Transaction Data Crowns the B2B Payments AI Winners

Chatbot polish is the wrong benchmark for B2B payments AI. The real contest is over who controls the transaction data detailed enough to make automation trustworthy, according to Boost Payment Solutions Founder and CEO Dean M. Leavitt, who told PYMNTS that AI will become embedded in payments rather than sit beside them as another software feature.

Leavitt’s core claim is sharper than the usual AI pitch. Enterprise payments are not just transfers. They are bundles of money, invoice detail, account identifiers, buyer-supplier terms and reconciliation obligations. In that setting, generic AI has limited value unless it can read the commercial context behind each payment.

“AI goes beyond payments. It will ultimately be completely embedded in payments, and payments will be embedded in it.”

B2B payments AI race will be won by transaction data, not chatbot polish

The expected AI story in finance has been interface-first: copilots, chat windows, faster search, better summaries. Leavitt is pointing to a less flashy layer. Transaction data is where the defensible value sits.

That matters because B2B payments AI has to operate inside workflows where small errors carry direct financial consequences. A single enterprise transaction can represent hundreds or thousands of invoices, PYMNTS reported, with line-item detail and buyer-supplier terms that need precise reconciliation.

Leavitt described the category simply:

“In B2B and enterprise-level B2B, it’s this combination of two worlds. It’s moving money and moving data.”

XOOMAR analysis: that framing cuts through the hype. If payments are only about moving funds, AI is an incremental add-on. If payments are also about interpreting obligations, enforcing rules and resolving exceptions, then the company with the richest transaction history has the better model input.

That’s the strategic divide. Firms bolting generic AI tools onto old workflows may improve user experience. Platforms with years of payment data can train systems to understand how buyers and suppliers actually behave.

Why Boost’s transaction data argument hits a nerve in commercial payments

Boost Payment Solutions sits in the commercial payments stack where buyer-supplier rules, card acceptance, payment timing and data exchange matter. In related PYMNTS coverage, Leavitt also described the continuing role of card-based payments and credit in helping buyers manage DPO while suppliers manage DSO.

The new claim is that AI’s best use is not replacing those rails. It is adding intelligence above them.

PYMNTS reported that financial institutions and payment networks have spent decades making money movement reliable. The recurring B2B pain sits around the payment: invoice matching, formatting, approvals, exception handling and the enforcement of bespoke commercial terms.

Before vs. after, according to the Boost thesis:

• Before: Payments were treated as a late-stage operational step.
• After: Payments carry structured commercial logic that finance teams can use strategically.
• Before: Intermediaries moved money and passed data along.
• After: AI can help organize, convert and apply that data based on stakeholder requirements.
• Before: Rules lived across contracts, systems and human judgment.
• After: Platforms can encode more of those rules into execution workflows.

Leavitt put it this way:

“What we’re now able to do is reflect the nature of the commercial relationship in the payment itself.”

That is the moat. Not the model alone. The model plus the lived transaction history.

For broader context on control points in commercial payments, see XOOMAR’s coverage of Mastercard and Amex Seize the B2B Payments Rulebook. The same pressure shows up in another way in Real-Time Payments Invade Payroll, Checkout and B2B, where faster rails still leave workflow questions unresolved.

The numbers behind Boost’s AI case are about invoice density, not market size

The supplied PYMNTS source does not give a total addressable market figure for B2B payments, and this article won’t invent one. The numbers it does provide are enough to explain the AI case.

A single large payment may include hundreds or thousands of invoices. Each invoice can carry supporting detail that must be matched against terms, accounts and internal controls. That is not a consumer checkout problem. It is a high-volume operational burden.

Related PYMNTS material says Boost has nearly two decades of enterprise payment activity and billions of dollars in transaction volume. Leavitt described the use of AI against proprietary data as a competitive tool.

“We’re using AI as that tool against our proprietary data really as a weapon vis-à-vis competitors.”

XOOMAR analysis: this is where B2B payments AI has a stronger business case than many front-end fintech features. The pain is repetitive. The errors are measurable. The workflows already exist. AI does not need to invent a new behavior. It needs to reduce exceptions, speed review and make payment data usable at the moment decisions are made.

From legacy rails to layered intelligence

The assumption in many fintech cycles is that old infrastructure must be ripped out. Leavitt’s argument is different. In related PYMNTS coverage, he said the bigger legacy issue is often mindset, not infrastructure.

“When people think of legacy, the first thing they think of is infrastructure. I would argue that a big piece of the changes that are going on with legacy right now actually relate to mindset.”

That fits the current AI discussion. PYMNTS reported that payment infrastructure is already mature, while the harder work sits in data exchange and operational workflow. AI can sit above existing rails and make fragmented processes more usable.

This is also why autonomous agents are unlikely to start by negotiating major supplier relationships. Leavitt said humans still need to establish relationships, ask questions and decide which rules make commercial sense. Agents are better suited first to implementation.

“The role of the agent is less so on the negotiation than it is on the implementation.”

That is a practical boundary. AI can approve invoices, route payments, apply rules and flag exceptions only when the rules are explicit enough and the outcome can be checked.

Banks, buyers, suppliers and fintech platforms are not chasing the same outcome

The same AI layer looks different depending on where you sit.

The constraint is trust. Leavitt said a commercial payment platform must “tick and tie everything to the penny every single time.” That sentence should kill any fantasy of unchecked automation in enterprise finance.

Boost’s response, according to PYMNTS, includes multiple agents in some workflows, with one checking another, alongside human review.

Transaction-rich AI gives CFOs a sharper vendor test

For CFOs and enterprise software teams, the practical question is not whether a vendor has AI. Everyone will claim that. The better question is what data the system can actually use, what rules it can enforce and how its output is verified.

A serious B2B payments AI vendor should be able to answer:

• Data quality: Which invoice, payment and buyer-supplier fields does the system rely on?
• Rule enforcement: Can it apply commercial terms dynamically, or only suggest actions?
• Exception handling: How does it identify, escalate and resolve mismatches?
• Controls: Are agents checked by other agents, humans, or both?
• Auditability: Can finance teams reconstruct why a payment was approved, routed or rejected?

Leavitt’s warning is the right one:

“Engage it, learn as much as you can about it. Certainly, don’t ignore it, but don’t just blindly trust it.”

The transaction data land grab will reward boring accuracy

The next phase of B2B payments AI will not be won by the loudest demo. It will be won by systems that quietly make fewer mistakes, enforce more payment rules and turn accumulated transaction history into operating leverage.

XOOMAR analysis: the clearest near-term proof points will be narrow and verifiable. Watch exception handling, rule-based approvals, reconciliation accuracy and bounded agent workflows. Evidence that would strengthen Leavitt’s thesis would include platforms showing that proprietary payment histories improve automation quality in real enterprise settings. Evidence that would weaken it would be persistent human rework, poor explainability or controls that fail when transactions involve complex invoice sets.

The AI winners in B2B payments probably won’t look magical. They’ll look disciplined. In this market, that’s the point.

Disclaimer: This XOOMAR analysis is for informational and educational purposes only. It is not financial, investment, legal, tax, or professional advice. It does not provide buy, sell, hold, price-target, portfolio, or personalized recommendations. Verify information independently and consult qualified professionals before making decisions.

The Bottom Line

• Boost argues the real AI advantage in B2B payments comes from proprietary transaction data, not user-facing chat tools.
• Enterprise payments require AI that can understand money movement, invoice detail and reconciliation obligations together.
• Companies with richer payment histories may be better positioned to build trustworthy automation for complex B2B workflows.