Fixing Automotive AI: Lakshmi Duvoor on Scaling with Purpose

Fixing Automotive AI’s Scalability Challenge

Artificial intelligence is transforming industries worldwide, and the automotive industry is no exception. While some automakers are leading the way with predictive maintenance and advanced battery management, others struggle to move beyond pilot projects that drain budgets without generating meaningful results.

The difference lies in approach. Too often, AI programs are launched from a technology-first mindset focusing on models and algorithms rather than the business problems they are meant to solve. This not only delays value but also leads to stalled initiatives when executives fail to see measurable outcomes.

The Complexity of Automotive Data

Automotive manufacturers face one of the most complex data landscapes in business today. From sprawling supplier networks to legacy systems accumulated over decades, many companies juggle dozens of ERP platforms just for finance alone.

This complexity makes building unified data foundations extremely costly. Instead of enabling AI innovation, the effort to merge disparate systems often consumes budgets long before results can be realized. Worse, poor data quality erodes the accuracy of models, undermining trust in AI’s ability to guide decisions.

Where AI Is Delivering Impact

Despite these hurdles, real-world examples prove AI’s potential when it’s applied with discipline:

• Connected vehicle data allows automakers to detect emerging design or component issues early, helping prevent large-scale recalls.
• Battery management systems in electric vehicles now use AI to calculate range with higher accuracy, factoring in traffic, driver behavior, and environmental conditions.
• Operational AI enables CFOs to predict cash flows with targeted datasets, delivering insights without the complexity of full ERP integration.

These use cases show that when business problems lead, AI becomes a true driver of efficiency, cost savings, and customer satisfaction.

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A Problem-First Path to Scale

The most effective way forward is to flip the traditional model. Instead of trying to build a massive data architecture upfront, organizations should:

1. Start with a specific business problem.
2. Identify the minimum dataset required to address it.
3. Pilot with simple tools before expanding into larger infrastructure.
4. Scale iteratively, allowing the data ecosystem to grow organically.

This approach avoids wasted investment on endless curation and shifts AI from an IT-led experiment to a business empowerment tool.

The Road Ahead for Automotive AI

AI offers the automotive industry a chance to boost efficiency, predict issues before they escalate, and elevate customer experiences. But real success means shifting focus from chasing technology to solving the problems that matter most.