Business Fellow Travis Diepenbrock Brings Real-World AI Insight

05-05-2026

Purdue’s Mitch Daniels School of Business is welcoming a new voice grounded not in theory, but in execution. New Business Fellow Travis Diepenbrock brings nearly a decade of hands-on experience helping organizations navigate the fast-moving world of artificial intelligence.

Currently a principal advisor at World Wide Technology, Diepenbrock has built his career at the intersection of data, operations and emerging technology. His early work in supply chain roles at major consumer goods companies evolved into leading AI-driven transformation efforts for global clients. That journey now informs what he hopes to contribute at Purdue: a practical, outcomes-focused lens on AI adoption.

“I think universities tend to be caught in academia,” Diepenbrock says. “AI is moving at light speed.”

A practical framework for AI adoption

At the core of Diepenbrock’s perspective is a framework for organizations seeking to adopt AI effectively. He emphasizes three critical priorities:

• Start with return on investment (ROI). Too many organizations pursue AI because of hype rather than business need. “If that’s not the first thing they’re thinking of, they’re starting on the wrong foot… I will add that not every organization needs significant amounts of AI in every business unit,” he says. “If the ROI isn’t there yet, don’t pursue AI for that specific use case.”
• Build a clear operating model. Successful AI adoption requires defined roles, structured processes and a pathway from idea to pilot to scaled deployment. Without this, initiatives stall before delivering value.
• Bring security and compliance in early. “They normally get brought in at the end and say, ‘whoa, absolutely not,’” Diepenbrock says. Engaging these stakeholders upfront reduces risk and accelerates deployment.

Together, these principles reflect a working philosophy for companies adopting AI. Treat AI as a set of technologies that impact organizational culture and operations, not just a technology decision.

The next frontier: speed and scale

Looking ahead, Diepenbrock sees two major shifts shaping the future of AI.

The first is the rise of AI-assisted software development, dramatically accelerating innovation cycles. “Once it would have taken weeks to develop code, now we can do it in days,” he says, pointing to projects where production-ready prototypes emerged in under a week.

The second is the need for governance at scale, particularly as organizations deploy hundreds of AI agents. Leaders, especially CTOs, will need systems to monitor, manage and control these tools, ensuring quality, security and ROI.

“The backlog is almost essentially gone,” Diepenbrock says. “We have to retrain our brains to prototype something in 10 prompts.”

Bridging academia and industry

At Purdue, Diepenbrock plans to engage both students and alumni through guest lectures and industry storytelling. His goal is to expose learners to real-world AI journeys, complete with challenges, trade-offs and results.

“My key objective is to talk about the practical application of AI,” he says. “Here’s a real customer. Here’s a real objective. Here’s what actually happened.”

He shared one example of applied AI that involved a global restaurant chain struggling with inaccurate sales forecasts across thousands of locations. By combining internal historical data with external variables like weather, events and local demand signals, his team built predictive models that improved accuracy and reduced waste.

“We’re now giving the CFO more informed data to make really critical decisions,” he explains. His perspective — grounded in experience across hundreds of client interactions — positions him as a bridge between the classroom and the C-suite.

As AI continues to reshape industries, Diepenbrock shares from his experience: It is time to move beyond mere experimentation. It’s time for disciplined execution. “The winners won’t be those who experiment the most,” he says, “but those who operationalize the fastest.”