Practical vehicle AI examples for drivers

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AI dominates every conversation in the automotive industry, but very few companies know how to make it truly useful. That focus on real value is what led MIT research scientist Dr. Bryan Reimer to write How to Make AI Useful.

The idea began casually over dinner in Lisbon, when someone asked him what he really thought about AI. Bryan didn’t dive into predictions about machines taking over. He focused on something more practical: how AI only matters when it’s built with people in mind.

He breaks AI down into three realities: the excitement of what it could do, the fear that follows when we realize what it might do, and the long, steady work required to make it truly valuable. 

AI can automate the basics and even create new content, but its real strength is amplifying human skill, not replacing it. The goal isn’t an autopilot workforce. It’s a copilot.

That means the fear that AI will take jobs is misplaced. AI changes work; it doesn’t erase it. Just as assisted driving has changed how we drive, rather than removing the driver, AI will shift roles and demand new skills. 

Bryan points out that layoffs blamed on AI are often just business decisions wearing a convenient mask. The real question is how companies use AI to make work better rather than cheaper.

To do that, leaders in automotive need to unlearn old habits. Years of rigid processes, slow decision-making, and fear of change make it hard for AI to deliver value.

He argues that useful AI requires trust and transparency. It’s hard for any organization to move forward when fear, hidden approvals, and layers of bureaucracy control decisions. If employees can’t be trusted to make decisions, AI won’t save them. The real challenge is cultural, not technical.

Bryan expands the conversation globally. Japan is embracing robotics as companions, while Europe is focusing heavily on privacy. Culture shapes how AI grows, and automotive companies need to pay attention to what consumers value, not just what tech can do.

He connects this to China as well. China’s speed is not about dumping features into cars. It’s about building products people can afford and use. If Western brands only chase faster or cheaper without real value, they will lose.

AI becomes useful when companies start small, test real-world problems, and continually improve the tool until it actually helps people do their work. That progress may cost more in the beginning, but better safety features, more accurate data, and enhanced customer experiences rarely come from shortcuts. The goal is not to replace people. It’s to build technology that helps them perform at a higher level.

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Themes discussed in this episode:

• How AI becomes useful only when it is designed to support human judgment instead of replacing workers
• Why the “Wow, Whoa, and Grow” framework helps companies move beyond AI hype and build tools that solve real problems
• How assisted driving proves that advanced technology still depends on human responsibility and oversight to deliver safe, reliable results
• The importance of unlearning outdated processes before applying AI to existing workflows in automotive
• Why a lack of trust inside automotive organizations slows down AI adoption more than the technology itself
• Lessons from China’s speed in product development and why Western automakers should prioritize value and accessibility over rushed innovation
• What automotive leaders can learn from the pharmaceutical model of testing, releasing, and improving technology through data-driven updates over time
• Why leaders should start small, run narrow pilots, and scale only after AI tools prove measurable value for customers and business results

Featured guest: Dr. Bryan Reimer

What he does: Dr. Bryan Reimer is a Research Scientist at the MIT Center for Transportation & Logistics and a key member of the MIT AgeLab. His work focuses on how drivers behave in an increasingly automated world, using a combination of psychology, big data, and real-world testing to study attention, distraction, and human interaction with vehicle technology. He leads three major academic-industry consortia that are developing new tools to measure driver attention, evaluate how people use advanced driving systems, and improve in-vehicle information design, thereby guiding automakers and policymakers toward safer, human-centered mobility solutions.

Mentioned in this episode:

• MIT Center for Transportation & Logistics
• MIT AgeLab
• MIT AVT | Advanced Vehicle Technology Consortium
• AI Sweden | National center for applied AI
• Automotive Suppliers and the Revenue Acquisition Process – Then and Now: 2025 Update
• How to Make AI Useful: Moving beyond the hype to real progress in business, society, and life

Episode Highlights:

[03:04] Lisbon, Wine, and a Big Question: A casual dinner in Portugal, fueled by a few glasses of wine, led to a book built around a simple idea: AI only matters when it helps real people, not just shows off technology.

[05:13] The Wow, Whoa, and Grow: AI starts with excitement, triggers hesitation when its power becomes real, and only becomes useful when organizations move past fear and begin building systems that support people, policy, and long-term value.

[09:55] Fear vs. Reality: Layoff headlines make AI sound like a job killer, yet its real impact is changing how work is done, not removing it, and companies often use AI as an excuse while human skills and responsibilities continue to grow alongside the technology.

[11:50] Header: AI note-taking creates efficiency, but the real shift comes when companies unlearn old processes and use AI to turn meeting outputs into work plans that assign tasks, drive follow-through, and reshape how the work actually gets done.

[15:04] Unlearning to Compete: To meet China’s pace and build vehicles people can actually afford and use, the industry must rethink old development cycles and focus on AI that supports drivers rather than chasing fully automated cars.

[19:31] Different Cultures, Different AI: Japan embraces robotics as companions, Europe prioritizes privacy, and the U.S. remains cautious, showing how each culture adapts AI in its own way and must shape policies that reflect human needs, not just technology trends.

[21:03] Technology Moves Fast. Institutions Don’t.: Austin’s Law explains why automated driving and AI can advance quickly while governments, policies, and organizations move slowly, creating delays driven by fear, inconsistent rules, and low trust within the systems trying to adopt new technology.

[24:39] Trust Before Technology: Layers of approvals, hidden decisions, and bureaucratic red tape break trust inside automotive companies, and without a culture that empowers people to act, AI has nowhere to grow and no one who believes in it.

[27:59] Fix Culture, Then Code: AI can’t succeed in a blame-driven industry, because once decisions are written into software, companies must own them, learn from them, and evolve like the pharmaceutical model that improves systems over time instead of pointing fingers.

[30:14] Copilot, Not Cost-Cutting: AI isn’t a cheap layoff tool, it creates value when leaders plan for lifecycle costs, learn through small pilots, and use it as a decision-support copilot instead of dumping out low-value work.

[35:08] AI Plus People: AI can speed up translation work, but the real value comes from pairing it with human expertise, where the best results may cost more yet deliver a higher-quality experience that’s worth it.

[38:31] Mindset Over Machines: Real progress happens when leaders stop fearing the technology or spending blindly on it, and instead redesign their processes with a practical, consumer-focused mindset that keeps core values intact while evolving how work gets done.

Top Quotes:

[10:28] Bryan: “I don't think AI, like any other technological revolution, is going to shake all the jobs. I think what it is going to do is change the nature of work. It's automation by a different language. Automation doesn't replace work; it changes the nature of it.”

[23:03] Bryan: “Technology is evolving much faster than the institutional changes to support that, and that fear is a limiting factor. And that's where the fear and hype of AI become so challenging: we need to find a middle ground that allows us to build and evolve these technologies forward faster and more efficiently, while managing the overhype. Automation's going to change the nature of work, and we're going to get rid of all employees due to the fear of, "What am I going to do if technology takes over?" And I think a lot of that comes down to balancing trust, trust in institutions, trust in organizations, trust in my colleagues.”

[37:59] Bryan: “We've got to think about what the value proposition for that is and how we deploy AI and other technologies. If we keep chasing better, faster, cheaper, and that's the sole output, I could tell you that the Chinese will win with that. Our strength is going to become how we strategically focus on each of those elements in a more optimal system. And that's exactly how I think Detroit and other western and legacy automakers are going to have to reinvent, driving the mobility experience to compete with a growing potential tsunami of Chinese cars across the world.”