The Biggest AI Lesson? AI Raises the Value of Process and Expertise.

As finance teams move from experimentation to implementation, it’s becoming clear that the biggest gains come from applying AI to the right processes with the right controls.

That’s certainly been our experience at Zuora. As we’ve embedded AI across our quote-to-cash processes, one lesson has stood out: AI creates value when it helps teams move through work faster without weakening trust in the result. In finance, the standard isn’t simply speed. It’s whether the output is usable, reviewable, and accountable.

Start With the Work That Creates Friction

The most successful AI use cases focus on routine, time-consuming work where better information and faster execution have an immediate impact. 

On our Billing Operations team, AI helps explain why an invoice is billed a certain way, identify the bill run that generated it, and pull together the relevant account and subscription history. Instead of manually piecing that information together across multiple screens, our teams can start with the context they need and focus on resolving the issue. Tasks that once took hours can now be completed in minutes, with reporting time reduced by about 70%.

Our Revenue Accounting team is using AI to investigate why revenue wasn’t recognized, what caused a variance, or which contract change drove a particular accounting outcome. Those questions often require tracing information across contracts, amendments, allocations, and revenue schedules. AI shortens the investigation so accountants can spend more time analyzing the result.

Across both teams, AI reduces the time spent gathering information so finance professionals can spend more time analyzing it.

AI Is Only as Good as the Process

AI performs best when the underlying process is already well understood. When we’ve applied AI to problems where the process itself wasn’t clearly defined, the results have been much less compelling. AI is very good at accelerating execution. It’s much less effective at defining the problem you’re trying to solve.

That’s one reason our own quote-to-cash transformation mattered. Before our quote-to-cash transformation, too much work depended on manual intervention, disconnected systems, and after-the-fact reconciliation. AI wouldn’t have solved those problems. Once we simplified workflows across CPQ, Billing, and Revenue, reduced our revenue close from roughly 15 days to three, and created a more connected operating model, AI became much more effective because it had a stronger foundation to build on.

The same principle applies to AI. If data is fragmented and processes are inconsistent, AI tends to accelerate confusion rather than solve it. When workflows are standardized and the underlying data is reliable, automation becomes much more effective.

The Real Opportunity for Finance

The most effective AI applications we’ve seen help teams investigate exceptions, assemble context, and focus attention where expertise is needed most. Human review, controls, and accountability remain firmly in place. AI simply reduces the manual work required to get there.

That’s the lesson I’d leave finance leaders with: Start with the work that creates the most friction. Make sure the process is well understood. Then use AI to help teams move from manual research to informed action without compromising controls.

Our experience has reinforced something finance has always known. Better tools help, but they don’t replace strong processes or sound judgment.

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