Agentic Collections
Automate dunning, dispute handling, and payment plan negotiation with guardrails. Use this page as a sample implementation pattern, then validate whether it belongs on your roadmap with an AI MRI.
How this pattern usually works.
Use this as a starting point. The AI MRI tells you whether this workflow belongs near the top of the backlog, what to fix manually first, and what needs to stay human.
- Segment customers by risk & balance
- Generate outreach with policy constraints
- Negotiate within allowed ranges; escalate exceptions
- Update CRM/ERP; close the loop with notes
- Inputs: AR aging, comms history, contracts
- Outputs: promises to pay, disputes, reconciled payments
- Regulatory language constraints
- Customer experience oversight with human‑in‑the‑loop
- Financial controls and auditability
Before and after.
Typical improvements observed when this kind of workflow is implemented well. Your baseline determines exact gains.
| Metric | Before | After |
|---|---|---|
| Days Sales Outstanding (DSO) | 52 days | 39 days |
| PTPT conversion rate | 18% | 29% |
| Monthly write-off rate | 2.4% of AR | 1.6% of AR |
Frequently asked.
Direct answers to the questions we hear most from operators evaluating whether this workflow belongs on the roadmap.
Agentic collections uses AI agents that can autonomously segment accounts, generate contextual outreach, negotiate payment plans within policy guardrails, and update systems of record. Unlike rule-based automation that follows rigid scripts, agentic collections adapts tone, timing, and terms based on customer history, risk profile, and response patterns.
Most organizations see DSO reduction of 5-15 days within the first quarter. The improvement comes from three factors: faster segmentation and prioritization, more consistent outreach cadence across all accounts, and earlier dispute identification that prevents receivables from aging.
Yes. The system enforces regulatory constraints at the policy layer, including contact frequency limits, required disclosures, and prohibited language. Every communication is logged with full audit trails, and human-in-the-loop escalation triggers automatically when conversations approach regulatory boundaries.
The AI negotiates within pre-configured policy ranges that define allowable terms, discount thresholds, and installment structures. It considers account history, balance, risk score, and customer segment to propose optimal terms. Any negotiation that exceeds allowed ranges is escalated to a human collector with full context.
The solution integrates with major ERP platforms (SAP, Oracle, NetSuite), CRM systems (Salesforce, HubSpot), and communication channels (email, SMS, voice). It reads from AR aging reports, contract terms, and communication history, then writes back promises to pay, dispute records, and reconciled payment updates.
Book an AI MRI intro. We'll confirm the pain point, the signal sources, and whether this workflow deserves a build now, later, or not at all.