You can think of my role as a “Chief Digital Information Officer.” I lead the Digital Technology Solutions (DTS) team, which provides IT services across the entire institution. Our work spans multiple areas, including:

• Academic Technology Solutions – Supporting classrooms and learning technologies.

• Infrastructure Services – Managing data centers, networks, telecom, and endpoint devices.

• Cloud & Collaboration Services – Enabling seamless communication and teamwork.

• Cybersecurity, Risk & Compliance – Ensuring data security and regulatory adherence.

• Enterprise Applications – Overseeing ERP, CRM, and other core business systems.

• Digital Transformation – Driving initiatives in AI, data analytics, UI/UX, and DevOps.

• Research Computing – Providing advanced computing solutions for academic research.

• Business Services – Handling project management, contracts, procurement, and HR.

It’s a broad and dynamic portfolio, designed to support the entire institution and drive innovation across all facets of technology.

Digital transformation is kind of a fuzzy term. We define it as the modernization of our people, processes, and technology to align with our strategic vision, Next Lives Here. This vision drives us to be innovative in the solutions we offer and to cultivate the next generation of innovators and leaders through education.

The transformation is fundamental to this vision, requiring advancements in technology, people, and processes. I define it through three key pillars: Excellence in IT Operations, which establishes a strong foundation; Cybersecurity, ensuring the protection of data and digital assets; and Modernization, enhancing systems, processes, and workforce capabilities.

We have significantly modernized our servers, storage infrastructure, and classrooms, while also enhancing wireless services across the institution. On the cybersecurity front, we’ve strengthened our risk posture by reducing vulnerabilities and ensuring comprehensive asset coverage with security tools. To raise awareness, we’ve conducted Information Security roadshows across campus, educating users on potential risks.

One of our most groundbreaking initiatives is the establishment of a fully student-staffed Security Operations Center (SOC), making us one of the first institutions in the country to do so. This initiative provides students with valuable hands-on experience through internships and co-op opportunities.

Additionally, we have prioritized automation in configuration and patch management to improve efficiency.

We’re also exploring GenAI methodically and responsibly. While the potential is vast, we recognize the associated risks and are taking a careful, strategic approach to adoption.

It is similar to a center of excellence that drives AI initiatives across the institution, and has four subcommittees:

• Academic use cases: Focused on teaching, learning, and research

• Inclusion, ethics, and responsible AI: Addressing bias and fairness

• Enablement: Deploying AI tools and capabilities

• Policy and guidelines: Establishing governance frameworks

On the enablement side, we’ve deployed BearcatGPT, our private OpenAI instance on Microsoft Azure. We’re also doing a lot of work on robotic process automation, to automate manual processes and eliminate a lot of the manual work.

We’ve made significant progress on policy and guidelines, including launching our AI website (uc.edu/ai), which provides policies and guidelines for teaching, learning, and research, along with AI tools and resources.

Additionally, we’ve deployed chatbots to support various institutional services, including enrollment, housing, parking, and libraries, with plans to expand further. On the data and analytics side, we have established a comprehensive data strategy to enhance decision-making and drive innovation across the university.

Currently in pilot mode for 1,000 users, the private large language model (LLM) helps us explore use cases and assess a potential broader rollout. We are one of the first universities to deploy a private LLM.

A key benefit is secure access, ensuring university data remains protected. Previously, users relied on public AI platforms like ChatGPT, inadvertently exposing university data. With BearcatGPT, we safeguard information while enabling advanced model training. Early successes include applications in the medical field, where AI supports private health data use cases and personalized tutoring solutions.

We’re also conducting a cost-benefit analysis to determine who benefits most from the technology. Given the significant costs, a full-scale deployment to all 53,000 students would require substantial funding. This year’s pilot phase will help refine our rollout strategy, prioritizing research, tutoring, and high-impact user groups.

Additionally, we’re enhancing AI awareness through a university-wide roadshow, engaging colleges and departments to identify use cases and prototype solutions. We’ve also automated manual processes using tools, improving efficiency. While still in the early stages, these efforts mark significant strides in AI adoption at the university.

