How to use AI in Accounts Payable: extraction to approvals to ERP. Fix manual entry, delays, and matching errors with an explainable, agentic workflow.

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Executive summary

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• AP teams continue to face structural issues: 56% report excessive manual data entry, 46% experience invoice processing delays, and 50% encounter invoice matching errors (SSON’s AP Automation Virtual Summit 2024).
• A pragmatic way forward is a three‑layer architecture: structured extraction → agent‑driven decisioning → governed posting to ERP.
• This approach improves cycle time, first‑pass yield, and match rates while maintaining auditability and control.

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The problem statement, quantified

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Most organizations have partial automation across capture, matching, and approvals, but variance in formats and policies drives rework:

• Manual entry (56%) persists when templates break or fields are missing.
• Processing delays (46%) arise from exception queues and unclear routing.
• Matching errors (50%) often stem from inconsistent PO/receipt data and line‑level discrepancies.

Numbers from 2024 survey of 1533 finance professionals performed by Censuswide

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Addressing these three areas reliably delivers the majority of the value from AP automation and invoice automation initiatives.

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A reference architecture for AI in AP

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1) Structured extraction (document to data)

Business users define the target schema once, rather than building brittle templates for each layout.

• Configure columns (e.g., Vendor, Invoice #, PO #, Dates, Currency, Line Items, Tax, Net, Remit‑To).
• Provide column‑level instructions (how to interpret, normalize, or infer values).
• Run the model to populate the table, with bounding boxes that show provenance for each extracted value.
• Output can be exported to Excel for ad‑hoc work or forwarded programmatically to the next step.

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2) Agent‑driven decisioning (data to action)

AI Agents operate on the extracted fields, master data, and system integrations. Where necessary, deterministic code handles firm policy.

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Typical responsibilities:

• 3‑way match (PO, invoice, receipt) or 2‑way for services.
• GL coding and cost allocation from line descriptions and history.
• Duplicate detection and vendor risk checks.
• Approval routing according to thresholds, cost centers, and SOD rules.
• Supplier statement reconciliation (periodic): match vendor statements to open AP and surface breaks.
• Month‑end account reconciliation: align subledger to GL and propose resolutions.

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3) Governed posting (review and ERP write‑back)

• Items meeting confidence and policy thresholds can post directly to the system of record (ERP); others are routed to a review interface with full context.
• All steps preserve evidence: source files, extracted values, bounding boxes, prompts, decision logs, and data lineage.

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Where value lands first (typical wins)

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1. Touchless invoice flow for high‑volume vendors: improved first‑pass yield and shorter cycle time.
2. 3‑way match at line level: fewer exceptions, faster dispute resolution.
3. Targeted approvals: lower queue times and better compliance with approval thresholds.
4. Supplier statement reconciliation: automatic identification of missing invoices/credits and faster month‑end closure.

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Controls, risk, and audit readiness

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• Explainability: bounding boxes on fields; decision logs for each agent step.
• Governance: role‑based access, SOD, configurable confidence thresholds, dual control for bank detail changes.
• Compliance: retained artifacts for audit, exportable evidence packs, clear data retention policies.

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Implementation plan (30 days)

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Week 1— Foundation

• Select 1–2 vendors and one statement format.
• Define target schema and column‑level instructions.
• Connect read‑only to ERP for master data (vendors, POs, receipts, GL).

Weeks 2–3 — Decisioning and approvals

• Enable agents for 3‑way match, coding, and duplicate checks.
• Implement approval routing and SOD checks.
• Begin supplier statement reconciliation for pilot vendors.

Week 4— Posting and scale

• Turn on ERP write‑back for in‑policy invoices; route exceptions to review.
• Expand to additional vendors and document types (credit notes, statements).
• Publish dashboards and finalize operating procedures.

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Success metrics (define upfront)

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• Touchless rate and first‑pass yield
• Cycle time (receipt → post)
• Match rate (2‑way/3‑way) and exception rate
• Discount capture and late fees avoided
• Close speed for reconciliations and statement clearance

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How our platform applies this architecture

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• Structured extraction: Users specify the columns in a tabular interface and provide prompts per column to define extraction and normalization. The AI returns a populated table with bounding boxes on the source document for verification.
• Downstream automation: Results can be exported to Excel or forwarded to the next step.
• Agent‑driven logic: AI Agents operate on extracted data plus master data and integrations; where policy demands determinism, we use code. Agents handle 3‑way match, GL coding, duplicate detection, approvals, and supplier statement reconciliation.
• Posting and review: Outcomes are either pushed to a review interface or written back to the ERP/SOR with full evidence.
• This supports Accounts Payable Automation, Invoice automation, reconciling vendor statements, and 3‑way match scenarios without disruptive system changes.

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FAQs

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How does this differ from template‑based OCR?
Schema‑first extraction with column‑level instructions reduces brittleness and provides provenance for audit.

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Will this work with my ERP?
Yes. The pattern treats AI as an integration layer. Read master data; post approved transactions via standard ERP interfaces.

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Where should I start?
Pick a contained scope, e.g. a manual data entry task for one process, define success metrics, and scale once the metrics are met. We are happy to help you through the process.