In this episode of Viaduct Visionaries, Matej Drev, COO of Viaduct, sat down with Mark Norman, Managing Partner at FM Capital, to discuss the role of data and AI in the automotive and transportation sectors. Mark, a seasoned executive with leadership roles at Zipcar, Chrysler Canada, and others, shared his perspective on how emerging technologies are driving efficiencies and reshaping the industry.
Here are the highlights from their conversation.
Matej Drev: The automotive industry has seen transformative changes over the last decade, particularly with CASE technologies – connected, autonomous, shared, and electric. How are these reshaping the industry, and what role does AI play in this transformation?
Mark Norman:
We’re now in the second phase of CASE. A lot of investment – by strategics, venture capital, and startups – went into developing these technologies. Now, we’re seeing the shift to commercialization, where they’re driving real efficiencies for operators.
Take connectivity as an example: GM had 4G connectivity in cars back in 2014. Today, you see entire fleets – some lasting 20 or 30 years – fully connected. This connectivity allows operators, whether it’s a government fleet of 30,000 vehicles or a small business fleet, to focus on uptime and mobility metrics rather than unit counts. It’s a huge shift.
Autonomy is another exciting area. Robotaxis are now operating in cities like Las Vegas and Phoenix, providing fleet efficiency gains. While we’re not at a point where the average consumer can use an autonomous car for daily commutes, these technologies are becoming viable in niche markets.
Electrification is similar. States with clean-air mandates, like California, are adopting EVs in specific channels and use cases. These are areas where AI can play a critical role in optimizing adoption and delivering efficiencies for operators.
Matej Drev: Connected vehicles generate vast amounts of data. How can automotive companies – whether OEMs or fleet operators – leverage AI to turn this data into actionable insights and drive ROI?
Mark Norman:
AI is crucial for making sense of the mountains of data generated by connected assets. It can identify patterns that humans would miss. For example, you might use AI to find that a specific set of vehicles in a car park during a certain time of day is responsible for safety risks or downtime.
The power of AI is in its ability to make real-time learning possible, even with disparate datasets. Consider a fleet with varying vehicle ages, powertrains, and usage conditions. AI can process all that complexity to deliver insights that improve reliability, safety, and product development.
The key is to translate that data into meaningful insights, not just more noise. That’s where many companies struggle – without the right structure, data security, and governance, they can’t fully capitalize on these opportunities.
Matej Drev: AI has started to transform quality and maintenance in the automotive space. How have you seen it impact these areas, and what examples stand out to you?
Mark Norman:
In the past, quality was siloed. Manufacturing focused on initial quality – ensuring vehicles were built to spec. Field service and warranty issues were handled separately. Tesla disrupted this model by closing the feedback loop. Elon Musk insisted on direct feedback from vehicles and customers, much like your phone sends feedback to Apple. This integration is now indispensable, especially as software-defined vehicles become more complex.
Even legacy OEMs are adopting real-time feedback loops. For example, Nissan uses connected vehicle data to rapidly detect and resolve field issues, feeding insights back into product development. This approach enables them to identify and address problems orders of magnitude faster than before.
On the maintenance side, AI enables customization. Fleets traditionally follow rigid preventative maintenance schedules, but AI can adjust those schedules dynamically based on real-time data like payload, weather, or vehicle usage. For instance, at Zipcar, we managed 10,000 vehicles with a strict schedule for oil changes and tire replacements. Imagine if the cars themselves could tell us when they needed servicing based on actual conditions. It would have been a game-changer.
Matej Drev: Despite these clear benefits, many companies are hesitant to adopt AI. What do you see as the main barriers to adoption, and how can they be addressed
Mark Norman:
One major barrier is workload. Fleet management teams have been downsized over the years, and many operators fear that more data means more work. This is where AI can help by delivering exception-based reporting. Instead of overwhelming operators with more data, it can surface only the most critical insights.
Another challenge is the mindset shift required. Many operators have seen technology make their jobs harder, not easier. To overcome this, we need to simplify outputs so they integrate seamlessly into existing workflows. It’s not about showing off the complexity of AI models – it’s about delivering results that are intuitive and actionable.
Matej Drev: You invest in startups at FM Capital. What trends are you seeing in AI applications for automotive and transportation, and what advice do you have for startups in this space?
Mark Norman:
We’re seeing exciting developments in the application layer of AI – solving specific problems like fleet uptime, vehicle quality, and customer service. Startups like Viaduct are addressing pain points that many companies face but lack the resources to tackle themselves.
My advice to startups is to focus on solving real problems for real customers. Too often, we see technology solutions that are answers looking for a question. Engage with industry stakeholders early to ensure your solution delivers measurable ROI.
Matej Drev: Looking to the future, what excites you most about AI in automotive, and what advice would you give to companies preparing for these changes?
Mark Norman:
The automotive industry touches everyone – it’s a critical part of the global economy. AI’s applications will extend across functions, from customer service to product development to city planning. The key is to start somewhere – companies that embrace these changes now will be the leaders of tomorrow.
Transportation is a complex industry, but it’s ripe for optimization. Whether you’re managing fleets, designing vehicles, or planning infrastructure, AI will play a transformative role in almost every aspect of the ecosystem. The biggest risk is standing still.
The bottom line
Mark Norman highlights how the automotive industry's shift to data-driven and AI-enabled solutions is uncovering both opportunities and challenges. By breaking down silos and leveraging real-time feedback loops, companies can address long-standing inefficiencies, but realizing this potential requires overcoming critical barriers like workload concerns, data governance, and the complexity of implementation.
Learn more about how Viaduct can help you harness connected vehicle data to improve quality and reduce downtime. And stay tuned for more conversations with industry leaders in upcoming episodes of Viaduct Visionaries.