Like many large institutions, our data is highly siloed — spread across student systems, human resource, finance, and research. The goal is to provide a unified view.

We can’t do that today because they’re on their own little data swamps. About a year and a half ago, we launched the Bearcat Insights Program, an enterprise data strategy focused on unifying our data architecture and governance.

We are nearing completion of our enterprise data lake — sometimes called a data warehouse or lakehouse — set to launch this spring. This initiative will centralize clean data from multiple sources, creating a unified source of truth. To ensure data integrity, we are implementing governance using specialized tools, while cloud and data platforms serve as the foundation for storage and management.

We’ve also established committees of data stewards and owners, along with data dictionaries and definitions, to standardize information across the university. In parallel, we are modernizing our infrastructure by migrating workloads from our data center to AWS and Microsoft Azure. This shift improves resiliency, disaster recovery, and scalability. Additionally, we are consolidating and streamlining our institution-wide Salesforce implementation.

So the short answer is we don’t have a unified data foundation, but that’s what we’re working toward. This will serve as the backbone for GenAI. Right now, BearcatGPT is trained on siloed data, but our goal is to establish a lakehouse architecture as a single source of truth. Once in place, this will enable more robust and reliable AI-driven insights across the institution.

It is within our domain, within Azure. This keeps it securely within the uc.edu domain, much like our email and other cloud-based services. While we rely on Microsoft for foundational security, we have additional safeguards, including single sign-on authentication. Access to BearcatGPT is restricted to designated pilot users only.

Data integrity and validity depend on the quality of each domain’s data. While we have reliable data within individual silos, we’re not yet at 100% accuracy across the board. Once our data warehouse is fully established, we’ll have greater confidence in the consistency and accuracy of our datasets. Right now, the challenge lies in connecting siloed data.

The transition from Oracle to higher education brought both similarities and differences. Both are large organizations, but corporate environments prioritize shareholder value and profitability, while public institutions are not driven by profit. This shift has both advantages and challenges.

At Oracle, decision-making was highly top-down, often dictated by leadership. In contrast, higher education operates through collaborative governance, committees, and consensus, making decision-making slower but more inclusive. As a result, processes take longer and budgets are more constrained compared to corporations.

A key lesson has been integrating corporate discipline into higher education, ensuring institutions operate efficiently. This is especially critical with the impending “enrollment cliff,” where declining high school graduates will impact college enrollments, leading to consolidation and institutional challenges.

Another contrast is technology adoption. While research in higher education is cutting-edge, institutional infrastructure often lags behind. Cloud adoption, for instance, is still in its early stages, whereas the corporate world embraced it years ago. Bringing modernization and efficiency from the corporate sector into higher education has been a key focus. This approach ensures institutions remain sustainable and competitive in a rapidly evolving landscape.

Our strategy revolves around three major pillars.

1. Eliminating technical debt: This involves reducing outdated infrastructure and ensuring essential investments in technology. While technical debt can never be entirely eliminated, minimizing it is crucial for long-term efficiency.

2. Best place to work: Our goal is to be the best place to work by fostering growth and development. IT is a knowledge-driven field, where people are our greatest asset. Bringing them along on the modernization journey, especially those accustomed to legacy systems, is essential. Upskilling, reskilling, and engagement are key to ensuring they evolve with technological advancements.

3. Cybersecurity and resilience: Security is a priority, but resilience is equally important. Cyber threats are inevitable, so beyond protection, we focus on disaster recovery, data backups, and cyber resilience. If an attack occurs, we must ensure minimal disruption without relying on ransom payments.

Beyond these pillars, modernization plays a crucial role. AI deployment is a priority, but data quality, governance, and platforms must first be optimized over the next three to five years. 

We are also exploring emerging technologies like virtual and augmented reality for education, IoT for smart campus solutions (e.g., parking systems), and quantum computing and blockchain for research. Additionally, on the mechanical engineering side, robotics and automation are gaining traction.

These initiatives will position UC at the forefront of research, education, and digital innovation.