Vehicle electrification, autonomous driving, smart factories — none of this is science fiction from Hollywood blockbusters anymore, it’s today’s reality. Behind all these flashy innovations, though, lurks a less glamorous but equally critical problem: mountains of paperwork, routine operations, and outdated business processes that hold the industry back. McKinsey data shows that a typical automaker spends up to 30% of employee working time on repetitive administrative tasks.
RPA (Robotic Process Automation) — technology that automates processes using software robots — has become that secret ingredient allowing auto giants to finally shed this burden. This article examines how exactly RPA is transforming the automotive industry, which specific challenges are already being solved successfully, and what pitfalls await companies during implementation.
Current market situation: what’s happening with RPA automotive right now
Automakers aren’t sitting on the sidelines of digital transformation. Volkswagen Group launched its “Mission: Robotic Process Automation” program back in 2019, and as of 2024, the company runs over 700 active RPA bots processing millions of transactions monthly. BMW implemented RPA in its logistics centers, cutting order processing time by 40%. Toyota uses software robots for supply chain management — a critically important solution after the pandemic semiconductor crises.
The RPA in automotive industry market shows impressive growth rates. Gartner forecasts that global automaker investments in RPA solutions will reach $2.4 billion by 2026. Leading market players — UiPath, Blue Prism, Automation Anywhere — are actively developing specialized solutions specifically for the automotive sector. For instance, UiPath presented an automotive package in 2023 that includes templates for the 15 most common business processes in the industry. Meanwhile, major consulting firms, including https://dxc.com/industries/automotive, are developing comprehensive digital transformation strategies for automakers where RPA serves as one of the key operational optimization tools.
Interestingly, even startups from the electric vehicle sector — Rivian, Lucid Motors — are building RPA into their operational model from day one. They don’t carry legacy system baggage, so they can build processes from scratch, integrating automation from the start.
Top 8 use cases: where RPA in automotive shows maximum effectiveness
1. Automating warranty claim processing
Warranty cases are a real headache for automakers. The average dealer processes 200-300 warranty claims monthly, each requiring documentation verification, damage photos, manufacturer coordination, and compensation calculation.
How RPA works:
- Software robots automatically extract data from email inquiries and CRM systems
- Verify warranty validity periods in databases
- Match VIN numbers with service history records
- Classify malfunction types based on descriptions and photos
- Calculate reimbursement amounts according to tariffs
- Generate reports for the finance department
Ford implemented such a system in 2022, reducing processing time for one claim from 45 minutes to 8 minutes. Processing accuracy increased to 98% by eliminating human error in data transfer between systems.
2. Managing spare parts supply chains
Modern automobiles consist of 30,000+ components from hundreds of suppliers. Coordinating this process is a logistical nightmare. RPA automotive allows synchronizing data between the manufacturer’s ERP system, supplier platforms, and transportation companies.
Specific tasks for robots:
- Real-time monitoring of inventory levels
- Automatic order creation when reaching minimum thresholds
- Tracking delivery status through transportation company APIs
- Updating delivery forecasts accounting for delays
- Alerts about critical situations (key component delays)
Nissan uses RPA to manage 400+ suppliers worldwide. The system automatically processes 15,000+ transactions daily, ensuring uninterrupted production at plants across Europe and Asia.
3. Automating credit checks at dealerships
Buying a car on credit is standard practice. Up to 70% of buyers in the US arrange financing. The creditworthiness verification process traditionally takes 1-2 hours, which negatively impacts customer experience.
RPA solves the problem this way:
- Robot retrieves customer data from dealer CRM
- Automatically requests credit history from bureaus (Experian, TransUnion)
- Verifies documents through OCR (optical character recognition)
- Calculates credit scoring using partner bank algorithms
- Prepares preliminary decision in 5-7 minutes
General Motors Credit introduced such a system in its dealer network, boosting sales conversion by 12% thanks to service speed.
4. Processing invoices and payments
A typical automaker processes tens of thousands of invoices monthly. Hyundai, for example, works with 2,500+ suppliers just for its Czech factory. Manual processing of each invoice means checking nomenclature, comparing with contracts, entering into accounting systems, and initiating payments.
RPA use cases in automotive industry for financial operations:
- Automatic data recognition from PDF/paper invoices
- Matching invoice items with contracts and orders (three-way matching)
- Detecting duplicates and errors
- Routing for approval according to set rules
- Preparing payment orders
- Updating status in all related systems
Mercedes-Benz cut the invoice processing cycle from 7 days to 1.5 days after implementing RPA, saving €3 million annually at just one manufacturing complex.
5. Automating IT support and incident management
Modern automotive plants are high-tech facilities with thousands of computers, tablets, and industrial terminals. IT departments receive hundreds of requests daily: reset password, grant access, restart system.
RPA bots serve as first-line support:
- Automatic password reset through Active Directory
- Account unlocking
- Granting standard system access (SAP, PLM, MES)
- Service and server restarts on request
- Creating tickets in ServiceNow with automatic classification
Audi implemented RPA bots in its IT infrastructure, automating 60% of routine requests. Response time for standard inquiries dropped from 2-4 hours to 5 minutes.
6. Managing product quality data
Quality control in automotive RPA is a critical function. Each defect can lead to massive recalls. Software robots collect quality data from various sources — production lines, testing laboratories, service centers — and consolidate them in a unified system.
The system works like this:
- Collecting data from measuring instruments on the assembly line
- Integrating crash test and material testing results
- Analyzing customer feedback and service requests
- Identifying quality patterns and anomalies
- Automatically generating reports for regulators (NHTSA, IIHS)
Tesla actively uses RPA for analyzing telemetry from millions of cars on roads, identifying potential problems before they become widespread.
