Transformance runs this loop with CollectPulse and its agent, Vero: 100% of overdue invoices are actioned within 24 hours, against 30-40% for manual teams. This guide covers B2B accounts receivable, not consumer debt. It walks through each step and what changes when an agent, rather than a rule set, does the work.
Key Takeaways
- AI collections changes seven steps of B2B collections: prioritisation, dunning, outreach, calls, promise-to-pay tracking, dispute handoff and the cash forecast.
- Rule-based dunning sends the same message on fixed days; an AI collection agent decides who to contact, writes the message, calls and acts on the reply within limits you set.
- With CollectPulse, Vero handles 60-80% of routine touches and makes 15-20 calls an hour in 70+ languages, so collectors keep the judgement calls.
- Disputes found during collections go to deductions with their evidence, and promises to pay move expected cash into the right week of the forecast.
- People stay in control through approval levels, an audit trail and an AI caller that identifies itself as AI at the start of every call.
In This Article
- Key Takeaways
- What is AI collections?
- Why isn’t automated dunning enough for B2B collections?
- How does AI improve collections at each step?
- Example: one overdue invoice, before and after
- AI collections agents vs traditional dunning software: what is the difference?
- Where do people stay in control?
- How to get started with AI collections software
- Every overdue invoice gets a next step
What is AI collections?
AI collections is collections automation in which models and an AI agent, such as Vero in Transformance, decide which overdue B2B invoices to work, send the emails, make the calls, interpret the replies and record each outcome against the invoice.
It is software that runs inside your own AR process, not a third-party collection agency. It sits on top of the accounts receivable collections process and replaces manual touches, not your credit policy.
Nearly four in five Western European companies say B2B customers pay invoices late, and about one quarter of invoices are paid after the due date (Atradius, 2026). The B2B payment gap, actual payment time against agreed terms, widened from 16 days in 2023 to 20 days in 2026 (Intrum, 2026).
Late payment is the normal state of a B2B ledger. The practical question is how many overdue invoices your team can actually touch.
Why isn’t automated dunning enough for B2B collections?
Automated dunning sends reminders on schedule, but in B2B the cash is often held up by things a reminder can’t fix: the wrong contact, a short payment, a disputed price, or a customer who answers calls and ignores email. When a customer stops answering altogether, see what to do when a B2B customer goes silent on overdue invoices.
Then there is capacity. A collector makes 15-20 calls a day, so the long tail of smaller invoices never gets a call, and every invoice that waits adds to DSO.
Those days have a price. The European Commission estimated that a one-day reduction in payment delays would increase EU companies’ aggregated cash flow by 0.9% (European Commission, 2023).
Rule-based dunning is the right base layer and stays in place. AI changes who decides and who acts.
How does AI improve collections at each step?
AI changes seven steps, and each one feeds the next: the risk score sets the outreach, the outreach produces replies, and the replies become promises, disputes or escalations that update the forecast.
Worklist prioritisation and payment prediction
Without AI, the worklist is sorted by AR aging bucket and balance. Predictive modelling scores each open invoice on the probability and expected date of payment, using payment history, invoice age and size, dispute history and how the customer responded last time.
The worklist is then ranked by expected cash impact. The output: every overdue invoice has a next action and an owner, agent or person, on the day it falls due.
Automated dunning that adapts to the account
Your delinquency bucket logic stays as the policy frame. Inside it, the AI picks timing, channel and tone per account. Timing only helps if the reminder reaches the inbox: see why dunning emails land in spam.
A reliable payer three days late gets a short reminder with the invoice copy attached. A habitually late customer with a large balance gets an earlier call. Cadence design is covered in our guide to dunning email templates and automation. In CollectPulse, Vero handles 60-80% of these routine touches.
Personalised outreach in the customer’s language
Vero writes each message from the invoice, the account’s open items, past disputes and prior conversations, because it works on Customer 360: one graph per customer that acts as its working memory. It knows the last promise and the last dispute before it writes.
Messages go out in the customer’s language without native-speaker headcount. Where one shared service centre collects for several legal entities and countries, each with its own terms and escalation authority, see enterprise dunning automation for multi-entity groups.
AI calling agents for the accounts nobody had time to call
The Transformance calling agent in CollectPulse makes 15-20 calls an hour in 70+ languages, against 15-20 a day for a human collector. This AI collection agent identifies itself as AI at the start of every call, then confirms the open invoices, asks for a payment date and records the outcome. For how the call works step by step, see the AI calling agent guide for accounts receivable.
Promise-to-pay tracking that follows up on its own
In many teams, a promise to pay ends up in a notes field. An AI agent logs each promise from an email reply or a call against the invoice, with date and amount, and follows up the next day if the money doesn’t arrive. CollectPulse captures promises to pay at 3x the manual rate.
Disputes handed to deductions, not left in the dunning cadence
In a rule-based setup, a disputed invoice keeps receiving reminders until someone pauses it. With an agent, a reply or call that reveals a dispute or a short pay (a pricing difference, missing proof of delivery, a promotion, damaged goods) stops dunning on that amount. The case goes to dispute management with the evidence attached.
It lands in ClaimIQ, which identifies deductions with 97% accuracy and classifies them into six categories. The undisputed balance stays in collections, so a single disputed line no longer holds up the whole invoice.
Cash forecast feedback
With an agent, promise-to-pay dates, disputed amounts and payment predictions flow into the cash forecast, so expected cash sits in the week the customer committed to and disputed cash is held out.
