With a community of over 30,000 diverse students, staff, and research partners — encompassing a wide range of skill levels and possible use cases — the University of Chicago has a mission to make AI as widely accessible as possible. In part, this means developing cost-effective, user-friendly AI tools.
But accessibility is only one consideration of AI strategy for higher education. With 30,000 stakeholders, the university faces many of the same technology hurdles and change-management issues any large enterprise might face when building out AI tools and services.
CTO Kamal Badur is responsible for this tricky transformation, managing a balance of collaboration, experimentation, and security as he decides which AI initiatives to pursue and what platforms to build them on.
The inaugural AI-First Podcast features Box Chief Customer Officer Jon Herstein in conversation with Badur about re-imagining work with the power of content and intelligence. Herstein and Badur explore the practical adoption of AI in higher education, touching on the challenges, opportunities, and lessons learned from implementing AI tools across the university’s IT environment.
Key takeaways:
- Involve all kinds of stakeholders from day one in order to get buy-in and develop the right tools
- Bring humility to collaboration to be the best possible AI leader
- Approach AI with an experimental attitude, and move fast
- Never let compliance suffer even as you hustle to get AI products out the door
- Keep accessibility in mind, whatever that might mean to your potential audience
Takeaway #1: Involve stakeholders from day one
Higher education involves a lot of different kinds of stakeholders. In the University of Chicago’s case, this includes about 17,000 students from very diverse backgrounds, along with 12,000 employees and many other affiliated organizations and partners collaborating on cutting-edge research and education.
Within this environment, as they institute AI tools, Badur stresses how important it is to loop in key stakeholders at early stages. He says: “If they're involved and understand what you’re doing, you're going to be a lot more successful — as long as they’re willing to be flexible.”
Badur underlined the importance of involving key stakeholders before you’ve started to build. Early collaboration minimizes roadblocks and encourages everyone involved to participate in creating solutions that balance innovation with compliance. It also avoids “last-minute surprises” and fosters alignment with organizational goals.
Takeaway #2: Bring humility to collaboration
Building on the point about involving stakeholders, true progress comes from listening to diverse perspectives. “None of us know everything,” quips Badur, and this is particularly true when it comes to how AI will impact higher education. If people see you as a rigid AI advocate or even an “AI dictator,” you aren’t being a successful leader in the education space.
In other words, AI leaders can’t simply dictate solutions; they need to engage their whole teams and communities in order to uncover pain points and design tools that will meet the right organizational and business needs.
“Humility and transparency are critical when integrating AI,” Badur insists. “It’s all about collaboration, experimentation, and keeping stakeholders involved from the start. You’ll learn a lot from the people you talk to — what they need, where their pain points are, and how you can address them.”
Takeaway #3: Approach AI with an experimental attitude
He also offers pragmatic advice on rolling out AI tools in complex environments like universities: start small, learn fast, and iterate often. Rather than aiming for perfection from day one, Badur encourages experimentation with prototypes and beta versions. Adopting this sort of iterative mindset allows organizations to test functionality, refine approaches, and integrate stakeholder feedback.
A humble, experimental approach provides flexibility and helps address any emerging issues throughout implementation. The end goal? To build solutions that prioritize functionality and adaptability without compromising innovation or security.
Takeaway #4: Don’t let compliance and security suffer
Before taking any tool live, Badur’s team has to address specific needs like HIPAA compliance and other privacy regulations. So even as he uses a fast-moving, innovative approach to integrating AI into medical education and research, Badur always keeps security and compliance top of mind. For this reason, the institution exercises caution when activating AI features, and seeks out service providers with robust security protocols that match up to their needs — which is how they came to Box.
As Badur tells it, “I had many people come to us and say, ‘I was really skeptical about using a vendor tool because I was concerned about my privacy.’ But I’m not worried anymore. Box is a HIPAA-compliant, highly secure storage environment.”
Takeaway #5: Create a bigger picture of equity and accessibility
With potential AI users across myriad income brackets, backgrounds, physical abilities, areas of study, and levels of technology comfort, the University of Chicago is committed to equitable AI access. Ideally, every community member should be able to benefit from generative capabilities, without barriers like subscription fees, technology complexity, and inaccessible software.
Badur emphasizes: “We didn’t want to say, ‘To get ahead with AI, you need to pay $20 a month, $30 a month, or $200 a month.’ That was another motivating principle for us to build something state of the art, but available to anyone.”
Beyond financial accessibility, the university builds out AI tools with other types of accessibility in mind, including supporting screen readers and other assistive technologies. Inclusivity remains at the forefront of university AI initiatives, underscoring the ethical priorities behind AI adoption.
Catch the full episode
With a thoughtful and pragmatic strategy that addresses specific compliance needs, the university was able to unlock valuable use cases that were previously off limits and create new possibilities for physicians and researchers.
After all, AI adoption isn't one size fits all. Tailoring solutions to both user requirements and practical regulatory concerns is key to successful integration in higher education.
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