B2B payments are stuck in a trillion-dollar jam, with too many manual steps, fragmented systems, and overdue invoices creating a massive cash gap.
Complexity has become a permanent feature of the business payments market. Too many rails, too many exceptions, and too much manual work characterize the current state. Grant Halloran, CEO of Billtrust, described the situation as physically messy. He noted that buyers pay through different channels while suppliers operate across fragmented systems. Checks persist alongside cards and digital rails, creating a disjointed environment.
The inefficiency has a measurable cost. Halloran estimated that nearly 60% of invoices are overdue at any given moment. He also pointed out that there is between $1.5 trillion and $2 trillion of trapped cash sitting in the B2B economy. This idle capital represents value lost to businesses facing raised borrowing costs and rising days sales outstanding.
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Companies are attempting to fix these issues by automating parts of the order-to-cash process. The goal is to continuously decide how each dollar of receivables should be converted into cash without damaging customer relationships. Halloran argued that businesses are trying to generate the most cash possible from their receivables at the fastest rate and at the best economics.
Historically, solving this magnitude of problem would take a long time. Halloran believes the integration of artificial intelligence is changing that timeline. He stated that in this new era, companies are able to execute rapidly on strategies they previously could only dream about. The shift represents a move from simple administrative tasks to a working-capital priority.
This transition alters how companies view collections. Instead of treating a portfolio of overdue invoices as a simple queue, they can use data to distinguish among buyers based on behavior, economics, and relationship value. Halloran calls the moment of payment the “magic moment” inside a broader cash-generation process full of signals. These signals allow for intelligent decisions about how receivables should be managed.
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The mechanics of moving money are becoming easier, but the harder problems in receivables sit upstream. Companies often lack a precise, real-time understanding of why cash they have already earned has not arrived. Halloran noted that the amount of data involved is profound. With a trillion dollars of trade credit commerce going through platforms, there are billions of events and behavioral signals that bring a data science lens to the process.
This data abundance suggests a future inversion in the operating model. Machines can handle repetitive decisions around invoices, collections, and payment routing. Humans then become responsible for exceptions where customer relationships, commercial context, or strategic judgment outweigh the algorithmically obvious answer. The value of AI is not to eliminate judgment but to concentrate it where the economics justify it.
