Collections and dunning turns overdue receivables into cash: it sets collections policy and escalation rules, segments and prioritizes the AR aging by risk, value, and payment behavior, runs automated dunning sequences across email, SMS, portal, and letter, predicts payment propensity and flags high-risk accounts, negotiates payment plans and records promise-to-pay, escalates unresolved accounts to internal teams or agencies, processes adjustments, disputes, and write-off recommendations, and reports on DSO and collector performance.
The 8 tasks — the nature of each, and the oversight it needs
Tag each task in plain terms — what kind of work it is and how hands-off it can run — before any mention of AI. The kind of work is what later decides which tool, if any, fits.
A predict-and-persuade process. ML prioritizes the AR book by who is likely to pay and who is at risk, and automation runs the dunning cadence — but the binding step is the human conversation: negotiating a payment plan and taking a promise-to-pay. No system autonomously commits a customer to terms, and the relationship, dispute, and write-off calls stay with a person. That relationship ceiling is why collections tops out around 35-55% AI value even at high confidence.
Aggressive or mistimed collections damages customer relationships and can breach regulation — prioritization is AI-driven, but negotiation, disputes, and write-offs stay with a person.
The question isn’t only “is there savings” — it’s can I run this better: cheaper, faster, higher quality, better service? Here’s what best-in-class looks like, and how teams get there. (How much of it AI specifically drives — and how proven that is — is Section 04.)
Process discipline first, then automation — AI is one slice of the second column, not the whole answer.
- Risk-based collections segmentation
- Promise-to-pay & payment-plan playbooks
- Escalation & write-off approval thresholds
- Deduction / dispute standards
- ML payment-propensity scoring
- Automated multi-channel dunning
- GenAI personalized outreach
- Collections performance dashboards (DSO)
“Who runs the work” is its own question, separate from AI. AI shows up across these options — sometimes heavily, sometimes not at all. Vendor-neutral; the real options mapped to AR02.
Recall the tasks and their nature from Section 01. AI is one lever, not the whole story — the mix below is simply the result of matching the right kind of solution to each kind of work, weighted by where the work concentrates.
ML/Predictive leads (35%) because the differentiator in modern collections is payment-propensity scoring and risk-based prioritization of the AR book — what every major platform (HighRadius, Sidetrade, Quadient) foregrounds. NLP is unusually high (25%) because dunning and customer communication — reading replies and disputes, personalizing outreach across channels, parsing promise-to-pay language — is language work. Agentic/RPA (20%) runs the dunning cadence; GenAI (15%) drafts outreach; Document AI supports.
The right lever fits your volume, variability, control needs, and appetite to operate a system. Start here.
The autonomy question: agent or copilot?
Whichever delivery model you pick, one choice cuts across them — who presses enter.
AI agent
Runs the steps end-to-end, completes the clean cases on its own, and routes only the exceptions to a person.
AI copilot
Sits beside the person and speeds up each step; the human acts on every decision.
What to evaluate — whichever you choose
- Accuracy on your own inputs — vendor benchmarks are on clean data; test your messiest cases.
- Straight-through / touchless rate — the real efficiency number, not “AI-powered.”
- Exception-handling experience — most of your team's time goes here, not the happy path.
- ERP write-back & integration depth — does it post cleanly to your system of record?
- Data residency — does data leave your environment, and is that acceptable to compliance?
- The accountability surface — what happens, and who owns it, when the model is confidently wrong?
See every lever across your processes
Run your portfolio through the assessment — work profile, improvement potential, confidence, and executor options across all your blocks, scored against 127 enterprise subprocesses.
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