On 1 April, Visa announced six AI tools to handle 106 million annual disputes — a staggering number that masks a simpler truth: this is infrastructure automation, not regulation-proof process design. The tools use generative AI to draft dispute responses and help merchants prevent chargebacks. For a payment processor, this is elegant. For a mid-market law firm, insurer, or financial services firm in the UK, copying this playbook would violate the FCA Consumer Duty (PS22/9) before the first AI response left the system. Visa operates in the pipes. Your firm operates at the customer-facing end. That matters.

This story is part of a larger pattern: AI vendors and platform operators are racing to embed generative AI into regulatory and compliance workflows because it works technically and cuts costs measurably. Harvey and Legora have built similar tools for legal disputes. Luminance does it for document review in complex transactions. Microsoft Copilot has infiltrated financial services firms' back offices. But each of these implementations sidesteps the same hard question: when an AI system makes or recommends a decision that affects a customer, who is accountable? The answer matters more in the UK than it does for payment networks. The PRA's SS1/23 guidance on ML governance and the ICO's emerging AI Act implementation framework both point to the same requirement: explainability and human oversight in material decisions. Visa's dispute tools solve speed. They don't solve governance.

Here's Trovix's honest view: Visa's approach works because disputes are binary decisions operating at infrastructure scale with high tolerance for occasional error. Your firm handles decisions that are singular, high-stakes, and subject to individual customer redress. Generative AI alone — even well-trained generative AI — is not fit for that. It needs three things Visa doesn't require: first, document intelligence that extracts the precise facts from your intake process (not general-purpose LLMs guessing at meaning); second, knowledge assistance that ties recommendations to your firm's actual policies and regulatory obligations (not broad pattern matching); third, regulatory monitoring that flags when external rules change (not static training). Products like Trovix Sift handle extraction with explainable outputs. Trovix Aria builds knowledge systems tied to your own controls. Trovix Watch catches regulatory change before it catches you. The difference is that none of these assume the AI decision is the final one. They assume a regulated professional is accountable, and the AI is evidence in that accountability chain.

If you're a mid-market law firm, accountancy practice, or financial services firm, the move to make right now is not to rush into dispute automation or response drafting. The move is to audit how many material decisions in your firm are already being influenced by AI tools your fee-earners are using without formal governance. Then ask: does your oversight model meet FCA Consumer Duty, SRA Code, or FRC ISA UK standards? Does it create an explainable record? If not, that's urgent. Visa can afford to iterate because they're infrastructure. You can't. Build governance first, then add AI to the processes where you can afford to be wrong. The firms that get this backwards will spend 2027 explaining themselves to regulators instead of explaining themselves to clients.

Source: CNBC