When it comes to AI, many companies are looking to determine where it creates value, pouring significant resources into its potential applications. Seeking a competitive advantage, companies want to pinpoint where AI can streamline and accelerate various processes. But where does AI actually excel across supply chains?
This question brought professionals from GE Vernova to the MIT Center for Transportation and Logistics (CTL) last summer for an intensive program on the most useful applications of AI across supply chains.
Rather than sending software engineers or data scientists, GE Vernova enrolled operational leaders responsible for products, projects, and strategy. These professionals are expected to bridge the gap between emerging AI capabilities and day-to-day business decisions. Their experience reflects a broader shift taking place across industry, which is the recognition that successful AI adoption depends as much on leadership capability as it does on technical capability.
For Aubrey Jacobson, a Senior Technical Product Manager at GE Vernova, the goal was to move beyond the public conversation surrounding generative AI. "My main goal coming into this experience was to learn about all of the different applications of AI, rather than just your generative ChatGPT," she said.
Instead of viewing AI as a single technology, she left with a broader understanding of how machine learning, optimization, and other approaches can be applied across supply chain operations. "I definitely achieved that [goal]. Hearing from all the TAs and professionals here about how they see AI and what their research is about, I learned a bunch of different ways AI can be used."
That distinction holds important stakes. Although many organizations have introduced employees to AI through productivity tools, operational transformation requires a much deeper understanding of the technologies behind them.
"You hear a lot about AI," Jacobson explained, "but it's like a black box. You don't know the theory." Understanding that theory, she said, fundamentally changed how she thinks about applying AI. "I currently use AI in my work to help with productivity tasks—small tasks that would have taken me a long time. From this class, I've learned how I could use AI on bigger projects and help guide strategy for the projects I'm leading."
Her experience reflects a common transition occurring across industry. Organizations often begin by adopting AI for incremental efficiency gains. The greater opportunity comes when leaders understand enough about the technology to redesign processes, evaluate opportunities, and incorporate AI into strategic decision-making.
Nathan Carr, a Technical Project Manager at GE Vernova, arrived with a similar objective. "My main goal," he said, "was to develop my foundational AI knowledge—from the basic machine learning concepts, what models are available out there, and understanding how I can apply it in the supply chain."
For Carr, the value of the program came from connecting theory with implementation. "This was a great opportunity to both learn the fundamentals of AI and machine learning, but also seeing how I can apply it practically in the projects I am pursuing in the future."
That emphasis on practical application is increasingly what organizations are seeking to use AI to its full potential. As AI technologies evolve rapidly, companies need leaders who can ask informed questions, evaluate potential use cases, collaborate effectively with technical teams, and determine where AI can generate measurable operational improvements.
Perhaps the most telling takeaway from GE Vernova's participants was not about a particular algorithm or software platform. Jacobson described leaving MIT with something less tangible but arguably more important. "I think the biggest thing I plan to take back to GE Vernova is the mindset that I've cultivated here at MIT. Really putting down my work and becoming a student has opened up a different side of my mind, a more innovative side."
That mindset is precisely what organizations increasingly seek as AI reshapes supply chain management. Technical tools will continue to evolve, but competitive advantage ultimately depends on leaders who understand how to recognize meaningful opportunities, challenge existing assumptions, and translate emerging technologies into operational results.
As companies like GE Vernova continue investing in AI education for their leadership teams, they offer evidence that the future of supply chain transformation is not simply about adopting new technology. It is about equipping the people responsible for making critical operational decisions with the knowledge and confidence to use that technology effectively.
For organizations preparing for that future, AI education is becoming less an optional professional development opportunity and more so a strategic investment in long-term competitiveness. For more information about this year’s AI-Driven Supply Chain: Advanced Training for Next-Gen Leaders program in July, read more here.
