AI AR Collections Agent: What It Does and How
Your AR team is not slow. They are working an aging report that grows faster than any manual process can keep up with. The result is predictable: smaller accounts wait days or weeks for a first contact, larger balances consume the team's full attention, and days sales outstanding climbs not because of bad customers but because of inconsistent outreach volume. An AI AR collections agent changes that without changing who is responsible for the accounts or how your ERP is configured.
The gap in most AR operations is not judgment. Your finance team knows exactly what to say on a collections call. What they do not have is the capacity to make every call, document every outcome, and follow up every commitment on schedule across hundreds of accounts every week. That is a structural problem, and personal effort alone will not resolve a structural problem.
An AI agent changes the structure. It takes the outreach work, the documentation, and the follow-up off the team's plate while routing decisions that actually require human judgment to the right person with full context attached.
Most AR teams have a functional collections process. The workflow is clear: call the account, request payment, document the response, follow up if the commitment is missed. The problem is that this workflow does not scale with the aging report.
A collections team working manually can typically reach a fraction of open accounts each day before other responsibilities fill the calendar. At 300 or more past-due accounts, a significant portion of the portfolio goes uncontacted on any given week. Accounts in the 31-to-60-day bracket age into the 61-to-90-day bracket not because nobody noticed but because there was not enough time.
The consequence is direct. Delayed cash, rising DSO, and accounts that could have resolved with a first call at 35 days now require escalation at 65. The carrying cost compounds with each week of missed contact, and no amount of effort from a fixed-size team closes that gap permanently.
An AI AR collections agent connects to your ERP aging data, reads past-due accounts against criteria your team defines, and works through the outreach queue automatically. It places outbound voice calls to customer contacts on record, conducts the conversation, requests payment, and records any commitment made. None of that requires a staff member to initiate or monitor each call.
After each call, the agent logs an outcome with a disposition code back to your ERP. Call recordings and transcripts are available for every interaction. If a payment commitment is made and not met, the agent follows up automatically on the defined schedule.
Accounts that fall outside the agent's configured parameters, including disputed invoices, accounts above escalation thresholds, and any situation the agent flags as requiring a person, get routed to your team with a full call summary attached. The agent produces a structured escalation queue rather than a mixed inbox of flags and exceptions that require manual sorting.
An AI AR collections agent connects to your existing ERP through an API integration. It reads aging data directly from your system, applies your defined escalation rules, and writes disposition codes and call outcomes back to that same system after every interaction. Your ERP is not replaced and no parallel system is introduced.
The inputs the agent requires are already in your system: invoice details and aging buckets, the primary contact number for each accounts payable contact, payment terms on file, escalation thresholds your team defines, and read-write access to pull data and log results. That configuration happens once before the agent goes into production.
This also means the agent produces a structured record your finance team can work from directly, rather than a spreadsheet of call notes assembled by hand or a queue of emails with uneven documentation.
Routine outreach, the calls that follow a predictable script and end with a payment date or a voicemail, moves to the agent. Your team works from a structured escalation queue of accounts that need human judgment: disputes, relationship-sensitive accounts, balances above defined thresholds, and situations the agent flags as requiring a person.
In a representative engagement, a finance team working a collections queue of several hundred open accounts saw routine outreach time drop to 4 to 6 hours of oversight per week. Before deployment, that same work had consumed 15 to 20 hours. The hours that remained after deployment were applied to escalations and complex accounts, not to dialing, leaving voicemail, and updating records.
The agent also produces consistent output across every account regardless of balance size. The 200th call in a week runs the same as the first. That consistency across the full aging portfolio is what closes the coverage gap between accounts a manual process reaches and the ones it does not.
A pattern seen across similar operations shows the cost of inconsistent outreach in specific terms. Before deployment, smaller accounts are routinely bypassed when larger balances demand priority. DSO drifts toward 50 days not because of any single failure but because lower-balance accounts receive no outreach until they age past the point where recovery is straightforward.
After a collections agent connects to the ERP and works every account on a defined schedule, the coverage gap closes. DSO reductions to the 38-to-42-day range within 90 days have been documented in similar implementations. At $200 million in revenue, a 10-day improvement in DSO frees approximately $5.5 million in working capital.
Those figures depend on starting conditions including volume, data quality, and organizational readiness. What does not vary is the mechanism: full portfolio coverage, applied consistently, produces better outcomes than selective manual outreach.
Confirm whether your ERP has the API access the integration requires. Read-write access to pull aging data and log call outcomes is the baseline technical requirement. That question is answered during the assessment phase before any build begins.
Document your escalation rules before engaging. Your team already knows which accounts get called, in what order, and what triggers an escalation. That knowledge needs to be stated clearly so the agent applies it consistently rather than requiring staff to override it after the fact.
Confirm who owns the escalation queue after deployment. The agent fills that queue. A designated person needs to work through it regularly for human oversight to function as designed.
Measure your current DSO and outreach coverage before deployment. Results compared against a baseline are evidence. Results without one are harder to act on when the pilot concludes.
An AI AR collections agent does not replace your finance team. It removes the outreach work the team should not be spending time on, so judgment is applied where judgment is actually required. The team that deploys one does not shrink. It shifts.
If you want to understand whether this applies to your operation, book a call with Tayana Solutions at tayanasolutions.com.
The agent connects to your existing ERP through an API integration, reads aging data, and writes outcomes back to the same system after each call. Your ERP is not replaced or reconfigured. Integration feasibility is confirmed before any build begins.
Routine outreach moves to the agent. Your team works from a structured escalation queue of accounts that fall outside the agent's defined parameters, including disputed invoices, relationship-sensitive accounts, and high-value balances requiring human judgment. The division of work is defined by your team before the agent runs.
DSO reductions to the 38-to-42-day range within 90 days have been documented in similar implementations. The mechanism is full portfolio coverage: the agent contacts every past-due account on schedule, including smaller-balance accounts that manual processes often bypass when larger balances demand priority.
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