For B2B credit and accounts receivable leaders, deductions represent a persistent challenge that extends far beyond administrative inefficiency. Unresolved short payments, disputed invoices, and inconsistent customer claims can distort AR balances, delay collections, and increase operational costs. Yet many organizations still rely on spreadsheets, emails, and manual research to investigate and resolve these issues.
At the recent NACM Connect Fall Credit Conference in Chicago – one of the largest NACM affiliate conferences in 2026 – Emagia joined credit professionals and industry leaders to explore the evolving role of artificial intelligence in credit and Order-to-Cash (O2C) operations – And deductions was one of the most popular topics these finance professionals kept bringing up during exhibit hall hours and networking session.
Emagia was on hand not only as an exhibitor, but Dexter Peavy, Vice President of Order-to-Cash Transformation at Emagia, presented the educational session “From Backlog to Resolution: Gaining Control on Deductions Using AI.”
Drawing on more than 25 years of O2C transformation experience, Peavy demonstrated how AI can accelerate deductions investigations, improve decision-making, and help organizations prevent recurring disputes without removing human judgment from critical financial decisions.
Why Traditional Deductions Management Falls Short
Deductions rarely involve a single transaction or department. A short payment may require AR teams to retrieve remittance details, review contracts, locate proof-of-delivery documents, and coordinate with sales, logistics, and customer service.
In many organizations, the analyst effectively becomes the search engine, manually gathering information from disconnected systems.
The consequences extend beyond processing costs. Delayed investigations can increase AR aging, obscure receivables visibility, and divert experienced professionals from higher-value activities.
Peavy emphasized that the opportunity for AI is not simply faster processing. It is transforming how deductions are investigated, understood, and ultimately prevented.
A $10,000 Deduction That Wasn’t What It Seemed
One of the presentation’s most compelling examples involved a $100,000 invoice against which a customer remitted $90,000, leaving a $10,000 outstanding balance.
The customer’s stated reason was a short shipment involving 1,000 missing units.
Initially, the supplier’s records appeared consistent: the purchase order, invoice, and advance shipping notice all reflected 10,000 units. Resolving the discrepancy would traditionally require extensive manual investigation.
However, reviewing the supporting documentation revealed something different.
The proof of delivery contained a damage notation, and carrier records identified 800 units damaged during transit. That evidence accounted for $8,000 of the deduction.
The remaining $2,000 was explained by the customer’s payment terms. The customer had taken a 2% early-payment discount despite paying on day 18, when the contract required payment within 10 days to qualify.
What appeared to be one $10,000 shortage was actually two separate issues:
- $8,000 in transit damage: Supported by shipping documentation but requiring a determination of responsibility and appropriate financial treatment.
- $2,000 in unauthorized discount: An unearned early-payment discount that the supplier could challenge.
The example illustrated an important lesson: a customer’s initial deduction reason code does not necessarily reflect the underlying financial reality.
Without a complete investigation, organizations risk accepting invalid claims, misclassifying deductions, or making unnecessary adjustments.
How AI Changes the Investigation Process
Peavy demonstrated how AI-assisted deductions management can assemble evidence, identify inconsistencies, retrieve relevant commercial terms, and recommend next steps.
Instead of manually searching across ERP systems, customer portals, emails, and document repositories, AI agents can help connect the information required to evaluate a claim.
Key applications include:
- Automated data capture: Retrieving remittances and deduction information from customer portals and documents.
- Intelligent classification: Mapping customer deduction codes to internal categories and routing cases appropriately.
- Evidence retrieval: Locating contracts, shipping records, and proof-of-delivery documentation.
- Validity prediction: Analyzing historical information to identify claims requiring investigation.
- Workflow automation: Prioritizing cases, recommending actions, and coordinating follow-up activities.
However, Peavy emphasized that assembling evidence and exercising financial authority are different responsibilities.
AI can identify discrepancies and recommend actions, while humans retain control over material credits, write-offs, ambiguous contractual terms, and unusual settlements.
Organizations may automate certain decisions within established policies and approval thresholds, but appropriate oversight remains essential.
The objective is to make human judgment faster, better informed, and easier to defend.
Moving Beyond Resolution to Root-Cause Prevention
Perhaps the most important insight from the session was that closing a deduction does not necessarily solve the underlying business problem.
Peavy illustrated this with three monthly deductions of $8,000, $7,600, and $8,400, each initially classified as a shipment shortage.
Individually, these claims might be investigated and closed. Collectively, they represent $24,000 in recurring deductions that could indicate a systemic problem.
By connecting information across transactions, AI can help identify patterns that traditional case-by-case processing may overlook.
In this example, the recurring deductions involved the same carrier, shipping lane, and packaging conditions. Rather than three unrelated customer claims, the evidence suggested an underlying transit-damage problem requiring attention from logistics or operations.
This changes the role of deductions management.
Instead of repeatedly processing the financial consequences of an unresolved issue, finance teams can use data to engage upstream departments in corrective action.
The potential benefits include fewer repeat deductions, reduced administrative effort, improved customer relationships, and more accurate receivables reporting.
Final Thoughts
Technology alone cannot eliminate deductions backlogs without clear processes and ownership.
Peavy introduced a practical framework in which every open deduction should include six essential elements:
- Reason: What happened?
- Owner: Who is responsible?
- Evidence: What information supports the claim?
- Next action: What happens next?
- Due date: When must action occur?
- Root cause: What underlying issue created the deduction?
This structure supports consistent investigation, automated tracking, and greater accountability.
It also enables credit leaders to move beyond measuring deduction volumes and closure rates toward analyzing recurring customer behavior, operational failures, and supplier-related issues.
Those insights can support customer reviews, process improvement initiatives, and more strategic working capital decisions.
Frequently Asked Questions (FAQ)
How can AI accelerate deductions resolution?
AI can automate repetitive tasks such as retrieving remittances, mapping deduction codes, locating documentation, identifying discrepancies, and routing cases for review, allowing professionals to concentrate on complex decisions.
Can AI determine whether a customer deduction is valid?
AI can analyze historical data, commercial terms, and supporting documentation to assess validity and recommend next steps. However, complex settlements, contractual interpretations, and material financial adjustments may still require human approval.
Why is root-cause analysis important in deductions management?
Closing an individual deduction addresses the immediate financial discrepancy. Root-cause analysis identifies recurring problems—such as shipping damage, pricing errors, or billing inaccuracies—so organizations can take corrective action and reduce future deductions.
How should organizations begin implementing AI for deductions?
Organizations should start with high-volume, low-complexity workflows, establish reliable data and standardized processes, define approval controls, and expand automation gradually as measurable results are demonstrated.
