The potential benefits, leading use cases and how to successfully implement AI solutions.
Boosting efficiency in accounts payable
Reducing manual invoice processing effort by 24% with AI-enabled transformation of the finance function
Related Expertise
Finance functions in large industrial companies are under constant pressure to improve efficiency while maintaining operational excellence. Yet without objective benchmarking and end-to-end transparency, many improvement initiatives remain fragmented and difficult to scale. This case study illustrates how a data-driven accounts payable (AP) benchmarking approach, combined with automation levers and AI-enabled transformation design, helped a leading global automotive manufacturer identify potential time savings of 24% and define a path toward digital world-class performance by 2027.
Finance leaders in complex, multi-entity organizations often lack the objective data foundation needed to assess whether their AP operations are truly competitive. Without rigorous external comparison and a structured view of automation maturity, efficiency initiatives risk becoming isolated efforts rather than stepping stones toward best-in-class performance.
Roland Berger project:
Challenge: A leading global automotive manufacturer needed end-to-end transparency on its AP performance across approximately 50 European entities and a clear path to greater efficiency amid growing cost pressure. At the same time, the client wanted to understand how automation, AI use cases and stronger governance could future-proof AP and broader purchase-to-pay (P2P) processes.
Solution: We conducted comprehensive, data-driven benchmarking across more than 15 operational AP KPIs, comparing AP performance against OEM and digital world-class peer groups. Based on the identified performance gaps, we developed a quantified portfolio of improvement levers, combining operational process optimization with automation and AI-enabled transformation opportunities.
Result: We identified a 24% potential time saving in manual invoice processing within the current scope, with additional value potential beyond the immediate organization through cross-functional spillover effects. We also delivered a clear roadmap toward the digital world-class benchmark by 2027 and defined the structural and AI-related enablers needed to shape a future-ready 2030+ target picture.
Actions: The actions we delivered included benchmarking AP performance across approximately 50 entities, identifying AI use cases, expanding self-billing and e-invoicing, simplifying approval and exception handling, and strengthening intercompany governance.
The starting point
The client faced increasing cost pressure across the organization, with efficiency targets cascading into the finance function. However, several factors made it difficult to objectively assess and improve AP performance:
- Lack of transparency across entities: With around 50 European legal entities operating across different enterprise resource planning (ERP) systems and process setups, there was no consolidated view of AP costs, full-time equivalents (FTEs) or process efficiency. Decision makers lacked the data foundation needed to identify where performance gaps existed and how large they were.
- No external benchmark reference: Without comparison to relevant peer groups – both within the automotive industry and against digital world-class standards – it was unclear whether current performance levels were acceptable or significantly below potential.
- Fragmented improvement initiatives: Process optimizations were pursued locally without a scalable, group-wide operating model. This led to inconsistent performance levels, with some entities operating at first-quartile efficiency while others lagged significantly behind.
- Untapped AI potential: While the organization had made progress in automation, for example with self-billing invoices, there was no systematic evaluation of how AI could further enhance AP processes – from intelligent document processing to agentic exception resolution.
- Limited end-to-end process ownership: Cross-functional thinking and KPI tracking across the purchase-to-pay cycle were lacking. Without clear end-to-end ownership, optimizations remained siloed and suboptimal from a global perspective.
Our approach
Our expert teams combined rigorous benchmarking with forward-looking transformation design to create both immediate transparency and a future-ready improvement path. Specifically, we:
Built a robust fact base
We established a data-driven performance baseline across a broad European entity scope, assessing cost, staffing, process and automation performance across multiple KPI dimensions and peer panels.
Translated insights into measurable value levers
Based on the performance gaps identified, we developed a prioritized portfolio of operational and structural improvement levers, including AI-enabled opportunities, with quantified impact and implementation logic.
Connected today's efficiency agenda with tomorrow's target picture
Beyond immediate AP measures, we defined the governance, data, intercompany and operating model enablers needed to move toward a more AI-enabled finance function .
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"AI delivers impact in accounts payable only when built on a foundation of transparency, standardization and end-to-end ownership."
Key improvement themes
Rather than optimizing isolated AP activities, we structured the improvement path around four interconnected themes:
1. Scale up automation where the business case is proven
Expand self-billing and e-invoicing, supported by touchless processing, to reduce manual effort and improve process efficiency at scale.
2. Use AI where classic automation reaches its limits
Identify and prioritize AI use cases for labor-intensive and exception-heavy activities, from document processing and matching to validation and exception handling.
3. Simplify processes before digitalizing complexity
Reduce approval friction and streamline exception handling, while strengthening compliance logic to unlock faster cycle times and more scalable execution.
4. Strengthen the delivery model behind AP performance
Improve shared services center (SSC) scalability and process standardization, with clear end-to-end ownership to make gains sustainable across entities and functions.
Results:
Unlocking AP value and building the AI foundation
The benchmarking created end-to-end transparency across a broad European entity landscape. It also established a robust fact base on cost, productivity, process performance and automation maturity. This made it possible to identify significant efficiency potential – including a 24% potential time saving in manual invoice processing – and translate it into a clear, implementation-ready improvement path.
What made the difference was not only the identification of savings potential but the way this potential was made actionable: through objective benchmarking and quantified value levers, supported by a transformation logic that connected process efficiency with broader evolution of the finance function. Rather than treating AI as a standalone topic, the project positioned it as part of a wider shift toward more scalable and end-to-end managed finance processes.
Three strategic enablers for 2030 and beyond
The project showed that reaching benchmark levels is only the first step. Sustaining performance in an AI-driven future requires broader structural change across three strategic enablers:
#1 Fix the present
Strengthen governance in areas where fragmentation drives inefficiency today – not just in intercompany processes but also in accountability and cross-functional coordination.
#2 Strengthen the foundations
Build on existing progress in master data and AI governance to create the conditions for scalable AI deployment across functions.
#3 Shape the future
Define the target picture for a more AI-enabled finance function through stronger end-to-end process ownership and orchestration across systems, supported by a clearer ambition for the future operating model.
Why this case matters beyond AP
The project not only improved transparency in AP but also showed how finance functions can connect benchmarking and automation with AI-enabled governance and operating model design in one coherent transformation story. This matters because AP efficiency depends on more than AP alone – it is shaped by purchasing and approvals, as well as logistics and intercompany processes, underpinned by data quality. At the same time, AI creates new opportunities, but it also brings new requirements for governance and controls, backed by clear organizational ownership. As a result, sustainable efficiency gains require both short-term operational levers and longer-term structural changes.
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