How Agentic AI Is Reshaping Payment Strategy for Software Platforms

Agent-led commerce is taking off, at least in theory. Retail giants are piloting agents that can search, compare, and transact on a user’s behalf. Many startups are layering agentic workflows on top of checkout experiences. And card networks are already rethinking how credentials, data, and disputes should be managed in a world where a bot might press “buy.” 

Adoption is still early, but momentum is building. Morgan Stanley predicts that by 2030, AI-powered shopping agents could influence nearly half of U.S. online shoppers. For software platforms with embedded payments, that creates a clear challenge: more transactions will originate outside the platform’s controlled checkout flow and, in some cases, on rails the platform may not fully control. 

This shift raises a strategic question: How do software platforms protect their payments revenue, risk posture, and user experience when the end customer may never directly interact with their software? For many providers, this will require rethinking payments not as background plumbing, but as a product that must evolve alongside new patterns of automation, authorization, and risk. 

Agent-Led Checkouts Meet the Limits of Today’s Rails 

Agentic commerce introduces distance between user intent and transaction execution. For payments, that distance matters.  

Dispute and authorization frameworks were built for clear, human-initiated actions. When a bot initiates the payment, foundational questions become harder to answer: Who actually authorized the transaction? And how should a platform prove user intent if the transaction is later challenged? 

Software providers have spent years optimizing checkout paths to boost conversions, increase cart sizes, and offer personalized upsells. Agent-led transactions can bypass these flows entirely. What may feel seamless for a user can remove the platform’s ability to guide the purchase, surface recommendations, or capture value at the point of sale. It’s like trying to run self-driving cars on a road built for horse-drawn carriages: the rails weren’t designed for these patterns of authorization or automation.  

In practical terms, agent-led flows challenge the assumption that platforms control the point of conversion and basket composition. That disruption directly affects the mechanics that drive integrated payments revenue. 

Monetization Gaps Will Likely Widen Before They Narrow 

Integrated payments have become a core part of the software platform business model, tied directly to growth, profitability, and customer retention. According to Wind River Payments’ 2025 Payments Report, 41% of software providers said that more than half of their total revenue now comes from payments, and 96% said they are exploring new ways to monetize payments this year.  

Despite the revenue potential, adoption remains low. Sixty-five percent of software providers report that fewer than half of their customers are actually using their integrated payment offerings. This is the monetization gap: the widening disconnect between the revenue integrated payments could drive and the revenue platforms actually capture. 

Agent-led commerce risks widening this gap in two ways: 

• Transaction displacement: As agent-led commerce grows, some transactions may bypass a platform’s integrated payments flow under certain conditions. If an agent injects payment credentials into the merchant’s existing checkout (as emerging open protocols like ACP allow), the transaction still settles over the merchant’s processor and the platform retains the volume. However, if the agent completes the purchase through its own embedded wallet or payment rails, the merchant still gets paid, but the platform loses the associated processing volume. The risk is conditional, but it grows as more agents adopt their own payment pathways. 

• Reduced average transaction value: AI agents are designed to execute a narrow instruction set: find the product, buy the product, complete the task. That removes opportunities for add-ons, warranties, and higher-margin alternatives. Over time, this can compress merchant margin and cause platform monetization to shrink. 

Combined, potential volume displacement and lower average transaction value compound the monetization gap at the exact moment payments revenue is becoming more central to platform economics. This pressure forces software providers to strengthen the parts of the payments experience they still control. 

Software platforms cannot control which agents consumers use. But they can control how many customers adopt their integrated payments offering–the most direct lever for protecting long-term revenue.   

AI’s Real Value Is in the Background  

AI supports this effort by strengthening the reliability, predictability, and transparency of the payments experience, attributes merchants care about when deciding whether to adopt an integrated solution. AI-driven insights can streamline onboarding, improve authorization logic, and give merchants clearer visibility into how and when payments are processed. 

These improvements rarely make headlines, but they build trust in the system, reducing the friction merchants often associate with switching or adopting a new payments provider. 

As more purchase decisions happen upstream of the platform’s interface, the back end becomes the anchor of control.  This is why many software providers are starting to treat payments as a true product rather than an add-on: investing in stronger risk controls and refining pricing and payment options for their merchants. These fundamentals don’t change.  AI simply helps platforms apply them with far more consistency as volume grows and buying patterns shift. 

A New Layer of Risk and Responsibility  

Agent-led commerce doesn’t introduce new categories of risk –chargebacks, unclear authorization, and operational complexity –have always existed–but it does change their frequency and traceability. When purchases are initiated by automated systems rather than humans, the evidence trail becomes thinner, and the burden on platforms becomes heavier. 

For example, if an AI assistant buys the wrong product variation or quantity because it misinterpreted a customer prompt, and the customer later disputes the charge, the platform must prove what the agent did, what signals it acted on, and whether the user ever authorized that specific purchase. Today’s dispute frameworks weren’t built to capture that level of automated decision-making. 

Software providers can’t control where an agent initiates a purchase, but they can control how that transaction is validated, logged, and approved once it reaches their system. 

Complicating matters further, card networks are tightening dispute and fraud oversight. Visa’s Acquirer Monitoring Program (VAMP), which introduces stricter thresholds and enhanced scrutiny for acquirers and their merchants, is only one example of how dispute requirements are becoming more rigorous. 

Until dispute and liability frameworks evolve, software providers will need to strengthen credentialing, logging, authorization paths, and transparency within their systems. Whether a human or an agent triggered the transaction, the responsibility sits with the platform. 

What Happens Next 

The next phase of agentic commerce will grow in pockets, shaped by how quickly risk systems, authorization standards, and dispute processes adapt. Large ecosystems will continue experimenting with their own native assistants because it allows them to manage identity and authorization within their walls. Third-party agents will still exist, but they will operate inside more formalized rules and permissions. 

Consumer adoption will likely accelerate faster than merchant readiness. Until liability frameworks mature and platforms have clearer guidance on how to evidence intent, widespread use of agent-led checkout inside software platforms will likely remain gradual.  

AI will alter how transactions are initiated and completed, but the strategic fundamentals for software platforms embedding payments remain the same.  They must reinforce the parts of the payments experience they control: authorization logic, identity management, and visibility. Fraud systems must evolve to interpret automated behaviors, not just traditional user-driven patterns. The goal is not to prevent agent-led commerce, but to ensure that when automation occurs, the platform can still maintain integrity, predictability, and trust. 

Long-term advantage will belong to platforms that treat payments as a flexible, intelligent, and monetizable product, not a background system. The shift toward agent-led commerce makes that transition more urgent.