Gartner urges CFOs to pilot finance AI with governance

Gartner has urged Chief Financial Officers to treat their first finance AI agent as a governance pilot before broader deployment.

Gartner Director Analyst Alex Levine said finance leaders should prioritise oversight and traceability over early return on investment as AI agents begin moving into finance workflows.

According to Gartner, the key distinction between AI agents and earlier automation tools is autonomy. Traditional automation follows fixed rules, while generative AI creates content. AI agents, by contrast, can interpret objectives, plan steps, execute tasks and interact with multiple systems.

That creates a different risk profile for finance teams. If controls are weak or ownership is unclear, errors can spread quickly across processes. It also makes it harder for staff to understand exactly how an action was taken, increasing pressure on audit trails and review procedures.

Different risks

Levine said the issue is not limited to whether an output is correct. Finance leaders must consider the full chain of actions an agent takes, including what it accessed, what it planned and what it produced.

"AI agents are riskier in finance because they act with greater autonomy. Unlike traditional automation, which follows fixed rules, or GenAI, which generates content, AI agents interpret objectives, plan and execute steps, and interact with multiple systems. This autonomy means agents can make decisions and take actions that may not be visible or easily explained to humans. It also means errors or misjudgments can propagate quickly if oversight is unclear. Finally, the risk shifts from just the output to the entire process, making governance and traceability essential," said Alex Levine, Director Analyst, Gartner.

The comments reflect a wider debate in corporate finance over how quickly departments should automate judgment-heavy tasks. Many finance teams already use rule-based software and analytics tools, but AI agents introduce software that can act with less direct human instruction.

Gartner argues that this changes the starting point for pilots. Rather than judging an early trial on cost savings or labour reduction alone, finance chiefs should use the first deployment to test whether governance systems are fit for purpose.

Governance first

Levine said early pilots are more likely to fail because of weak controls than because of the underlying technology. In his view, identifying gaps in review processes, accountability and auditability should come before expansion into more sensitive areas.

"The greatest risks and the greatest value lie in establishing oversight, not in chasing quick financial gains. Early pilots are most likely to fail due to unclear controls, not poor technology. By prioritizing governance, CFOs can surface and address gaps in oversight, traceability, and review processes before scaling to use cases with higher stakes. This approach ensures that future AI deployments are built on a foundation of robust, auditable controls, protecting both the organization and its stakeholders," said Levine.

That approach would mark a shift for finance teams under pressure to show measurable benefits quickly from artificial intelligence spending. In many organisations, Chief Financial Officers are both gatekeepers of technology budgets and the executives expected to introduce AI into their own functions.

Gartner recommends that the first pilot take place in a low-risk, contained workflow where mistakes can be detected and reversed. Processes with clear boundaries, repeatable steps and verifiable outputs are more suitable than tasks tied to regulatory reporting or any process that could lead to a restatement.

Levine also said the agent's limits should be defined before development starts, including rules on data access, permitted actions and where human review is mandatory.

Ownership is another condition. Finance, IT, and audit or risk teams should each have clear responsibility in the pilot, and the system should run in a sandboxed environment so behaviour can be observed without exposing live operations.

Measuring success

In Gartner's framework, a successful pilot is not one that simply completes tasks with minimal intervention. Instead, success depends on whether the organisation can demonstrate practical controls, consistent review points and full traceability for each run.

Levine said finance teams should keep a detailed failure log and be able to reconstruct an agent's actions from start to finish. He added that pilots should also produce templates and training that reduce the risk and cost of later deployments.

"Success should be measured by governance readiness, not just autonomy or ROI. Key indicators of a successful agent pilot include practical and consistently applied controls and review points and achieving complete traceability of agent actions. Another critical milestone is maintaining a comprehensive failure log that documents issues and fixes. The finance team should be able to reconstruct what the agent planned, accessed, and produced for every run. Successful AI agent pilots should deliver templates and training that reduce the risk and cost of future deployments, as well as reusable governance capabilities that enable safe, scalable AI adoption," said Levine.