For the past decade or so, automation has been a key feature of B2B finance software, with systems routing invoices, scheduling ACH runs, and flagging overdue accounts automatically based on rules that were set in advance by humans. But that model is now being replaced by the next generation of artificial intelligence, known as agentic AI, which is opening the door to powerful new efficiencies and optimizations around B2B payments.
However, this shift is also exposing cracks in the technological infrastructure many companies still use to run their financial operations; Legacy tech stacks simply weren’t designed to be compatible with today’s autonomous AI agents.
Understanding what finance leaders can do to close that gap starts with differentiating agentic AI from the automation that came before it.
Automation vs. agentic AI
The distinction between automation and truly agentic AI largely comes down to decisions; automation can execute decisions based on human-determined “if/then” rules, while truly agentic AI can act autonomously to make data-informed and context-aware decisions. The real test, however, is in how each handles situations no one planned for in advance, explained Allison Steitz, head of product marketing for Paystand.
“Automation is really good at executing human-defined tasks, or doing what the person designing it told it to do,” Steitz said. “But it’s not good at handling edge cases that the designer didn't anticipate and code in ahead of time.”
For example, if a designer didn’t anticipate a particular exception to a rule, then a system that runs on automation simply won’t act on it.
Another case in which automation falls short: if a programmer failed to account for a “refunded” payment status, the rules-based system won’t know where to route the payment, Steitz said. Over time, these edge cases pile up, and dealing with them becomes a drain on finance teams’ time.
“A lot of what finance team members do is manually resolving exceptions to rules-based tools,” Steitz said. “These tasks are not easy — like chasing partial payments, working disputes and matching payments that don't match the invoice amount owed.”
Agentic AI solves this problem. Agentic AI uses context and reasoning to achieve an overall goal or solve a problem as opposed to following a rigid series of “if/then” statements. Rather than executing a fixed script within a closed system, large language models (LLMs) can scan vast amounts of unstructured data across the internet and reason through situations that weren’t programmed in advance.
Agentic AI also improves the collections process, Steitz said. Up until recently, automated accounts receivable (AR) tools needed hard-coded rules to determine when to send a request for payment. By factoring in much more granular and specific data, agentic AI offers significantly more flexibility and visibility, she said.
“An agentic system can query the full history of the customer’s payment behavior,” Steitz said. “For instance, it knows which past invoices were paid and when. It also knows when a customer’s payments have changed from a bank to credit card, which can offer a flag of risk.”
This customer-specific information enables AR teams to quickly design and offer highly tailored collection plans instead of standardized ones, increasing collection success rates while also helping maintain positive relationships with payers.
But achieving these improvements isn’t as simple as deploying the latest agentic AI tools. Finance leaders must first ensure their technological infrastructure is up to the task. And at the moment, most aren’t.
Infrastructure constraints
While agentic AI’s potential to optimize B2B payments is clear, many organizations have thus far failed to capture these benefits. Of the $37 billion invested in enterprise AI in 2025, just 16% of deployments qualified as truly agentic, according to a report from Menlo Ventures. Steitz attributed this gap to two problems.
First: The fragmented, siloed design of legacy tech stacks.
“The AI agents are there. The technology is not the constraint. The constraint is the business infrastructure to adopt it,” Steitz said. “Most businesses use fractured tech stacks. A company has an ERP, which is separate from their bank, processor, AR tool, accounts payable (AP) tool, expense tool, financial planning and analysis (FP&A), and spreadsheets.”
As an AI agent is only as effective as the data and systems it can actually access, this series of siloed systems design limits the value and ROI of any single deployment.
Capturing the full potential of agentic AI requires a unified tech stack that agents can autonomously act across. When receivables, payables, reconciliation, and systems of record all connect, an agent can operate based on a complete picture of the business rather than one siloed system.
But achieving this unification can be difficult, especially for companies with fragmentation driven by years of bolting on point solutions across multiple systems.
Steitz cited a second hindrance to a fully agentic payments workflow: The money itself.
"You can unify every workflow you have and still only solve half of it," Steitz said. "The agent decides in seconds. But if the value is still moving on batch files and settlement delays, the money lags far behind the decision. The last decade was about digitizing the workflow. The next one is about digitizing the money."
The infrastructure for digital transactions now exists. In 2025, on-chain settlement volume surpassed Visa and Mastercard combined, and the GENIUS Act established a federal framework for stablecoin transactions. Some platforms have been leaders in the field. For example, Paystand has operated within that framework since 2013, moving value instantly, with settlement and reconciliation built into the guardrails.
"AI is driving down the cost of labor; programmable money drives down the cost of moving value," Steitz said. "Put them on the same foundation, and the agent does the work while the money moves at the same speed — settling and reconciling as it goes, instead of sitting idle in transit."
Finding the right platform partner
One way to quickly break free from this accumulated tech stack debt is to partner with a provider whose platform already brings multiple financial functions together. That makes choosing the right partner one of the more consequential decisions of an agentic AI adoption strategy.
For companies facing this critical choice, Steitz suggested assessing potential partners on a few key criteria:
- Visibility and control: When AI makes financial decisions for your company, you need real-time visibility into its actions, the functionality to set guardrails and the ability to intervene.
- Flexibility to fit your business: No two businesses’ finance operations are alike, so your platform has to adapt to your unique needs, workflows and processes.
- Responsive human support: When agents influence how your money moves, your financial operation must be backed by a partner you can trust, with human support you can reach when issues arise.
- Pricing that rewards growth: Avoid per-seat or per-transaction models that effectively tax growth. With volume-based pricing, the more you grow, the more you save.
"In finance, trust matters more than novelty," Steitz said. "The goal isn't automation for its own sake — it's a system where money, data, and decisions move together."
To learn more about unifying your business’s tech stack to leverage the full potential of agentic AI-powered finance, contact Paystand today.
