AI in automotive supply chains: Leadership, data and closing the literacy gap

In this clip from Automotive Logistics' recent livestream, Mazda North America’s director of AI transformation John Rich and Paul Harris, director of solution consulting at Loftware discussed how organisations can close the AI literacy gap and turn experimentation into lasting capability.

AI is rapidly becoming embedded within automotive supply chains, changing how teams operate and the skills organisations require. From planners evolving into analysts to IT expertise being integrated into operational teams, the industry is beginning to rethink how digital transformation reshapes roles.

However, as adoption accelerates, many companies are discovering that technology alone does not guarantee success. Leadership understanding, data quality and a clear business case remain essential foundations for AI initiatives that deliver real operational value.

Mazda's Rich and Loftware's Harris explored how organisations can build the right foundations for AI and avoid the pitfalls of hype-led adoption, discussing why executive-level AI literacy is critical, how companies should define the problems they want to solve before implementing technology, and why understanding data is often more valuable than rushing to deploy new tools.

Leadership must drive AI transformation

Rich emphasised that one of the most overlooked aspects of AI adoption is leadership understanding. According to him, many transformation efforts stall when senior leaders cannot clearly articulate the value proposition or ask the right governance questions.

"I think the leadership is really a key aspect here, and that sort of dimension is often underweighted." Rich said. "AI transformation stalls when senior leaders can't articulate the value proposition and they can't ask those hard governance questions. You really should start with investing in executive-level AI literacy explicitly. You don't need to start with deep technical training. I've said this before on at previous events at some of the conferences that we've done together, you don't need to go out and hire AI engineers to come in. It's about data."

Rather than focusing first on hiring specialised engineers or adopting complex platforms, companies should invest in improving AI literacy at the executive level. When leaders understand both the opportunities and limitations of AI, organisations are better positioned to implement meaningful solutions.

Start with the business problem

Both speakers stressed that AI should never be implemented simply because it is trending. Instead, organisations should begin by clearly defining the operational challenge they want to solve. Only after identifying the business problem should they evaluate whether AI is the right tool to address it. Without that clarity, AI risks becoming little more than experimentation rather than a capability that drives measurable improvements.

Data readiness is the real foundation

A recurring theme in the AI discussion is the importance of data quality and governance. Rich noted that AI systems are only as good as the information they are built upon. Poor data will simply produce poor results at greater speed and scale. For that reason, organisations may benefit more from spending time understanding and organising their data than from rushing to find a technology vendor.