Vanderbilt’s AI Playbook: A Case Study through the Campus AI Framework and Strategic AI Compass
Analyzing Vanderbilt’s AI Strategy through the Campus AI Framework
Why Vanderbilt Matters
Vanderbilt University has been one of the pioneering campuses in artificial intelligence. My own journey with generative AI began years ago with Dr. Jules White’s Coursera courses, which gave me foundational insights into this rapidly emerging field.
Recently, I watched the YouTube interview “Breaking Down Vanderbilt’s AI Playbook”, featuring Allen Karns, Chief AI and Technology Officer at Vanderbilt’s Center for Generative AI. The conversation revealed how Vanderbilt designed and scaled its internal AI platform, Amplify.
Amplify is open-source, runs in Vanderbilt’s private AWS cloud, and connects to multiple AI models via API. Instead of paying for commercial licenses like ChatGPT EDU, Vanderbilt chose a per-token pricing model. This gave them cost efficiency, security, and the ability to directly integrate with systems like Outlook, calendars, and databases.
Vanderbilt’s choices provide a real-world example of how higher education can lead with AI responsibly and strategically. In this article, I use their playbook to demonstrate how the Campus AI Framework (CAF) can be applied to analyze a campus AI strategy.
What is the Campus AI Framework?
The Campus AI Framework (CAF) is a model for higher education institutions to adopt AI responsibly, strategically, and in alignment with their mission.
It has two parts:
Eight Institutional Pillars – the structures every campus needs, such as mission alignment, guiding principles, policies, governance, engagement, readiness, roles, and operations.
The AI Strategic Compass – the process and accountability layer that helps institutions choose, measure, and improve AI initiatives using rubrics, metrics, and outcomes.
Together, the pillars provide the foundation and the compass provides the guidance, helping campuses move from scattered pilots to scalable, ethical AI ecosystems.
Part 1. The Eight Pillars – Structure at Vanderbilt
Pillar 1. Campus Mission & Vision
Vanderbilt tied Amplify to both research and operational outcomes, positioning AI as mission-critical.
“How do we affect business outcomes and educational outcomes once we have the application and access to it.”
Allen explained that Amplify was not just a technology project, but a way to connect AI to core university goals: research excellence, operational efficiency, and student outcomes.
Pillar 2. AI Principles
Amplify was made broadly available, emphasizing equity and experimentation.
“Being able to provide broad access across the campus to anybody that wants it is kind of the primary driver of what we’re doing.”
Karns also stressed that the team didn’t want AI to be reserved for only researchers or IT staff, but something that could be adopted across the entire community.
Pillar 3. Policies & Guidelines
Amplify was hosted in Vanderbilt’s AWS private cloud with enterprise security and compliance controls. Open-source design prevented vendor lock-in.
“All of it is running inside of our private cloud infrastructure… I’m able to incorporate the controls that we use for all of our other cloud environments.”
He emphasized that this allowed the team to build AI with the same compliance standards already used for other sensitive systems.
Pillar 4. Governance, Risk & Compliance
Chancellor sponsorship legitimized the program and framed AI as both a risk and an opportunity.
“It takes leadership. If you don’t have leadership buy-in, then it’s going to be a tough sell.”
Karns noted that Chancellor Daniel Diermeier’s support “changed the tone on campus,” making adoption smoother across departments that might otherwise have resisted.
Pillar 5. Engagement & Collaboration
The Staff Fellows Program drew 180 applicants, while faculty used AI in curriculum design.
“We launched a program… 180 applicants and they’ve all got these ideas that are phenomenal.”
Karns also described how faculty wanted help designing systems to support students, and how staff–student cohorts would tackle long-standing pain points across the institution.
Pillar 6. Campus Readiness
The team began with simple pilots (PowerPoint generator, email summaries) and hired staff for curiosity over credentials.
“One of the biggest things we looked for was not whether they were a CS major… but were they curious.”
