UT's AI strategy encompasses fostering innovation, establishing governance frameworks, ensuring strong leadership, operationalizing AI solutions, building robust data infrastructure, offering support and advisory services, and empowering the community to engage with AI responsibly.
1. Enterprise AI Platform & Copilot
The UT.AI platform and Copilot for Microsoft 365 both utilize AI but serve different purposes. The AI platform provides scalable tools, such as an LLM platform, for creating customizable AI solutions and streamlining processes across UT. In contrast, Copilot for M365 is an AI assistant embedded in Microsoft apps like Word, Excel, Teams, and Outlook, focusing on boosting productivity by simplifying tasks and providing real-time insights.
2. Innovation and Ideation
This mechanism aims to foster a vibrant, university-wide AI innovation community. Key activities will include encouraging collaboration between different Colleges, Schools, and Units (CSUs), breaking down silos to facilitate knowledge and resource sharing, and promoting a culture of creative exploration and experimentation with AI. This approach will help build a cohesive and dynamic AI ecosystem across the university.
3. Governance
This mechanism aims to create a framework for responsible and ethical AI development and implementation. The framework will balance the need for guidance and oversight with fostering innovation, address ethical considerations like data privacy, bias, and transparency, and be adaptable to meet the distinct needs and goals of different CSUs. Additionally, it will establish clear policies and standards for AI development and use.
4. Leadership
Strong leadership is essential for advancing UT's AI strategy. Our roadmap highlights the importance of a diverse leadership group to oversee and prioritize AI initiatives, articulate a clear vision for AI at UT, set strategic goals, allocate resources, and champion the responsible and ethical use of AI. This approach ensures that our AI efforts are aligned with our values and strategic objectives.
5. Operationalization
This approach aims to translate AI strategy into tangible outcomes by implementing AI solutions at scale. Key activities include developing tools, infrastructure, and processes to support the entire AI lifecycle, ensuring that AI solutions are scalable, sustainable, and integrated into existing workflows, and potentially creating a centralized "AI Hub" to provide access to resources and best practices. This will help embed AI into the fabric of the organization, driving innovation and efficiency.
6. Data and Architecture
Highlighting the critical role of data in AI, this approach focuses on building a robust data infrastructure. This includes creating a centralized data repository to break down data silos and enable efficient data access and management, ensuring data quality, integrity, and security, and developing a data architecture to support AI objectives, including data governance policies and data pipelines. This will ensure that data is effectively leveraged to drive AI initiatives.
7. Support and Advisory
Recognizing that successful AI adoption requires ongoing support, this approach involves providing specialized resources to guide and assist individuals and departments. Key activities include deploying business and engineering resources to assess ideas, evaluate feasibility, and model solutions, providing tailored support based on specific project needs, offering technical guidance, troubleshooting support, and training on AI tools and platforms, and providing advisory services to address ethical concerns, data privacy, and other relevant policies. This ensures comprehensive support for effective AI implementation.
8. Activation
This approach centers on empowering the UT community to engage with AI in a responsible and informed manner. Key activities include increasing AI literacy through educational initiatives, training programs, and workshops, promoting a culture of AI adoption by highlighting AI benefits and showcasing successful implementations, and creating a sense of agency by providing access to resources and support networks. This will ensure that the community is well-equipped to leverage AI effectively and ethically.