How to use AI for spending controls without losing oversight

AI tools such as Claude, ChatGPT and Copilot can now connect directly to finance platforms. Finance teams save time and admin work because the AI tool pulls directly from spend data to handle tasks and answer questions.

But giving AI tools the ability to access and act on spend data raises concerns about what an assistant can see, change and approve. Finance teams also need to know the assistant’s answers are accurate and traceable to source records for audits.

Learn how to use AI for spending controls so finance teams can streamline spend management without losing oversight.

Nicole Michaelis, our Director of Design, leads design work focused on AI and agentic experiences at Pleo. Here she shares how to draw the line between what AI should handle and what should stay with your team.

Setting the rules for AI assistants in finance 

Creating a clear policy is the foundational step for maintaining trust and control when using AI in finance. It sets guidelines for employees who use AI tools, as well as letting AI assistants know which rules to enforce.

Consider the following when creating or updating your policy around AI for spending controls:

Stopping unofficial AI use 

Without an approved option, staff may paste expense reports, invoices or receipts into personal or unapproved AI tools. That data then sits outside your systems, where finance, IT and compliance teams can’t see how it’s used. An approved list of AI tools gives people a safer way to do the same work.

Establishing permissions 

An AI assistant connected to spend data could read expenses, recategorise them, add notes or help chase colleagues for missing receipts. Automating spend controls with AI reduces administrative time, but teams need to decide which specific actions an assistant can take.

In Nicole’s experience, “Can it move money?” is almost always the first question customers ask. That’s why keeping anything that moves money with a person is “a deliberate line we’ve decided to hold with Pleo’s MCP,” she says.

A simple way to approach permissions is to separate viewing from updating. Let an AI assistant analyse spend data freely, and apply stricter controls to anything that changes data. Leaders can also set different permissions for each AI tool, since teams often use more than one.

Keeping approval limits in place 

An AI assistant shouldn’t be able to approve anything the person using it couldn’t. When an assistant acts under the user’s own permissions, your existing approval flows and limits still apply, so AI doesn’t create a back door around them.

Nicole also advises against loosening limits because AI is involved. “If anything, start tighter on AI-initiated changes and widen as you see consistent results,” she says. “Treat autonomy like a limit you raise over time.”

Creating an audit trail 

If AI recategorises 40 expenses and nothing records it, teams will struggle to explain those changes at the end of the month or during an audit. That’s why, with Pleo’s MCP, every action is logged and marked as done, so you can trace what happened and who asked for it.

AI tools like Claude and ChatGPT ask for confirmation before writing changes by default, but users can switch this off. “Keep this setting on, at least until you’ve seen it work on your own data,” says Nicole.

Review the audit trail regularly too, especially in the first few months, so you can spot patterns or anomalies before they become problems.

Avoiding inaccurate outputs 

AI assistants can give confident answers with made-up figures or make errors, such as recording spend in the wrong category. Answers should come from live spend data, and anyone checking them should be able to trace each figure back to the source record. Treat summaries and categorisation as drafts to review rather than ready-to-use figures.

What to automate and what to keep with your team

Keep people in charge of exceptions and higher-value spend. That can include anything outside policy, purchases above an amount you set and spend with new suppliers. Write those limits into your policy so the AI assistant knows when to pass a decision to a person.

Over time, you can hand some of those decisions to the AI assistant once it has proved itself. Nicole points to one finance lead she interviewed, who lets the assistant flag something ten times, checks the results and then tells it to “do this going forward.” As Nicole puts it, “Trust is earned through verified results, not granted up front.”

Nicole recommends starting with tasks that are rule-based or easy to check, such as:

• Chasing colleagues for missing receipts
• Matching receipts to expenses
• Bulk coding and tagging expenses
• Flagging spend that’s been coded to the wrong category
• Pulling together a month-end overview of what’s still open
• Listing pending approvals that are holding up the close
• Reviewing expenses for unusual charges before you export them to your accounting system

Using this approach, the AI assistant handles the time-consuming checks and follow-ups, letting your team focus on the decisions that require human judgment.

No matter how much you automate, plan to keep some human review in place. “Even the most AI-forward finance teams we’ve spoken to expect to keep reviewing some expenses themselves, for now,” says Nicole. “The goal is to shrink the pile that needs a human, not remove the human.”

Putting AI for spending controls into practice

Adding AI tools doesn’t automatically reduce your team’s workload. In fact, almost half of finance teams using AI extensively are still spending more time policing spend than shaping strategy, according to Pleo’s AI Transformation report.

Clear policies and specific permissions help reduce that overload. Teams that trust their AI assistant’s outputs spend less time rechecking spreadsheets, and clear ownership means work doesn’t get done twice. These controls are also easier to maintain when they’re built into the platform where your team already manages spend.

In Pleo, the Policy Agent applies your spend rules in real time and routes approvals to the right person. And Pleo’s MCP connects AI tools to live spend data using the permissions already set up in Pleo.

With AI guardrails in place, routine spend work gets done faster, and your team keeps control of the decisions that need human judgement.

Pleo’s MCP is available now. Get started with Pleo’s MCP and connect your AI assistant to Pleo.

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