Karns added that curiosity was critical in such a fast-moving field: “The space is moving so fast… it’s certainly hard to keep up. We use generative AI ourselves to help us implement and write features.”
Pillar 7. Roles & Responsibilities
Roles were clear: the Chancellor and Center for Generative AI provided strategic leadership; a four-person team managed Amplify; fellows and faculty acted as adoption champions.
“Initially, it was just the two of us… at night and on weekends we were emailing and texting and trying different things.”
This highlights how innovation often starts small and grows with clear ownership.
Pillar 8. Implementation & Operations
Vanderbilt used an iterative approach—start small, scale what works, and integrate with enterprise systems.
“Sometimes you just have to pick a small project and go… find something low-hanging fruit and start.”
He also described a range of integrations: “We’ve created tools like a PowerPoint generator, Outlook summaries, calendar assistants, and even APIs that pull data from one system and feed it into another.”
Part 2. The Strategic AI Compass – Process, Metrics, Outcomes, and Rubrics
The Strategic AI Compass provides the process and accountability mechanisms. It ensures that AI adoption is not just about building tools but also about making smart decisions, measuring progress, and learning continuously.
The Six-Step Process
Screen – Vanderbilt compared vendor licensing vs. internal build.
“We quickly realized there were going to be more than a thousand people that wanted it, and it was going to be really expensive.”
Score – Internal build scored higher for cost, security, and integration flexibility.
“We access all of the models through their APIs… we found that it comes out much lower than if we were to license across the university.”
Select – The Chancellor’s endorsement made Amplify the campus platform.
“Having [the Chancellor’s] support… changed the tone on campus for us.”
Plan – A four-person team was formed, hiring for curiosity, with pilots like the PowerPoint generator and workflow automations.
“The biggest thing we looked for was curiosity… we’ve got two graduates that have both been vital members of our team.”
Track – Adoption measured through the Fellows Program and faculty pilots.
“We’re going to have this cohort of staff and students… and we’re just going to go around and start to solve some of these longstanding pain points.”
Reflect – Leaders emphasized applying current tools instead of chasing new ones.
“Even if nothing changed from today, you’ve got 10 years’ worth of work to do.”
Metrics and Outcomes
The Compass builds in measurable signals at every stage:
Screen: Options documented, risks logged.
Score: Evaluations using rubrics for cost, mission alignment, and equity.
Select: Leadership endorsement and decision records.
Plan: Pilots identified, owners assigned, timelines set.
Track: Usage metrics (active users, staff participation), impact (hours saved, workflows improved), cultural signals like enthusiasm.
Reflect: Lessons captured, policies updated, roadmap refreshed.
At Vanderbilt, outcomes included:
Lower costs via per-token pricing.
Over 180 staff applicants signaling broad engagement.
Faculty integrating AI into teaching and research.
Concrete productivity gains, like automated PowerPoint decks and daily email/calendar digests.
Rubrics and Tools
The Compass provides structured rubrics so campuses can evaluate initiatives systematically:
AI-Vision Alignment Rubric – checks if use cases support institutional mission.
Risk Scoring Rubric – rates proposals for compliance, privacy, and bias.
Adoption Scorecard – measures pilot success against usage, satisfaction, and outcomes.
Reflection Rubric – ensures each cycle of learning is documented and shared.
Vanderbilt applied these principles in practice:
Scoring internal build versus vendor licensing.
Tracking adoption through Fellows Program participation.
Documenting leadership legitimacy through Chancellor endorsement.
Closing Insight
Vanderbilt’s AI Playbook shows how institutions can combine structure and process to lead responsibly:
The Eight Pillars (CAF) define what to build: mission, principles, governance, readiness, engagement, roles, and operations.
The Compass defines how to act and measure: six steps supported by metrics, outcomes, and rubrics.
Amplify provides the evidence: a homegrown platform that delivered cost savings, strong governance, and broad engagement.
For higher education leaders, the message is clear: AI adoption succeeds when anchored in mission, governed with transparency, measured with rubrics, and advanced through iterative reflection.