{"id":6239,"date":"2026-11-13T17:50:00","date_gmt":"2026-11-13T17:50:00","guid":{"rendered":"https:\/\/www.peeriosity.com\/shared-services\/articles\/?p=6239"},"modified":"2026-11-13T17:50:00","modified_gmt":"2026-11-13T17:50:00","slug":"leveraging-genai-and-agentic-ai-for-auto-posting-in-accounts-payable","status":"publish","type":"post","link":"https:\/\/www.peeriosity.com\/shared-services\/articles\/2026\/11\/leveraging-genai-and-agentic-ai-for-auto-posting-in-accounts-payable\/","title":{"rendered":"Leveraging GenAI and Agentic AI for Auto-Posting in Accounts Payable"},"content":{"rendered":"

Beyond Automation: One Company’s Journey to Autonomous Accounts Payable with Generative AI<\/em><\/p>\n

Peeriosity Peercast | August 2026<\/p>\n\n\n\n\n\n\n\n\n
Host Company<\/strong><\/th>\nA global consumer products company (the presenting company)<\/th>\n<\/tr>\n<\/thead>\n
Presenters<\/strong><\/td>\nDirector, Global Service Management, Source-Plan-Pay Operations; Source-Plan-Pay Operations Service Manager<\/td>\n<\/tr>\n
Facilitator<\/strong><\/td>\nMichael Hostetler (Peeriosity)<\/td>\n<\/tr>\n
Format<\/strong><\/td>\nPeeriosity Peercast webinar (audio conference + shared slides), recorded for on-demand replay<\/td>\n<\/tr>\n
Audience<\/strong><\/td>\nAccounts Payable and Shared Services leaders from Peeriosity member companies<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Overview<\/strong><\/p>\n

The presenting company’s Global Service Management team presented its journey from traditional accounts-payable (AP) automation toward an autonomous, Generative-AI-powered invoice processing model. The session walked through the business case, the agentic AI architecture built on the company’s internal agentic AI platform, pilot results from a live market rollout in Thailand, and lessons learned, closing with an open Q&A on invoice-number derivation, paper-invoice intake, and how AI compares to traditional RPA.<\/p>\n

Note that this document is a summary. Peeriosity-100 members have full visibility to company names, presenters, Peercast attendees, and a recording of the Peercast discussion.<\/p>\n

The Business Problem<\/strong><\/p>\n

The company processes approximately 4.4 million invoices annually across more than 70 markets, with roughly 2.4 million of those invoices still requiring significant manual effort. Traditional rules-based automation (RPA and legacy OCR\/tax engines) was falling short of the company’s efficiency targets because of:<\/p>\n

    \n
  • High volumes of unstructured data: 30% of invoices still arrive as paper or PDFs sent via email<\/li>\n
  • Operations spanning 70+ diverse markets with country-specific regulations and evolving compliance mandates<\/li>\n
  • Elevated manual intervention and troubleshooting costs, concentrated in the “posting” and “troubleshooting” stages of the process (30% and 35% of total effort, respectively)<\/li>\n
  • A need to make processes “AI-ready,” integrate with existing and upcoming systems, and sustain accuracy against KPIs<\/li>\n<\/ul>\n

    Strategic Approach<\/strong><\/p>\n

    The company scoped the initiative deliberately rather than tackling the hardest markets first, following an 80\/20 approach:<\/p>\n

      \n
    • Defining the strategic scope:<\/strong> targeted manually posted invoices in high-volume markets with the largest automation gaps.<\/li>\n
    • Why this scope:<\/strong> high manual effort in key markets offered a clear opportunity for cost reduction and efficiency gains.<\/li>\n
    • Initial developments:<\/strong> proved out GenAI capabilities and built foundational models through data collection and model training.<\/li>\n
    • From pilot to product:<\/strong> the GenAI solution is now live and being prepared for broader scale.<\/li>\n<\/ul>\n

      Pilot markets were chosen for being low-complexity (high-impact suppliers under the 80\/20 principle, single-PO-line invoices, simple tax coding), for high transaction volume to prove scalability, and to validate multi-language GenAI capabilities against local-language complexity.<\/p>\n

      Solution Architecture<\/strong><\/p>\n

      End-to-End GenAI Workflow<\/strong><\/p>\n

      The production workflow blends human review, SAP, an AI agentic layer, and RPA (BluePrism):<\/p>\n

        \n
      • Scanned invoices enter the SAP system as images<\/li>\n
      • An AI agentic workflow extracts invoice data and performs tool calls, checks assignment\/payment controls (PO line, receipt, vendor, quantity, bank details, price), runs a compliance check, and validates tax coding<\/li>\n
      • Invoices that meet all criteria are posted automatically via RPA; exceptions route back to manual posting and human quality control<\/li>\n<\/ul>\n

        Agentic AI Design<\/strong><\/p>\n

        The agentic layer is a multi-step workflow (built with LangGraph) using multiple right-sized Gemini models, optimized for cost and performance while meeting accuracy thresholds:<\/p>\n

          \n
        • P0: PDF checks (size, validity, password protection, encoding, rotation, name)<\/li>\n
        • S1 Supervisor triggers a validate-PO-line tool; T1 performs bounding-box detection for the invoice number<\/li>\n
        • T2: if OCR does not match the invoice number, an evaluator reviews a magnified image, OCR output, and the supplier’s last invoice numbers<\/li>\n
        • T3 retrieves the SAP purchase order, GR\/IR history, and supplier details; T4 validates invoice compliance<\/li>\n
        • T5 validates tax codes; S2 Supervisor combines all details and generates the final response, which is parsed and verified before posting<\/li>\n<\/ul>\n