7. Automating regulatory reporting
The automotive industry is one of the most regulated sectors. Manufacturers must submit hundreds of reports to various government agencies: CO₂ emissions, safety, quality, fuel consumption, recycling.
Example of RPA operation:
- Collecting data from engineering, manufacturing, and testing systems
- Converting data into formats required by regulators
- Automatic report generation using EPA, CARB, Euro NCAP templates
- Submission through appropriate portals and systems
- Archiving in compliance with requirements
Peugeot automated 85% of regulatory reporting in Europe, freeing up 40 FTE (full-time equivalent) for strategic projects.
8. Managing dealer networks and incentive programs
Automobile manufacturers work with thousands of dealers worldwide. Managing incentive programs, bonuses, and discounts is a complex process requiring massive sales data processing.
RPA in automotive industry automates:
- Collecting sales data from dealer CRM systems
- Calculating bonuses and commissions using complex schemes
- Verifying KPI (target indicator) fulfillment
- Generating individual reports for each dealer
- Preparing payments and issuing invoices
Honda uses RPA to manage its network of 12,000+ dealers, processing monthly calculations in 2 days instead of the previous 2 weeks.
Technology stack: what comprises a modern RPA automotive platform
Implementing RPA isn’t just buying a UiPath or Blue Prism license. Successful automation in the automotive industry requires integrating an entire set of technologies.
Core components:
- RPA platform — the actual software robots (UiPath, Automation Anywhere, Blue Prism)
- OCR/IDP — text recognition from documents (ABBYY FlexiCapture, Kofax)
- Process Mining — process analysis to find automation opportunities (Celonis, UiPath Process Mining)
- AI/ML — intelligent processing of unstructured data (Google Cloud AI, Azure Cognitive Services)
- API Management — integration with corporate systems (MuleSoft, Apigee)
- Cloud Infrastructure — cloud infrastructure for scaling (AWS, Azure, Google Cloud)
Interesting trend: many automakers are moving toward hybrid models where some robots work on-premise (for critical production systems) and others in the cloud (for administrative processes).
Best practices: how to implement RPA without breaking everything
Start with Process Mining, not automation
The biggest mistake is immediately jumping into robot development. Before automating a process, you need to understand it. Process mining allows “seeing” the actual process based on system logs, not how it’s described in documentation.
Volkswagen began its RPA journey with Process Mining, discovering that employees spent 40% of their time copying data between SAP and a legacy MES system. This became the first automation candidate.
Create a Center of Excellence (CoE)
RPA use cases in automotive industry are too diverse to let every department develop their own robots without coordination. A central team is needed that:
- Establishes development and governance standards
- Manages shared infrastructure
- Conducts training for business units
- Controls solution quality and security
- Ensures component reusability
BMW created its RPA CoE in 2020, which avoided the “hundreds of zombie robots” situation where nobody knows which bots are running and what they’re doing.
Don’t automate a bad process
Classic trap: take an inefficient manual process and simply replace the person with a robot. Doing bad work faster is still bad. Before automation, the process needs optimization.
Example from Toyota: the company wanted to automate the design documentation change approval process, which took 3 weeks and went through 15 approvals. Instead of automating, Toyota first redesigned the process, reducing it to 5 key approvals. Only then did they implement RPA, cutting the cycle to 2 days.
Think about scalability from day one
A pilot project with 3-5 robots is fine for proof-of-concept. But if you’re planning hundreds of robots (and successful programs reach exactly that), infrastructure must be ready.
What needs consideration:
- Centralized logging and monitoring (can’t check each robot manually)
- Automated change testing (regression tests for robots)
- Version control and CI/CD for RPA artifacts
- Disaster recovery and backup
- License monitoring and usage optimization
Ford spent an additional $2 million building a proper RPA platform at program start, which saved $15 million over the next two years through more efficient scaling.
Invest in training and change management
The technical part is only 30% of success. The rest is people. Employees often fear robots will take their jobs. Clear communication is essential:
- RPA frees time for more interesting work, doesn’t destroy positions
- Employees can become citizen developers
- Automation makes the company more competitive, meaning more opportunities
Mercedes-Benz launched the “Everyone can code robots” program, training 500+ employees without IT backgrounds to create simple RPA bots for their needs. This not only accelerated implementation but also increased staff engagement.
Challenges and pitfalls: what RPA veterans warn about
Integration with legacy systems
Automotive companies are often century-old corporations with massive technical debt. AS/400, mainframe, custom-built 1990s systems — all still running in production.
RPA can theoretically automate interaction with any interface, including green terminal screens. Practically though — it’s fragile. Changing one button on a screen can break the robot. Solutions:
- Use APIs where possible
- Develop intermediate adapters for legacy systems
- Implement UI change monitoring and automated testing
Underestimating operational support
Developing a robot is half the job. It needs maintenance: updates when systems change, bug fixes, scaling for growing loads. Many companies focus on development, forgetting about the operational model.
Rule of thumb: for every 10 RPA developers, you need a team of 3-5 people for support and monitoring. Otherwise, in a year you’ll have hundreds of robots, half not working, and nobody knows why.
Wrapping up
RPA is transforming the automotive industry less visibly than electric vehicles or autopilots, but no less fundamentally. Software robots already process millions of transactions daily at Ford, BMW, and Toyota, freeing people from routine and allowing focus on innovation.
The key to success isn’t just implementing technology but thinking through strategy: start with process analysis, create the right governance model, invest in people training and operational support. Companies that got this right see 200-300% ROI within the first year.
The automotive industry stands on the threshold of a new era where physical robots on assembly lines are complemented by software robots in offices. If you haven’t started your RPA journey yet — it’s high time to think about it seriously. Competitors are already doing it.
