Finance
Always On: How Agentic AI Reinvents the CFO
Agentic AI anticipates what's next, works through it independently, and liberates finance teams from work no one wants to do.
Tim Wakeford
VP Product Management
Workday
Always On: How Agentic AI Is Reinventing the CFO Function
Agentic AI anticipates what's next, works through it independently, and liberates finance teams from spending time on work no one wants to do.
The office of the CFO is every company’s financial backbone. It tracks performance, manages risk, and maintains controls. While that functionality remains core, the tools and workflows built to improve and expand those capabilities are shifting quickly with the advent of agentic AI.
The loudest messages around AI are about efficiency, but in finance, using AI agents is more than just automating tasks to move faster. When the time it takes to close the books or model scenarios is compressed, the entire planning rhythm changes. Access to all that key data — performance, risk, control in real time — lets finance forecast, model options, and pivot as business happens. And instead of measuring risk after the fact, teams can catch anomalies and exposures as they emerge.
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What If Finance Never Slept?
In agentic finance, always-on agents advise finance teams about new sources of value, and then act to control and complete the work to capture them. These agents run continuously in the background, operating at unlimited scale and previously unimaginable speed, unlocking new ways of getting finance work done with AI operating within existing platforms, processes, and controls built for the precision finance demands.
Agents as Teammates
The easiest way to understand agentic finance is to stop thinking about AI as a tool and start thinking about it as a teammate. Agentic finance is a set of specialized agents, each handling part of the finance function: the analyst agent spots margin risk and reshapes forecasts, the contract negotiation and supplier contract agents manage procurement, and the cost and profitability management agent closes the books. A financial test suite runs continuously in the background, auditing transactions and flagging exceptions as they happen.
They anticipate what's next, independently progress tasks, and absorb the lower-level work that has to get done but drains the time and attention of the people doing it.
These agents operate within the same rules, the same processes, the same compliance frameworks, and the same business context as their human counterparts. But with one critical difference: they never stop. They don't get bored. They don't get frustrated. They don't mind running the same task a thousand times. And they scale without limit.
What does this look like in practice?
Insight and strategy: Agents surface the state of the business in real time and recommend strategic paths forward — giving finance leaders the clarity to act, not just report.
Procure to pay: Agents transform procurement and payment operations, eliminating the manual steps and approval bottlenecks that slow teams down.
Risk and controls: Agents continuously monitor for exposure, enforce controls, and flag issues before they become problems — not after.
Compliance: Agents ensure regulatory adherence is maintained across the business, without requiring human intervention at every step.
Management reporting: Agents assemble and deliver the reporting finance leaders need, when they need it.
Financial close: Agents compress the close from days into minutes, running the reconciliations and checks that once consumed entire teams.
Together, these capabilities make finance function differently.
Stop thinking about AI as a tool and start thinking about it as a teammate.
Putting Agents to Work
These finance agents are already helping companies manage spend, catch risk, and close faster. To see how this plays out in practice, consider this situation a retailer would face launching a new campaign.
Imagine a global clothing company that has just launched a denim campaign at its flagship store, with plans to expand to the local area. As the campaign went live, the CFO received a Slack alert from the analyst agent flagging two problems:
Off-contract spend was up 23%
Labor costs were trending above plan
A quick follow-up question surfaced the cause: the flagship store manager had hired local contractors to build extra display cases, driving a 30% spike in carpenter and hardware costs.
To solve this, the analyst agent pulls data from Workday Financials, Adaptive Planning, and Snowflake into a single view and recommends consolidating suppliers and aligning labor to actual demand. An AI-run RFP follows. The contract negotiation agent locks in:
$410 per unit pricing
48-hour delivery SLA
18% reduction in off-contract spend
In the background, the financial test suite monitors 90,000 transactions, catches an unapproved rush fee on a supplier invoice, and resolves it in minutes. A new standing test is added to govern contract spend going forward. At close, agents allocate costs line-by-line using activity-based costing. The outcome:
Off-contract spend reduced across stores
Margins restored
89% of the initial flagship margin loss recovered
With agentic finance, this scenario will be standard. And latency is finally a thing of the past.
With agentic finance, latency is finally a thing of the past.
Until now, AI conversations in finance started and ended with efficiency. Closing the books faster matters because it makes new things possible: forecasts grounded in what's happening now, risks caught as they emerge, controls that get stronger with every transaction they touch.
In the retail customer scenario, the agents didn't just flag off-contract spend quickly, they prevented it from recurring. Margin wasn't just recovered; the system learned to protect it. That's a different category of value than speed.
Agentic AI doesn't give the CFO a faster version of yesterday's finance function. It gives them a more precise one, where better information leads to better calls, and the function itself improves over time.
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