Shake a stick and you’ll find a quote from an older executive speaking about the revolution of Artificial Intelligence (AI) and how this exploding technology shall revamp the world. As an example, former Ford Chief Executive Officer, Mark Fields (age 64) opined during a recent interview with CNBC, “With the advent of AI and significant improvements in digital assistance, I think that’s going to turbocharge the type of offerings that automakers can offer their customers.”

But maybe the most accurate and relevant quote for the current era comes from Jean Baudrillard, a deceased, French sociologist, who pronounced, “The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence.” Yes, Large Language Model (LLM) chatbots can scour the Internet, train themselves on a given approach based upon vast amounts of textual data, and provide natural language, conversational responses to corporations. But groundbreaking, innovative strategies cannot be built upon such historical data. They must take the existing, building blocks at hand, imagine a design that’s “just crazy enough” yet easily buildable, and align the ‘bots and humans to deliver. As previously summarized by Jeremy Spaulding, Evolve Impact’s CEO, “… managing and improving innovation requires a complicated mix of mental clarity with deep technical knowledge, creativity, and business prowess focused on prioritizing strategies and leveraging resources. It’s not easy.”

In fact, in the article “The Essential Skills That Will Define Success In The AI Era” the author summarizes the findings from Coursera’s analysis and states “The rise in demand for these skills suggests that while AI may handle many tactical tasks, strategic thinking and relationship building remain uniquely human domains.”

Applying this logic to the automotive world and attempting to understand therein what roles shall be critical could very well be the difference between corporate dominance and imminent demise. Ironically, both behind the automakers’ curtains and exposed to the Internet, the aging executives familiar with a mechanical past have stumbled upon some fallacy answers while arguably the best answer remains a mystery; not just on its value, but also how to identify, train, and retain this MVG (Most Valuable Guru).

Rather than expose the strategic organizational design strategies of various automakers, I decided to pull together and summarize the answers from Google, Chat GPT and Microsoft’s AI when asking the crucial roles in the Artificial Intelligence and Software-Defined Vehicle era(s):

Per Google’s Gemini AI, “As SDVs become more sophisticated, the need for skilled software developers to write, debug, and update vehicle software will be paramount. They will be responsible for developing and deploying new features, improving performance, and fixing bugs, often through over-the-air (OTA) updates.” This surprising answer is likely the least correct since AI is disrupting this job function the most. In a New York Times article entitled, “A.I. Is Prompting an Evolution, Not Extinction, for Coders,” David Autor, a labor economist at the Massachusetts Institute of Technology (MIT) said, “A.I. will deeply affect the job of software developers, and it will happen faster for their occupation than for others,” somewhat supporting the prediction by Mark Zuckerberg that A.I. will match the performance of a midlevel software programmer by the end of 2025.

All three programs mentioned Data Science as crucial with Chat GPT having the best argument, “[This role] directly contributes to product differentiation and customer experience [by] transforming data from vehicles into insights and revenue (e.g., usage-based insurance, driver-behavior analysis) and lays the groundwork for compliance with data privacy and model-governance regulations.” While this logic is certainly not flawed, it doesn’t account for A) little of automotive’s differentiation is created via data insights, and B) with extensible architectures that permit reflashing, the barriers to becoming a fast follower on new technology is much lower, especially in the Fast East where Intellectual Property (IP) is not as respected.

As Microsoft’s CoPilot suggests, “Software-driven cars aren’t just about code; they must feel intuitive and seamless for users. This leader defines how drivers interact with AI, from voice commands and gesture controls to predictive personalization and infotainment systems. Their work ensures that AI enhances the driving experience rather than overwhelming the user with complexity.” Again, the argument for this role appears valid: strategy, creativity, and differentiation; all things where A.I. struggles. However, an automaker willing to learn from A.I.’s initial suggestions and feedback on iterative designs can quickly learn how to minimally improve the user interfaces and brand appearance.

Arguably, the Most Valuable Guru (MVG) is the Chief Software Architect (CSA) of a vehicle. In a recent LinkedIn OpEd entitled “The Return of the Software Engineer”, Peter Abowd stated it well, “AI is fueling a return to smaller, focused engineering teams that can clearly define problems and solutions, allowing AI to support—not replace—real engineering work. Businesses that embrace this shift will clarify their competitive edge, redeploy the developer-heavy workforce, and ultimately increase innovation and opportunity.” This is signaling the risen value of architecting wisely, rather than

For example, the creation of a vehicle platform is $6-10B and many automakers haven’t yet realized a flexible, extensible, software-enabling platform, the business case quickly aligns with this sentiment. Both the cost savings and additional revenue from reusable software and networks could easily be the one, true competitive edge.

Interestingly, Copilot did suggest the Software Architect as a nominee, which mirrors that a few automakers have also recognized their value. “Traditionally, automakers focused on hardware, but the rise of [Software-Defined Vehicles] shifts that emphasis to software ecosystems. A CSA leads the development of a scalable vehicle software architecture, integrating AI-driven features like autonomous driving, predictive maintenance, and over-the-air updates. Their expertise ensures seamless communication between cloud systems, in-vehicle processors, and user interfaces, keeping cars adaptive and future-proof.”

Now the next challenges: correctly identifying a good architect, how to train this MVG, and most importantly, use them to create that differentiation.