Why MBAi matters for managers now: insights from Charles Kuai '06 MBA
AI used to be a side conversation. For Charles Kuai, it's been core for more than a decade. He's founded and scaled technology companies, including Cerence, whose AI now runs in more than a billion cars. He also helped lead Nuance as senior vice president and served as the founding president of its Greater China Division.
Today, Kuai is a founding Industry Advisory Board member for Northwestern's MBAi program - a joint degree between the Kellogg School of Management and the McCormick School of Engineering. His take is simple: this program isn't optional for modern leadership.
"Two parts of the brain" in one program
"MBAi is a frontier program," Kuai said. "The thing that is very unique about the program is that it's a collaboration between a business school and an engineering school, so you are getting the two parts (of the brain) working together."
That mix shows up in the classroom and in real projects. Graduates learn to speak with C-suite leaders and with machine learning engineers and data scientists. They don't just pitch ideas - they know how to scope, build, and ship.
From concept to outcome: the Capstone
Kuai continues to mentor teams and connect companies to the MBAi + MSAI Capstone. Students from MBAi partner with peers in Northwestern Engineering's Master of Science in Artificial Intelligence (MSAI) program for one quarter to solve a real business problem.
This past year, he connected the program with ViewSonic for a sponsored project that won top honors at the MBAi + MSAI Capstone Showcase. The brief: how does the textbook change in an age of AI? The team's solution pushed the field closer to personalized learning.
Why managers should care
AI isn't a sidekick anymore. It's part of the org chart.
"Companies are going to be powered by anywhere from 1 to 99 percent synthetic resources," Kuai said. "If you don't understand how to manage synthetic staff, you're not going to be in a leadership position."
That means you need fluency in both strategy and systems. You must be able to define the problem, size the value, and work hands-on with people who build the models and data pipelines.
Kuai's career shift: a useful lesson
Kuai left Cerence and full-time work in 2023, but he doesn't call it retirement - he calls it "professional graduation."
"Retirement is a bad business model," he said. "You are born, you go to school, you work, you retire, and you die. In modern retirement, I'm moving into a portfolio career, meaning I'm engaged in different verticals at the same time, all related to artificial intelligence."
For managers, that's a prompt to update your own model. Broaden your scope, deepen your AI fluency, and align your work with where value is compounding.
Practical moves for management
- Build bilingual skill: business and ML. Learn model basics, data quality, evaluation, and deployment. You don't have to code, but you must ask precise questions and judge tradeoffs.
- Design hybrid teams. Set clear ownership between humans, AI systems, and vendors. Document what's automated, what's supervised, and what requires expert review.
- Run capstone-style pilots. Pick one workflow with measurable lift (revenue, cost, speed, accuracy). Time-box to 6-10 weeks. Share results, then scale or shut down.
- Set policy early. Define data sources, IP rules, and model risk checks. Make compliance and security part of your sprint cadence, not an afterthought.
- Upskill managers first. Your middle managers translate vision into daily decisions. Invest in their AI education before pushing tools to frontline teams.
What MBAi adds
MBAi blends leadership training with technical rigor, then tests it in live company projects. That's why Kuai backs it so strongly. "I strongly believe that we need to prepare business leaders with an understanding of this very important technological platform," he said. "Absolutely, it's going to serve a purpose."
If you lead a team, the takeaway is clear: develop the skill to manage people and "synthetic staff" together - or risk falling behind those who do.
Next step for managers
If you're mapping skills for your role or your team, explore role-based AI learning paths here: Complete AI Training - Courses by Job. Start small, measure impact, and build from proven wins.
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