5 Big Lessons From UVA Darden’s First Year With AI

When the University of Virginia Darden School of Business announced its partnership with OpenAI a year ago, it signaled more than a collaboration — it marked a commitment to rethinking business education in the age of AI.

Today, as the School continues executing the Darden 2026 Launchpad, its strategic framework for strengthening research impact, elevating the learning experience, and delivering lifetime career value, those early AI investments are contributing to a growing strategic advantage.

Darden is integrating AI into its internal processes by deploying decision-support agents that interpret policies, organize inputs and streamline rules-based workflows, especially those that rely heavily on email. This helps boost efficiency while preserving human judgment.

Examples include an Alt Text Generator that supports accessibility for digital images, a Data Sensitivity Sherpa to guide responsible data handling, and Hoo Helper, a personalized assistant to help admitted students prepare for their Darden journey.

The Darden Report spoke with Marc Johnson, senior associate dean and chief strategy and innovation officer, and Kush Arora, chief digital officer, about what they’ve learned about AI adoption and transformation efforts as applied to Darden’s operations across domains. Their reflections offer a candid look at what it really takes to build an AI-forward enterprise.

What has been your biggest hurdle in Darden’s AI journey so far?

Johnson: Successful AI adoption, like any technology transformation, has to start with culture. Technology is not the hurdle. We see how quickly people can pick up new tools like ChatGPT, whether in their personal or work lives. The challenge for an organization is creating the time and space for exploration so knowledge workers can apply them meaningfully in their roles.

Arora: We have found practices such as encouraging managers to adopt a goal around AI experimentation for their teams, lunch and learns where AI champions share how the tools are helping them in their work, and persona-based learning are some of the ways we are using to help people transition to being more effective with using AI in their roles.

Given some of the headlines, AI adoption may sound threatening at first — we start by having conversations about it as a journey to become more effective with our time and effort, so that people can focus more energy on human-centric tasks. That can build a lot of excitement and buy-in, which leads to more experimentation and impact.

How do you cultivate an “AI-forward” culture at every level of the organization?

Johnson: Both “top-down” and “bottom-up” focus is required for success. Top down means you need leadership commitment across the organization to make it an enterprise priority. Leaders modeling and talking about it helps managers foster an “AI-forward” culture that creates space and time for knowledge workers to experiment, and they feel encouraged and empowered to do so.

“Bottom-up” energy means you create a lot of positive change by unleashing that creativity and creating spaces where people come together to share the art of the possible and learn from each other.

With AI moving so quickly, how do you build governance that’s responsible without being rigid — and what are the essential guardrails you believe every enterprise needs?

Arora: To do responsible AI, enterprises really need to focus on getting three fundamental things in place: First, broad guiding principles; second, processes built to enforce those guiding principles; and third, ways to measure and know what is happening related to AI in an enterprise.

Johnson: Along the way, we have learned that because of the pace at which the technology and industry are moving, it’s not effective to have long, drawn-out policies and procedures on every single scenario. Things like broad ideas on accountability, transparency, intellectual property, etc., make sense, but don’t get tied to tool-specific policies or guidance, as those aspects are changing fast, and you want to be nimble. You want employees to feel empowered by the guidance and that they have a good understanding of what responsibility looks like as a framework, not a series of “yes-no-” or “if-then-” policies.

How are you thinking about employee training and upskilling?

Arora: This comes back to a culture of experimentation and adoption, so people develop the skills to navigate these new tools.

Our focus has been on broad upskilling in prompt design, workflow integration and building judgement around limitations, because those skills are portable across platforms and models.

Second, upskilling only sticks when it clearly connects to someone’s job. You have to be able to articulate “how does this make me more effective in my role?” So, we contextualize skills by function and persona.

Lastly, this isn’t a one-and-done effort. Regular demos, lightweight forums and making AI an active topic in enterprise and team meetings are some of the ways we continue to evolve as the technology evolves.

You’ve developed a three-part framework to help individuals think about AI in their roles. Can you walk us through those questions — and why persona-based application matters so much?

Arora: The key insight here is that the use cases or applications change with personas within the organization, and we need to equip people to navigate that. The three-prong set of questions we are using as a framework within the organization is: How am I using it for myself? How am I fostering an AI-Forward culture within my team? And what external developments related to my role do I need to stay connected with?

Johnson: These three questions start with the self and ask people to reflect on the nature of their work. How are the tools creating the most value for them? What do they learn from that reflection? From there, thinking about how they engage others around them in sharing best practices encourages engagement that will help the team. And finally, looking outward to understand best practices in a rapidly moving environment so we can draw in new ideas and insights.

The Five Takeaways

A year in, several themes have emerged from Darden’s journey — lessons that extend well beyond Charlottesville:

1. Create a culture of experimentation.
2. Make AI a priority at every level.
3. Govern by principles, not platforms.
4. Upskill broadly to stay adaptable.
5. Think in personas, not just use cases.