How finance teams should start with AI operations

Finance teams have no shortage of AI ideas.

That is not the hard part.

The hard part is knowing where to start without creating control issues, audit headaches, or another tool nobody wants to own.

The best starting point is usually not strategy. It is one repetitive workflow with visible cost and clear controls.

Why finance is a strong fit

Finance operations already has the characteristics good automation needs:

• repeated work
• structured data
• clear approval logic
• measurable cycle times
• meaningful labor cost

That makes the first implementation decision easier than it is in more ambiguous functions.

Where to begin

Strong first workflows in finance often include:

• invoice intake and approval routing
• reconciliation support
• collections follow-up
• vendor data verification
• month-end document handling

These workflows matter because they combine volume with rules.

They also usually span systems, which is where manual work starts compounding.

What finance leaders should avoid

Do not begin with a broad ask like "use AI in FP&A."

That is too open-ended and too hard to measure.

Do not begin with a workflow that has no clear owner.

And do not begin with a process so politically loaded that simple improvement takes months of alignment.

What a good first win looks like

The right first workflow should let the team show:

• current manual cost
• current turnaround time
• current exception rate
• expected automated improvement

That creates a real operating case instead of a curiosity budget request.

Finance teams are usually better than most at evaluating economics. The same discipline should be applied to AI.

If the workflow is high volume, rules-driven, and cross-system, there is a good chance it belongs near the top of the list.

If you want a finance-specific view of where we see the best opportunities, start with our financial services page or book a workflow audit.