New data from Access PaySuite highlights that payments failure remains a persistent source of lost revenue for SMEs. Until businesses are able to process transaction data with accuracy and in real time, payment failure will remain a bottleneck within the system. The use of AI in this context is grounded in data interpretation, rather than abstract automation.
The Access PaySuite research reveals that 3.4% of transactions fail, and more than half of these are never recovered. Nearly 50% of businesses report a loss of customers due to checkout abandonment, while finance teams spend hours each week manually managing failures. Taken together, this points to a structural issue rather than isolated inefficiencies.
At its core, this is about the checkout experience. Payments are often treated as a technical endpoint, but in practice they are a critical part of the customer journey. A failed transaction, whether due to a soft decline, expired card, or friction in the payment flow, interrupts intent at the most sensitive moment. Unlike earlier stages of the funnel, there is little tolerance for disruption here. If a payment fails, the customer often does not return.
Checkout reliability carries the same weight as conversion itself. For SMEs, where margins and customer acquisition costs are tight, even small failure rates can compound into meaningful revenue loss.
Jon Reynolds, Head of Product at Access PaySuite, identifies the problem:, “The real challenge for many payments teams is visibility. Without a consolidated view across authorisation rates, decline codes, recurring billing performance and checkout behaviour, it’s difficult to optimise payment flows or improve recovery rates.”
The underlying constraint is fragmentation. Many businesses still operate with payment data distributed across separate systems for billing, acquiring, and reporting. This fragmentation makes it difficult to understand why transactions fail, which failures are recoverable, and where intervention is most effective.
AI is being positioned as a solution. Tools that allow teams to query performance, identify patterns in decline codes, or surface recurring billing issues can reduce the operational burden and improve recovery rates.
We can see from Access PaySuite’s data that as an industry we are set to move toward more observable payment systems. Infrastructure is evolving to provide richer, real-time data on transaction performance, and businesses are being pushed to treat payments with active management rather than with passive processing. The value of AI within this shift is in faster diagnosis and more informed decision-making, not a fully automated optimisation pipe dream.
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