What AI Looks Like in Payments in 2026

If you’re in the payments world, you’re likely hearing from the top down that you need to be using AI in your business and workflows. Then again, who isn’t these days? But it makes particular sense in payments and collections due to the high volume of repetitive activities that follow clear business rules and are therefore well suited for automation and AI. 

For many people tasked with incorporating AI into their products and processes, it starts with asking, “How can we use this right now?” To help answer that question, we look at how automation and AI in payments have evolved and where the industry stands today. 

PayNearMe has spent years helping businesses manage complex payment experiences through Payment Experience Management (PEM), the discipline of owning and managing the entire payment experience and continually finding ways to make it better. We’ve seen firsthand where automation works well and where businesses still need human intervention.

From that vantage point, we see a clear evolution taking shape.

Executive summary

Automation and AI have come to the payments industry in waves, with the first wave focused on automating clearly defined, predetermined processes and the second focused on making self-service support interactions easier. These early systems were largely reactive and assistive, able to explain what happened or handle simple, scripted interactions but often defaulting to human support when something went wrong. 

AI in the payments industry is currently in a very early stage transitional period, moving from automation and assistance to more autonomous, decision-capable systems. Agentic AI will be able to take the next step, not only explaining the problem, but also interpreting context, accessing real-time data and standard operating procedures, applying business rules and taking the next best action to solve the problem in real time. 

The third wave is coming sooner rather than later, and we’re excited for what it can help achieve. Let’s take a closer look at how companies are implementing AI today.  

Automating clearly defined processes

Many businesses started by automating previously manual, rules-based processes with clearly defined outcomes. This is called deterministic automation, which uses fixed rules and pre-determined logic and doesn’t ever change its behavior based on new data or interactions. 

The benefit of this is reliability with minimal oversight. As long as the predetermined paths are correct, the system will carry them out consistently. The shortcoming is that the moment an interaction strays from a predetermined path, creating what we like to call friction in the payment experience, human support is often required. 

Automation in support and operations

This brings us to the next wave, which was automating self-service customer support. Customer service automation started with technologies such as interactive voice response (IVR) and scripted chatbots. IVR systems and scripted chatbots could handle some of the most basic customer service tasks but still relied on predetermined workflows.

Some companies have upgraded IVR systems to intelligent virtual assistants (IVAs), creating more conversational interactions, but many still rely heavily on predetermined scripts and workflows. IVAs can handle simple instructions, but when something goes wrong—a declined payment, a hardship request, a policy exception—the system often breaks and hands the ticket off to a human. 

Which leads us to the big, “so what?” moment.  

As we pointed out in our recent report The Payment Experience Gap, every assisted interaction across phone, chat and email costs you about $13.50. Which doesn’t sound like a lot, but compare it to the pennies it costs for self-service, and then multiply that difference by thousands of support interactions each month. It adds up, fast. 

This need for manual support and intervention is one of the biggest drivers of the $100 billion in annual payment acceptance costs beyond transaction fees that businesses aren’t seeing on a processor invoice.

Wrapping up the current state of AI in payments

Automation in payments is already fairly prevalent in certain workflows. The first wave applied automation to highly scripted, rules-based processes. A second wave made customer self-service interactions more conversational and easier to use, but many systems still default to costly human intervention when events fall outside the script.  

The next major shift is already beginning: the industry is moving from traditional scripted automation toward agentic systems that can understand context, make decisions and execute tasks within defined guardrails. In other words, these systems are closer to true intelligence and more capable of solving complex issues when customer interactions stray from the easy path. 

But giving AI the ability to act raises a much bigger question: What does an agent need access to in order to act effectively in payments? We’ll explore that next.