AI in Finance: Automating AP/AR Without Breaking Controls

For decades, finance teams have measured efficiency by how quickly they could process invoices, reconcile accounts, and close the books. Artificial intelligence is accelerating those tasks dramatically, but according to Altum Strategy Group’s Brendan Polke, that isn’t the most important story.

The real opportunity isn’t simply automating accounts payable or accounts receivable. It’s giving finance professionals the time and information they need to create more value for the business.

Organizations that approach AI solely as a cost-cutting exercise risk missing its greatest potential. The companies seeing the strongest results are using automation to eliminate repetitive work while allowing finance teams to focus on analysis, oversight, and strategic decision-making.

Polke says, “Think of AI as a value-creating opportunity rather than a cost-cutting initiative. It’s a new way to run your financial operations, not simply a way to replace the old one.”

Efficiency Is Only the Beginning

Much of today’s discussion around AI focuses on speed. Finance teams can process invoices faster, automate data entry, generate reports, and reduce manual errors. These gains are significant, but they’re only the first step.

Once organizations are no longer consumed by repetitive administrative work, they can begin asking bigger questions. Why are we paying for this service every month? Does this expense still support the business? Are there opportunities to reinvest savings into growth?

In one client engagement, AI reduced manual invoice processing from roughly 1,000 invoices per month to fewer than 100. Rather than eliminating employees, it gave the finance team time to examine spending patterns, document the business purpose behind recurring expenses, and uncover unnecessary costs that had gone unquestioned for years.

As Polke describes it, efficiency becomes the gateway to more valuable work.

Automation Doesn’t Replace Controls

Finance leaders naturally worry about introducing AI into processes responsible for safeguarding company assets. The answer, Polke says, is not to remove people from the equation but to redefine their role.

AI excels at repetitive, rules-based work such as creating invoice records, identifying duplicate transactions, detecting anomalies, or flagging exceptions. Human professionals remain responsible for reviewing unusual activity, approving payments, and exercising judgment where context matters.

That distinction is especially important in financial operations.

Organizations should never allow AI to independently authorize payments, approve significant spending variances, or certify financial statements. Those responsibilities require accountability that still belongs to people.

Instead, AI should surface exceptions for review, allowing finance professionals to spend less time searching for problems and more time resolving them.

Start Small, Build Confidence

One of the biggest mistakes organizations make is trying to automate too much too quickly.

Rather than replacing entire workflows, Polke recommends identifying repetitive tasks that consume time without creating business value. Report generation, data collection, file organization, spreadsheet preparation, and routine reconciliations often provide ideal starting points.

These incremental improvements allow organizations to learn how AI fits within their existing control environment before expanding into more sophisticated applications.

Successful AI adoption, he argues, should follow the same disciplined governance organizations would apply when implementing a new ERP platform. Business processes, internal controls, testing, and validation all need to be established before deployment.

The Finance Professional Is Evolving

Perhaps the biggest transformation isn’t happening inside finance systems. It’s happening within finance careers.

As AI assumes more repetitive tasks, finance professionals will spend less time processing transactions and more time interpreting them.

That shift is already changing the skills organizations need.

Future finance leaders will still require deep accounting expertise, but they’ll also need to understand AI tools, prompt engineering, automation workflows, and increasingly, how multiple AI agents interact with one another.

At the same time, technical capability alone won’t be enough.

“The new finance leader will be someone who understands both the application of AI and the financial data going into it and coming out of it. But just as important will be the soft skills. Change management will make or break AI adoption,” said Polke.

Helping employees embrace new ways of working, communicating why automation is being introduced, and ensuring AI enhances rather than diminishes human expertise will become critical leadership responsibilities.

From Cost Reduction to Competitive Advantage

Organizations often begin their AI journey hoping to reduce costs, but the more successful ones end somewhere very different.

By removing repetitive administrative work, finance teams gain the capacity to improve financial oversight, uncover new efficiencies, strengthen controls, and provide more strategic guidance to leadership.

The question is no longer whether AI can automate financial operations, we know it can. The question is how organizations will use the time and insight that automation creates.