CashPulse, which forecasts at 90-95% accuracy (100 minus MAPE) out to 90 days, works from those dates. Forecast misses feed back into prioritisation, so a broken promise moves the account up the next worklist.
Example: one overdue invoice, before and after
This example uses illustrative figures. A chemicals group’s Italian entity invoices a distributor EUR 48,000 on 60-day terms, and the group’s shared service centre collects for 11 entities from one team.
Before: rule-based dunning and manual follow-up
- Day 3 past due: an English-language level-one reminder goes out from the template.
- Day 17: the invoice reaches a collector who speaks no Italian and sends an email.
- Day 24: the customer replies that EUR 6,200 is a pricing difference on one line and pays nothing until it is resolved. The note goes into a spreadsheet; dunning keeps firing on the full EUR 48,000.
- The cash forecast still shows EUR 48,000 arriving on the due date.
After: an AI collections agent
- Day 1 past due: the invoice is ranked high because this customer pays late at quarter end.
- Day 1: Vero sends an Italian email with the invoice copy and statement of account. No reply.
- Day 2: Vero calls in Italian, identifies itself as AI and hears the pricing objection. The customer commits to pay EUR 41,800 by the 28th.
- Same day: the promise is logged against the invoice, and the EUR 6,200 goes to ClaimIQ with the price agreement attached; dunning stops on that line.
- The forecast moves EUR 41,800 into the week of the 28th and holds EUR 6,200 as disputed.
- The collector has one task left: approve the dispute route.
The agent sent no more messages than the rule set did. What changed is timing: the dispute surfaced on day 2 instead of day 24, and EUR 41,800 sits in the right forecast week.
AI collections agents vs traditional dunning software: what is the difference?
Traditional dunning software runs the rules you wrote. An AI collections agent runs inside those rules but decides who to contact, when, in what language and channel, and acts on what the customer says.
The right-hand column is agentic AI applied to collections. Intrum estimates that AI can reduce the costs of chasing late payments by around 20%, based on reported time savings in the payments function (Intrum, 2026).
A small ledger with few customers, one language and a low dispute rate can run well on rule-based dunning. The case for an agent grows with volume, entities, languages and disputes.
Where do people stay in control?
The agent acts at the level you grant it, every action is logged, and anything outside policy goes to a person.
Transformance uses a four-level approval model (read, recommend, execute, post) with a human in the loop for ERP postings. Every action sits in a full audit trail. SSO/SAML and role-based access control govern access, and VPC deployment keeps data in your cloud.
People keep strategic accounts, payment plans and negotiated terms, credit holds, and disputes that need a commercial decision. An escalation matrix sets who receives which case.
Vero opens every call by identifying itself as AI. It runs in Microsoft Teams, where collectors already work.
How to get started with AI collections software
Start with one entity, a baseline and a written policy, then widen the agent’s authority as its output proves itself.
- Pick one entity with a real overdue backlog and measure today’s baseline: DSO, collections effectiveness index, the share of overdue invoices touched within 24 hours, and the promise-to-pay kept rate.
- Connect the ERP and the collections inbox. The platform connects to SAP S/4HANA and ECC, Oracle, NetSuite and Dynamics.
- Write the policy the agent works inside: buckets, tone limits, who may approve payment plans, and which accounts stay with people.
- Start at “recommend”, then move routine reminders and calls to “execute” once the outputs match what your collectors would do.
- Run a proof of value against that baseline.
Transformance runs that proof of value in 4-5 weeks on one entity, and a full rollout takes up to 4-8 weeks. B2B collections software vs services covers when an agency still makes sense.
Every overdue invoice gets a next step
AI collections makes collections faster in one specific way: every overdue invoice gets a next step on the day it falls due, and every reply becomes a recorded outcome. The rules stay as policy, the agent does the volume, and collectors keep the decisions.
Frequently Asked Questions
How does AI improve collections?
AI improves collections by ranking overdue invoices by payment risk, contacting every account on time, and turning each reply into a recorded next step: a promise to pay, a routed dispute or an escalation. The gain comes from coverage and speed: manual teams action 30-40% of overdue invoices within 24 hours, while CollectPulse actions 100%.
What is AR collections automation?
AR collections automation is software that runs the follow-up on overdue receivables, from reminders and statements to calls, promise-to-pay tracking and dispute routing, so collectors don’t do each touch by hand. Rule-based tools automate the sending; AI tools also decide and act on replies.
What is the difference between AI collections agents and traditional dunning software?
Traditional dunning software sends pre-set messages on fixed days, while an AI collections agent, for example Vero from Transformance, decides who to contact, writes and sends the message, calls the customer and acts on the answer within limits you set.
Which AI collections software executes tasks instead of just generating worklists?
Look for software whose agent sends the emails, makes the calls and logs each outcome itself under approval levels, rather than handing a ranked list to a collector. CollectPulse works this way: Vero handles 60-80% of routine touches.
Does the AI calling agent say it is an AI?
Yes. Vero identifies itself as AI at the start of every call, including B2B calls to a customer’s accounts payable clerk.
Will AI replace collections teams?
No; AI takes the routine volume, and collectors keep the decisions that need judgement, such as strategic accounts, negotiated payment plans, credit holds and commercial disputes. With the agent on 60-80% of routine touches, the same team covers the whole ledger.
How much can AI collections reduce DSO?
CollectPulse customers see DSO fall by 8-12 days on average (Transformance customer data, 2026). Each day counts: the European Commission estimated that a one-day cut in payment delays would lift EU companies’ aggregated cash flow by 0.9% (European Commission, 2023). The DSO reduction playbook covers the other levers.
