How Agentic AI Is Changing Accounts Payable

It seems like no business conversation happens these days without AI taking center stage. That makes sense. Few technologies have promised the kind of efficiency gains we’re now seeing from AI since the cloud transformed how businesses operated nearly two decades ago.

The shift is already reaching finance. According to Tipalti’s State of AI in Finance research, 52% of finance professionals report using agentic AI and automating workflows regularly, while 33% expect agentic AI to become more deeply integrated into finance workflows over the next three to five years.

For AP teams, that raises a practical question: What does this actually change about the work we do every day?

As someone who manages the AP function at my company, I’m less interested in AI as a headline and more interested in what happens when we put it to work inside our workflows. My team and I are looking at where technology can take over the repetitive steps that consume time, while leaving people responsible for the decisions that require context, judgment, and accountability.

In practice, that means redesigning some of our core AP workflows around advanced automation and agentic AI. And in my experience, the biggest opportunity isn’t removing people from the process. It’s removing the parts of the process that never really needed a person in the first place.

What Does Agentic AI Actually Do in Accounts Payable?

Agentic AI is different from traditional automation because it works toward a goal rather than following a fixed set of instructions. It can assess a situation, determine what needs to happen next, take action across connected systems, and bring in a person when human judgment is needed.

Consider this example. A software invoice arrives for $4,800 more than the purchase order. Traditional automation flags the mismatch and sends it to a queue for someone to investigate. An agent can go further. It can pull the underlying contract, find an amendment that added 20 seats during the term, and compare the prorated charge with the agreed per-seat rate. It can attach the supporting documentation and send the invoice to the budget owner with the relevant context. If the charge still falls outside the contract terms, the agent can prepare the next step for the AP team, such as drafting a request for a corrected invoice.

Instead of starting from scratch, your team is reviewing work that has already been investigated. That’s possible because agentic AI can:

• Read context: Pull information from POs, contracts, amendments, emails, and payment history rather than looking at the invoice in isolation.
• Handle multiple steps: Work through a sequence of related tasks rather than performing one predefined action.
• Work exceptions: Investigate discrepancies and prepare them for resolution instead of simply flagging them.
• Know when to escalate: Operate within the approval limits and controls established by the AP team and route decisions to the appropriate person.
• Leave an audit trail: Record actions and the reasoning or supporting information behind them.
• Learn from patterns: Use previous resolutions to handle similar situations more consistently.

The Real Opportunity Is Redesigning Accounts Payable Workflows

For my team, the interesting part isn’t just that an AI agent can perform a task faster. It’s that we can start asking a different question: Why are we doing this task manually at all?

Our industry is already moving in this direction. Among teams that have fully automated their AP operations, 36% report using AI-powered automation broadly across AP, according to Tipalti’s Global Finance Outlook research. Finance professionals point to invoice processing, payment reconciliation, financial reporting, and fraud detection or risk monitoring as areas where AI can create the most value.

Those are also the areas where I see the most potential to rethink how my team works:

• Invoice processing: AI-powered capture, coding, matching, and approval routing can handle much of the repetitive work, leaving people to focus on exceptions and judgment calls.
• Payment reconciliation: Automated reconciliation can surface discrepancies earlier instead of waiting for month-end, while AI can help investigate the cause and prepare the information needed to resolve them.
• Financial reporting: AI can move reporting beyond showing what happened by identifying the transactions behind a variance, explaining what changed, and preparing information for the next step.
• Risk management: Instead of manually reviewing every transaction, AI can monitor patterns across payments and supplier data and bring unusual activity to the attention of the people who need to investigate it.

But none of this works if the systems involved can’t communicate. A seamless ERP integration is part of the workflow itself. When invoice, payment, and reconciliation data can move between systems without repeated manual entry, my team spends less time moving information around and more time making sure the information is right.

That’s the part of AI adoption I find most interesting. We’re not trying to remove people from these workflows. We’re removing the highly manual work surrounding them. My team still owns the controls. We still decide what requires approval. We still investigate unusual transactions. We still make the judgment calls. What changes is how much time we have to do that work well.

Why Workflow Redesign Is a Skill Set

For me, the real value of agentic AI isn’t only doing today’s AP work faster. It’s rethinking how that work should be done in the first place.

As advanced automation and agentic AI become part of everyday finance operations, AP workflows will continue to change. Tasks that once required manual intervention will increasingly happen automatically, while people will move into managing exceptions, setting controls, and making decisions that require context and judgment.

All of this will create a new kind of demand for finance professionals. Knowing how to work with AI will matter, but knowing how to redesign workflows around AI will matter even more. That includes understanding how individual tasks connect, where controls belong, how systems should interact, when human judgment needs to enter the process, and how to build workflows that are both efficient and accountable.

For AP teams, this is an opportunity to start building that skill set now. Experiment with new capabilities, rethink the workflows your team manages every day, and work closely with trusted finance technology partners to understand what’s possible.

AP will look very different from what we know today. The finance professionals who can help design that future (and redesign the workflows that support it) will have an increasingly important role in shaping how our industry evolves.