A third of SMEs lose a working day a month to late payments

Picture the start of a Wednesday morning. A finance team member opens their inbox and spends the next two hours writing chaser emails, cross-referencing an aged debtor report, and calling a client who still has not paid a 60-day invoice. No new revenue is generated. No strategic work gets done. This is not an edge case. Research shows that almost one-third of UK businesses spend more than six hours every month chasing overdue invoices, the equivalent of losing nearly a full working day to repetitive admin. For SME owners and finance teams already stretched thin, that cost is not trivial. For fire installers waiting on main contractors, and for law and accountancy firms chasing fees, it bites harder still.

The scale of the late payment problem

Late payment is not a new challenge for UK small businesses. What is new is how precisely we can now measure its cost. When almost a third of businesses are losing a full working day each month to chasing invoices, the aggregate impact across the UK economy is significant.

For a firm with 20 to 500 team members, that time has a direct financial value. An owner or finance manager spending six-plus hours on debtor follow-up is not reviewing margins, meeting clients, or planning cashflow strategy. The hidden cost compounds: delayed inflows tighten working capital, and stress on small teams without dedicated credit control functions is real.

The problem is also predictable. Overdue invoices follow recognisable patterns: the same clients, similar intervals, repeated message cycles. That predictability is exactly what makes manual credit control a strong candidate for AI automation.

Why manual chasing is an AI-ready process

Not every business process is well-suited to automation. Late payment chasing is one of the clearest exceptions. It is high-volume, rule-based, and time-sensitive, which means it maps directly onto what AI does well.

Consider the typical manual workflow. A finance team member checks an aged debtor report, identifies overdue accounts, drafts a reminder email, sends it, logs the activity, and waits. Then repeats. Each step is largely the same. The only meaningful variation is tone: a first reminder reads differently from a third.

AI can handle debtor prioritisation based on invoice age, value, and client history. It can generate tailored follow-up messages that adjust tone as days overdue increase. It can integrate with accounting platforms to trigger reminders automatically on a schedule. And it can log every action, creating an audit trail that a manual process rarely produces consistently.

This is not about removing human judgement from credit control entirely. It is about removing the mechanical steps so that people focus on the conversations that actually require their expertise.

What the evidence tells us about AI and finance admin

The pattern of AI reducing time on repetitive finance tasks is well-established across small business research. Case studies examining real AI automation deployments consistently show that structured, rules-based tasks, including invoice management and debtor follow-up, are among the earliest and clearest wins.

The reason is straightforward. AI performs best when the inputs are structured and the outputs are defined. An aged debtor report is structured data. A follow-up email sequence is a defined output. The matching of one to the other is exactly the kind of task that AI handles reliably and at scale.

For SME owners without a dedicated credit control function, this means the capability previously available only to larger firms, automated, consistent debtor follow-up, becomes accessible. Research into small business AI adoption indicates that finance and operations are among the most common starting points precisely because the return on the time investment is measurable and fast.

What an AI-assisted credit control process looks like

For SME owners and finance teams considering this approach, the practical implementation follows a recognisable sequence.

First, the business connects its accounting platform, whether that is Xero, QuickBooks, or another tool, to an automation workflow. The workflow monitors invoice due dates and triggers actions based on configurable rules: a gentle reminder at seven days overdue, a firmer message at 21 days, an escalation flag at 45 days.

Second, an AI Assistant drafts each message using the client's name, invoice reference, and outstanding amount pulled from the accounting data. The tone is calibrated to the stage: cordial early on, direct later. The finance team reviews and approves, or sets the system to send automatically within defined parameters.

Third, every action is logged. The audit trail shows what was sent, when, and to whom. If a dispute arises, the record is complete.

Finance teams do not disappear from this process. They approve templates, handle escalations, and manage clients where a relationship call is more effective than an automated message. The distinction matters: AI handles the mechanical volume, people handle the judgement calls.

Implementation safeguards

Automating invoice chasing introduces compliance considerations that SME owners and finance teams need to take seriously. UK businesses must comply with the Late Payment of Commercial Debts (Interest) Act 1998 and its regulations. Any AI-generated communication referencing interest charges or statutory compensation must be accurate and fair.

This means reviewing every message template before it goes live. AI can draft at speed, but the legal responsibility for what is sent remains with the business. Templates should be checked by someone with knowledge of the regulations, and any message that references statutory rights should be reviewed with particular care.

A second consideration is tone. Automated messages that feel aggressive or impersonal can damage client relationships. Good implementations use AI to handle early-stage reminders while reserving direct human contact for accounts where a relationship matters or where the situation is sensitive.

Finally, data handling matters. Any system connecting accounting data to email or CRM platforms should be assessed for GDPR compliance. Client financial data is personal data. The integration should operate within a secure, documented setup.

Making this work for your business

For SME owners and finance teams ready to act on this, the starting point is mapping the current manual process. How many invoices go overdue each month? How many hours does chasing them consume? What is the average days-to-payment for late accounts? These numbers establish the baseline against which any automation can be measured.

From there, the integration between accounting platform and communication tools is the technical step. Most modern accounting platforms support API connections or native integrations with automation platforms. The workflow itself, trigger on due date, draft message, send or queue for approval, can be built without code.

The AI Assistants approach works well here: by automating debtor prioritisation, generating tailored follow-up messages, and integrating with accounting platforms, finance teams can eliminate manual routing and focus on negotiation and exception handling instead. gecco's AI Assistants service builds exactly this kind of structured workflow, connecting accounting and communication platforms to replace repetitive manual steps with consistent, auditable automation.

Measuring the result is straightforward. Track time spent on invoice chasing before and after. Track average days-to-payment. Track the proportion of invoices settled within terms. These are measurable outputs that make the business case visible.

Start with a clear picture of where you are

If your finance team or you as an SME owner is spending hours each month on invoice chasing, this is exactly the kind of process the AI Readiness survey is designed to surface and prioritise.

Take the AI Readiness survey. You will receive 65+ free resources and a custom AI Readiness report based on your answers. From there you can book a free 30-minute showcase to walk through the results with a gecco adviser.

---

Website · LinkedIn · Case Studies · Newsletter