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BMW
Automotive · Germany / International
2.5M vehicles touched by AI
BMW's Proactive Care and Celonis Process Intelligence together reach every vehicle sold and every dealer interaction
Two complementary AI tracks: Proactive Care uses vehicle sensor data to predict service needs and contact customers before they experience problems. Celonis Process Intelligence maps real-time bottlenecks in warranty, after-sales, and dealer interactions.
Friction
After-sales and customer service were fragmented across 6,000+ dealers with inconsistent processes for warranty claims, service scheduling, and customer communication, leading to slow response, dealer and customer frustration, and process delays in warranty handling.
Breakthrough
Proactive Care: ML models on vehicle sensor data predict service needs and trigger proactive communications via the My BMW app. Dealers receive automatic tickets when sub-standard interactions are detected. Celonis: real-time process mining across warranty claims, service, and after-sales data detects deviations from the optimal process path instantly.
Impact
Every one of 2.5 million BMWs sold annually touched by at least one AI-optimised process. Customer service response times significantly faster. In Northern Europe: customer feedback to response within 24 hours to resolution within 5 days. BMW Genius customers report 50% higher satisfaction scores. BMW scores above industry average in digital customer satisfaction across multiple JD Power rankings.
Unlock the full analysis with breakthrough, impact, what made it smart and its technical approach below!
Problem
BMW's scale creates a complex service challenge: 2.5 million vehicles per year, 6,000+ dealers across dozens of markets, each with their own processes and quality levels. Inconsistency at scale is invisible until it shows up in NPS, by which point individual customers have already had bad experiences.
What made it smart
Process intelligence and predictive maintenance solve the same underlying problem from different angles. Celonis shows where the process went wrong after the fact; Proactive Care prevents the problem from happening in the first place. Together they create a closed loop.
Technical approach
Celonis Process Intelligence mines event data from BMW's warranty, service, and after-sales systems in real time, detecting deviations from the optimal process path and flagging bottlenecks. Proactive Care ML models run on vehicle telemetry to generate service need predictions, triggering outreach via the My BMW app. Automatic tickets generated for sub-standard dealer interactions.
Strategic lesson
Process intelligence shows you where your operations are breaking. Predictive maintenance prevents them from breaking. The organisations that deploy both simultaneously are compressing the gap between reactive and proactive.
Reflection question
In your organisation, how long does it take to detect a systematic process problem, and when you detect it, is that because the system flagged it or because a customer complained?