones that treat governance as part of the deployment decision rather than an afterthought to it. Cleaner accountability means faster decisions and AI investments that can actually be defended against the board or a regulator. Throughout the rest of this year and beyond, that is the key differentiator between organizations that scale AI confidently and those that are still untangling the bill.
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