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Downloading remittance files, re-sending PDFs to the same AP contact for the third time, logging into customer portals, and manually reconciling payments in the ERP, is what take the longest. When revenue grows and headcount stays flat, those tasks don't shrink. They compound: Smaller accounts get ignored, past-due balances age past 60 days, and DSO climbs.
Cash application automation addresses this directly by doing the repetitive work, not organizing it better. The question AR Directors face is where the hours actually go and whether automating those tasks frees enough capacity to matter.
Payment matching sounds simple until your analyst is doing it every day across hundreds of accounts, partial payments, bulk Stripe deposits, and multi-invoice wires where the remittance detail is missing or wrong. The collections teams at most mid-market companies aren't stuck because of bad processes. They're stuck because the volume of routine work leaves no room for the complex work.
Manual payment matching creates a compounding bottleneck. An AR analyst receives a bank file, opens the ERP, searches for the matching invoice, handles partial payments individually, and then researches any gaps. Across our deployments, payment matching is consistently the single largest time drain we see. When that work isn't finished same-day, cash sits unapplied, aging reports look worse than they are, and month-end close extends by days.
Stuut addresses the bottleneck at the source. Stuut's three-way matching algorithm parses remittance data from bank accounts, lockboxes, and digital payment rails, handles exact matches, partial payments, overpayments, and bulk deposits, and posts cash application entries to the AR subledger in real time. Stuut targets a 95%+ automated match rate, reducing cash application turnaround from days to minutes.
Bounced emails, wrong AP contacts, and customer portals that require manual invoice submission are the most time-consuming tasks that add the least value. An AR analyst chasing a $4,000 invoice that went to the wrong department isn't doing collections work. They're doing clerical work that any automated system could handle.
As covered in Stuut's post on why collections teams shouldn't be email detectives, when an analyst manages a large portfolio of accounts, the hours spent re-sending documents and tracking bounces eliminate any possibility of proactive relationship management.
Unapplied cash delays month-end close and distorts the aging report, making it harder to prioritize collections accurately. Deductions create a separate drain: an AR team that can't process early-pay discounts, damaged goods claims, or trade promotion deductions within filing windows writes off revenue that was already earned. Stuut automatically categorizes and processes deductions, applies contractual terms for early-pay discounts, and files recovery claims for invalid deductions, recovering revenue that would otherwise disappear from the aging bucket without a clear reason code.
The productivity gain from cash application automation isn't abstract. It shows up in the first week as reduced email volume, fewer manual ERP entries, and an AR analyst who spends Tuesday morning reviewing exceptions rather than generating them.
EZG Manufacturing saves approximately 20 hours per week by automating payment matching, invoice resend workflows, and exception research that the AI handles autonomously.
Stuut's AI agent handles the following tasks autonomously, escalating exceptions for analyst review:
Stuut's AI learns metadata most ERPs never capture, such as originating company numbers and bank transaction identifiers, so future payments from the same source match instantly without manual rule configuration.
The table below consolidates our published performance metrics from live customer deployments:
These metrics come from live customer deployments, not projections. Results vary by portfolio mix, ERP complexity, and existing AR process maturity.
Reducing manual work isn't the end goal. The goal is what your team does with the time. After cash application automation handles payment matching and routine follow-up, the AR function changes in practical, immediate ways. The most direct shift is capacity: the same analyst covers more accounts, handles fewer inbound exceptions, and spends less time on tasks that don't require judgment.
Stuut reduces manual tasks by 70%, shifting existing staff from volume work to complex exception work. For teams already stretched thin, this matters more than hiring. You can't outrun transaction growth by adding headcount, but you can cover it with an AI that scales automatically with transaction volume.
Stuut helps AR teams scale from managing 500 accounts to 5,000 without adding headcount. Bishop Lifting, an industrial equipment company, processes around 1,000 invoices per day across 45 branches and 5,000 active accounts, and achieved 50% more accounts managed per employee after deploying Stuut, with 91% of outbound communications automated.
PerkinElmer reduced overdue invoices from 50% to 15% in one year using Stuut's autonomous collections, with 80% of tail customers managed through automation and $300M collected during that period. The AR team's capacity redirected to the 20% of accounts that needed active management, including complex disputes and strategic relationships that required human judgment.
The first week after go-live produces immediate relief on the most hated tasks: payment matching, invoice resends, and routine dunning emails. Bishop Lifting's 6-week go-live across 45 branches resulted in a 2-minute average response time to customer inquiries and an immediate reduction in outbound manual effort, and PerkinElmer's multi-region rollout showed measurable DSO improvement within the first few months.
Track these four metrics in the first 90 days to demonstrate team impact:
Book a demo with the Stuut team to see the exception dashboard in action and walk through how exception handling works on your specific ERP environment.
EZG Manufacturing saves approximately 20 hours per week by automating payment matching, invoice resends, and routine exception research. Results vary by transaction volume, current match rates, and ERP complexity.
No, it removes the transactional tasks (payment matching, invoice resends, routine follow-up) so analysts can focus on complex disputes, payment plan negotiations, and high-value account relationships that require human judgment.
Stuut's average onboarding completes in 3 to 4 days for standard SAP, Oracle, NetSuite, or Dynamics environments, with full go-live including configuration and first autonomous outreach in 6 to 10 days.
Stuut targets a 95%+ automated match rate by learning remittance patterns, handling partial payments and short-pays, and flagging exceptions for review when confidence drops. This compares to industry averages where significant manual matching is still required.
Stuut is SOC 2 certified and GDPR compliant, with ISO 27001 and HIPAA compliance in progress. Stuut double-encrypts customer PII through its partnership with Skyflow and documents data retention policies across all model providers.
Stuut connects to SAP, Oracle, NetSuite, and Microsoft Dynamics via API without modifying your existing ERP configuration or chart of accounts.
Stuut's exception dashboard shows flagged items in a single view, including short-pays, unmatched payments, and accounts requiring human escalation. Analysts approve correct matches, adjust incorrect ones, and handle only the cases Stuut couldn't resolve with high confidence.
Cash application: The process of matching incoming customer payments to open invoices in the AR subledger and posting the entries to the general ledger.
DSO (Days Sales Outstanding): The average number of days it takes to collect payment after a sale. Lower DSO frees cash from receivables faster, improving working capital.
Automated match rate: The percentage of incoming payments matched to invoices without manual intervention. We target 95%+, meaning fewer than 5 in 100 payments require analyst review.
Short-pay: A customer payment that is less than the full invoice amount, typically requiring research to determine whether the difference is a deduction, a dispute, or a data error.
Deduction: An amount withheld by a customer from a payment, often related to early-pay discounts, damaged goods, trade promotions, or pricing disputes. Unprocessed deductions represent revenue leakage.
Exception handling: The review and resolution of payments or invoices that the automated system flags as too complex or ambiguous to process without human input.
Remittance: Details provided by a customer alongside a payment that identify which invoices the payment covers. Missing or incomplete remittance is the primary cause of unapplied cash.
Aging buckets: Categories that group open invoices by how long they've been outstanding: 0 to 30 days, 31 to 60 days, 61 to 90 days, and 90-plus days. Teams use aging buckets to prioritize collections outreach.
AR subledger: The detailed record of all customer transactions and open balances within the ERP system, reconciled to the general ledger.
