Why AI matters at this scale

Rhombus Energy Solutions, as part of BorgWarner, designs and manufactures advanced power conversion systems and electric vehicle charging infrastructure. Operating at a large enterprise scale (10,000+ employees globally), the company is positioned at the critical intersection of automotive electrification and grid modernization. For a player of this size in a capital-intensive manufacturing sector, AI is not a speculative bet but a strategic necessity to defend market position, improve margins, and enable new smart-grid services. The scale generates vast operational data, and the competitive pressure to deliver more reliable, efficient, and intelligent hardware creates a compelling mandate for AI adoption.

Concrete AI Opportunities with ROI Framing

1. Predictive Maintenance for Charging Stations

Deploying AI models on telemetry data from field-deployed DC fast chargers can predict failures in power modules or cooling systems. For a large installed base, reducing mean-time-to-repair by even 20% directly boosts revenue-generating uptime and slashes warranty and service truck roll costs. The ROI is clear: increased asset utilization and lower operational expenditures.

2. AI-Optimized Product Design

Generative AI and simulation can accelerate the design of next-generation power electronics, exploring thousands of thermal, electrical, and mechanical configurations to optimize for efficiency, cost, and manufacturability. This reduces prototype cycles and material costs, speeding time-to-market for superior products—a critical advantage in the fast-evolving EV space.

3. Dynamic Energy Management for Fleet Depots

AI algorithms can optimize charging schedules for electric bus or truck fleets, considering real-time electricity rates, vehicle routes, and local grid constraints. This turns energy from a fixed cost into a managed variable, potentially cutting fleet energy bills by 15-30%. The ROI compounds with scale and can be offered as a value-added software service to customers.

Deployment Risks for a Large Enterprise

While Rhombus benefits from BorgWarner's resources, integration risks are significant. Legacy manufacturing execution systems (MES) and industrial IoT platforms may lack the data pipelines needed for AI. Siloed data between engineering, manufacturing, and field service hinders model training. Large organizations also face cultural inertia; proving AI's value requires cross-functional pilots with clear ownership. Cybersecurity for connected charging infrastructure is paramount—any AI system must be built on a secure foundation. Finally, the talent gap is acute; attracting AI/ML engineers to a traditional manufacturing hub requires deliberate strategy and partnership.

Success hinges on starting with focused, high-impact use cases that demonstrate tangible financial returns, thereby building internal momentum and securing ongoing investment for a broader AI transformation aligned with the parent company's electrification goals.