Celonis and Valence Intelligence have jointly launched the Celonis Deductions and Disputes Manager, an AI-driven application designed to help enterprise finance teams handle the deduction management process with greater speed and less manual intervention. The product is available now through the Celonis Platform Apps Programme.
Deduction management sits at a costly intersection of trade finance and operational complexity. In the consumer packaged goods (CPG) sector, deductions claimed by retailers and logistics carriers can represent between 5% and 15% of gross sales, according to figures cited in the announcement. Reconciling those claims typically requires finance staff to manually chase documentation across retailer portals, carrier websites and internal ERP systems, with disputes frequently taking weeks or months to close.
What the product does
The application uses AI agents to automate the aggregation and interpretation of deduction data across fragmented sources, including unstructured documents such as remittance advices and proofs of delivery. It produces a unified, real-time view of a company’s deductions position and generates guided recommendations for claims processing, including one-click options for submitting disputes and communicating resolutions to counterparties.
Liam Mawe, global VP for AI and Industries at Celonis, said the tool gives AI the operational context it needs to handle workflows that have historically resisted automation. “The Deductions and Disputes Manager handles the messy operational complexity these teams face daily and gives AI the context it needs to successfully automate these workflows,” he said. Kevin Cunningham, chief executive of Valence Intelligence, described Valence’s contribution as an agentic data layer capable of turning unstructured PDF documents into clean, structured data fed into the Celonis platform.
The launch is part of the broader Celonis Platform Apps Programme, which provides pre-built, domain-specific solutions developed by certified partners on top of the Celonis process intelligence infrastructure.
Market context
Deductions and disputes automation is an under-served but commercially material niche within the broader enterprise finance operations market. Most accounts-receivable automation tools focus on invoice matching and payment reconciliation; the specific challenge of retailer deductions, with their retailer-specific coding rules and carrier-dependent documentation requirements, has remained largely manual at many large CPG manufacturers.
The competitive landscape includes specialist deductions management vendors, some of which have operated in the segment for a decade or more, as well as broader order-to-cash automation platforms expanding their feature sets. The Celonis and Valence product competes by combining Celonis’s process-mining and digital-twin infrastructure, which gives it visibility across the full enterprise process graph, with Valence’s purpose-built data ingestion layer for retailer and carrier portals.
For enterprise buyers, the relevant evaluation criteria are the breadth of retailer and carrier integrations, the accuracy of AI-driven reason coding and validation, and the degree to which the tool can be configured to retailer-specific dispute rules without custom development. The announcement does not provide benchmark data on recovery rates or accuracy metrics, which are the figures that procurement and finance transformation teams will want before committing to deployment.
The broader trend running underneath this launch is the move from robotic process automation, which scripted repetitive tasks, to agentic AI, which interprets unstructured content and takes multi-step actions. That shift is reshaping the enterprise finance software market and attracting investment from both established platform vendors and specialist startups. Celonis, which is positioned as a process intelligence layer rather than a point solution, is using the partner programme to extend into adjacent verticals without building every domain application in-house, a model that also reduces its own development risk.
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