          An initial “toe in the water” pilot applied this agentic flow to PDF invoice pre-coding in Europe and North America, achieving:<\/p>\n\n\n\n\n\n
          99.7%<\/strong><\/th>\n50%+<\/strong><\/th>\n40,000+<\/strong><\/th>\n30x<\/strong><\/th>\n<\/tr>\n<\/thead>\n
          AI accuracy<\/strong><\/td>\nHuman effort eliminated<\/td>\nInvoices processed by AI<\/td>\nCheaper vs. traditional OCR<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          Pilot Results: Thailand<\/strong><\/p>\n

          Thailand was selected as the first full end-to-end pilot market and went live in July 2026:<\/p>\n\n\n\n\n\n
          90.3%<\/strong><\/th>\n2.4K<\/strong><\/th>\n99.9%<\/strong><\/th>\n<\/tr>\n<\/thead>\n
          Auto-post rate<\/strong><\/td>\nInvoices processed<\/td>\nAutomation accuracy<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          2,200 successful automations were recorded during the pilot window.<\/p>\n

          Roadmap<\/strong><\/p>\n

          The company’s stated next step is global expansion of the GenAI-powered end-to-end invoice process beyond the Thailand pilot, with candidate markets and hubs including North America, Latin America (Mexico City, S\u00e3o Paulo), Europe (Paris, Berlin, London), the Middle East\/Africa (Cairo, Dubai, Lagos), and Asia-Pacific (Tokyo, Singapore, Sydney).<\/p>\n

          Lessons Learned<\/strong><\/p>\n

            \n
          • Knowledge source gaps:<\/strong> existing documentation was often outdated, incomplete, or not practically actionable.<\/li>\n
          • Inconsistent process execution:<\/strong> discrepancies existed between documented process and actual practice.<\/li>\n
          • Current vs. future state mapping:<\/strong> required careful identification of gaps, challenges, and next steps.<\/li>\n
          • 80\/20 approach:<\/strong> don’t attempt to solve the most complex problem first.<\/li>\n
          • Data integration:<\/strong> connecting to enterprise data sources takes real time.<\/li>\n
          • Governance, support, and business continuity (BCP):<\/strong> the solution can break and needs a support model.<\/li>\n
          • Operationalization:<\/strong> requires designing the future support organization.<\/li>\n<\/ul>\n

            The team framed this as an ongoing experiment, learn, adapt cycle rather than a one-time deployment.<\/p>\n

            Audience Q&A Highlights<\/strong><\/p>\n

              \n
            • Paper invoice intake:<\/strong> A member asked whether shared services still receives paper invoices, or whether local teams scan mail to electronic format first, reflecting that paper\/PDF intake remains a meaningful share of volume (about 30% per the process breakdown shown).<\/li>\n
            • Missing invoice numbers:<\/strong> Asked whether the solution can derive an invoice number when one is missing (e.g., monthly utility or leasing statements with only a statement date and a non-unique account number), the presenters said such documents are identified as “not an invoice” and are routed out for manual review rather than being forced through automation.<\/li>\n
            • AI vs. traditional RPA:<\/strong> A participant observed that AI appears more nimble and adaptable than structured RPA scripts, which can break easily (particularly when accessed URLs or GUIs change), underscoring a key advantage of the agentic AI approach over legacy rules-based bots.<\/li>\n<\/ul>\n

              Attendees responded positively overall, with multiple participants thanking the presenters for a strong presentation.<\/p>\n

              Key Takeaways<\/strong><\/p>\n

                \n
              • The company is moving beyond rules-based RPA\/OCR toward an agentic, GenAI-driven AP model built on a proprietary multi-model platform, combining Gemini models, LangGraph orchestration, and human-in-the-loop exception handling.<\/li>\n
              • A disciplined, staged rollout (pilot on low-complexity, high-volume, high-impact markets before scaling) produced strong early results: 99.7% AI accuracy and 30x cost efficiency in the initial PDF pre-coding pilot, and a 90.3% auto-post rate with 99.9% accuracy in the Thailand production pilot.<\/li>\n
              • Non-invoice documents (e.g., statements without unique invoice numbers) are deliberately excluded from automation and routed to manual review, preserving control and compliance.<\/li>\n
              • Success depended as much on process discipline (scope definition, data integration, governance\/BCP, and future-state operating model design) as on the underlying AI technology.<\/li>\n
              • Global expansion beyond Thailand is the next phase, extending the model across the company’s broader market footprint.<\/li>\n<\/ul>\n

                Source: Peeriosity Peercast recording and chat transcript, “Leveraging GenAI and Agentic AI for Auto-Posting in Accounts Payable,” August 2026.<\/em><\/p>\n

                <\/span><\/span><\/span><\/p>","protected":false},"excerpt":{"rendered":"

                Beyond Automation: One Company’s Journey to Autonomous Accounts Payable with Generative AI Peeriosity Peercast | August 2026 Host Company A global consumer products company (the presenting company) Presenters Director, Global Service Management, Source-Plan-Pay Operations; Source-Plan-Pay Operations Service Manager Facilitator Michael Hostetler (Peeriosity) Format Peeriosity Peercast webinar (audio conference + shared slides), recorded for on-demand replay […]<\/p>\n

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