Enterprise AI
Governance at UT Austin
A university-wide framework for governing artificial intelligence responsibly — enabling innovation across every college, school, and unit while maintaining alignment with UT Austin’s mission, values, and institutional goals.
A governance model built for a federated university
UT Austin’s AI governance framework distributes authority across a clear decision chain, from the Enterprise Technology Executive Committee setting institutional direction down through advisory and technical bodies that carry CSU voices upward. All decisions stop at the ET Executive Committee. UT.AI Studio operates as the institution’s operational engine, coordinating across the entire structure.
UT Austin’s AI governance framework distributes authority across a clear decision chain, from the Enterprise Technology Executive Committee setting institutional direction down through advisory and technical bodies that carry CSU voices upward. All decisions stop at the ET Executive Committee. UT.AI Studio operates as the institution’s operational engine, coordinating across the entire structure.
- Set high-level vision for AI across the institution
- Serve as the final arbiter of all AI governance decisions
- Ensure alignment with UT Austin’s broader goals and mission
Bridges campus stakeholders and enterprise governance structures, ensuring AI adoption reflects the priorities and values of the full university community. Draws on representation from academic leadership, colleges and units, legal and compliance, and student voices. Unresolved issues escalate to the Enterprise Technology Executive Committee.
- Recommend policies and priorities aligned with institutional values
- Advise on opportunities, risks, and emerging needs in AI adoption
- Ensure colleges, schools, and units have a formal voice in AI strategy
- Support transparency and accountability through bias audits and monitoring
- Serve as escalation path for unresolved AI-related issues from across campus
Operates in partnership with the AI Advisory Group to provide specialized technical expertise across the university. Membership draws from colleges, schools, and units, ensuring that implementation guidance reflects the diversity of technologies and needs across campus. One TWG member serves as the official representative to the Advisory Group. Unresolved technical issues escalate to the Advisory Group.
- Evaluate feasibility, scalability, integration, and security of proposed AI initiatives
- Translate Advisory Group recommendations into actionable technical plans
- Identify technology risks, resource requirements, and infrastructure dependencies
- Support bias evaluation and performance assessments for AI systems
- Serve as the primary consultative body for technical design decisions
The primary source of AI innovation across the university. CSUs deploy AI for their specific teaching, research, and operational needs, and provide the institutional knowledge that informs strategy through both the Advisory Group and the Technical Working Group. AI Champions within each CSU serve as the connective tissue between local practice and enterprise governance.
- Manage CSU-specific AI projects and deployment decisions
- Provide feedback on university-wide AI initiatives and tools through Advisory and Technical bodies
- Align all AI use with university governance practices and policies
- Leverage centralized artifacts, data, and support from UT.AI Studio
UT.AI Studio coordinates the essential functions required to advance and manage UT’s AI strategy across the institution. It does not sit in the decision chain but works in close coordination with the Advisory Group, the Technical Working Group, and federated units to ensure governance processes are established, best practices are followed, and enterprise platforms are operated effectively.
Four pillars that operationalize UT’s AI strategy
UT.AI Studio is structured around four complementary functions. Together they ensure UT Austin can move quickly on AI while maintaining the rigor, equity, and accountability the institution requires. UT.AI Studio is designed to grow and adapt as UT’s AI ecosystem evolves.
Establishes policies, standards, and oversight processes that guide responsible AI use across all university contexts — research, instruction, operations, and student services.
Designs and delivers training and upskilling programs for students, faculty, and staff — building the fluency needed to use AI tools effectively, critically, and responsibly.
Provides expert guidance to CSUs navigating AI adoption decisions, vendor evaluations, use case development, and alignment with institutional strategy and data governance requirements.
Oversees and manages the enterprise AI platform infrastructure — ensuring reliability, security, integrations with UT data systems, and technical enablement for CSU-level deployments.
Three tenets that guide UT’s enterprise AI approach
The enterprise AI platform is the operational expression of a three-part strategy — a central UT.AI Studio to provide governance and consistency, a shared platform to democratize access, and a federated model that lets each unit innovate for its own needs.
UT.AI Studio provides the governance infrastructure, institutional expertise, and coordination capacity that allow AI to scale responsibly across a complex university system.
Leverages foundational capabilities — including governance frameworks, training programs, and advisory services — established and maintained by UT.AI Studio to benefit every part of the university.
A university-wide platform democratizes access to best-in-class AI tools — from UT Spark to Microsoft 365 Copilot — ensuring no student, faculty member, or staff person is left behind as AI reshapes higher education.
Provides a scalable, unified foundation for tailored point solutions, ensuring they can be validated and aligned with the university’s broader strategic goals before deployment.
The federated model recognizes that the best AI applications in biochemistry research look different from those in student advising or financial operations. CSUs lead their own AI innovation within a shared governance structure.
Enables customizable solutions that meet specific CSU needs while leveraging centralized data, infrastructure, and governance artifacts to avoid duplication and reduce risk.
What makes the platform work at scale
Three unified supporting capabilities underpin effective rollout, maintenance, and adoption of the enterprise AI platform across the federated CSU model.
Ensures access to quality data for AI applications across the university. CSUs can leverage relevant UT datasets — academic, research, or operational — for specific use cases while maintaining alignment with data governance standards and privacy requirements.
Oversees and manages the enterprise AI platform to ensure reliability and performance. Adapts AI tools for various use cases and utilizes CSU resources effectively, providing the technical backbone that CSU-level innovation depends on.
Drives tool usage and adoption within CSUs to maximize impact. This includes change management, communication, training delivery, and ongoing engagement to ensure AI tools are adopted meaningfully and not just deployed.
Governing AI is how we realize its potential
Strong governance is not a constraint on AI innovation — it is what makes innovation sustainable at institutional scale. The opportunity at UT Austin is significant across every dimension of university life.
Customized learning experiences and adaptive paths for every student through AI-driven course recommendations.
Personalized academic, career, and social opportunities that meet students where they are.
Enabling CSUs to address localized efficiency opportunities with AI-driven insights as they arise.
Strengthening research potential with democratized, state-of-the-art AI tooling available to every lab and faculty member.
Ready to use AI at UT Austin?
Explore the full suite of enterprise AI tools available to every student, faculty member, and staff at no cost — all governed by this framework.
This page was developed with AI support as part of the writing and editing workflow.