Digital Transformation 2.0: The Age of AI

min read

With the rapid developments in artificial intelligence, higher education is entering a second phase of digital transformation: Dx 2.0. Technology and other campus leaders can take steps now to help prepare their institutions.

Interlocking pieces forming a triangle. Each piece is a different color.
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Over the past five years, higher education institutions have been leveraging digital transformation (Dx) to advance their services, streamline operations, and further strategic initiatives. The EDUCAUSE Dx Journey framework has helped IT and other teams across colleges and universities think through the elements needed for success and lasting impact.

Rapid developments in artificial intelligence (AI) represent a significant opportunity to enhance and magnify the effect of Dx projects. AI can act as an accelerant that boosts the value of Dx efforts through its powerful and broad influence on core components of Dx initiatives, ushering in a new chapter of digital transformation on our campuses. Incorporating AI into the Dx framework provides a pathway for institutional leaders as they explore ways to harness the power of AI across the campus.

Key Components of Digital Transformation

In Washington, D.C., in the spring of 2017, EDUCAUSE convened two expert panels of representatives (including myself) from varied colleges and universities to talk about the transformative significance of technology in higher education. Those panel meetings led to the formation, in 2018, of the EDUCAUSE Task Force for Digital Transformation to further explore and refine this work. Our charge was to better understand what digital transformation meant for higher education and the underlying forces that were acting as transformative catalysts for these changes. The work highlighted the evolution of the role of campus IT organizations from service provider to strategic partner.Footnote1

We grew to understand that the transformative impacts that we were seeing were the result of two sets of factors. The first set was what we called "signals" or "changes" that were occurring outside of our campuses. At the time, these signals included technology advancements such as big data, cloud computing, and universal connectivity, along with other changes such as how people consumed information and interacted with technology.

The second set of factors consisted of a series of intentional shifts that were needed for true transformation to occur. This was about not just shifting the technology but also, and simultaneously, working to achieve systemic shifts in the culture and the workforce of our institutions. Only when sustainable changes were achieved in all three areas would true, long-lasting transformation occur. We illustrated this idea with a "formula":

Intentional Shifts in Technology + Culture + Workforce = Digital Transformation

We used the phrase "Digital Transformation" (Dx for short) because technology (i.e., digital) advances served as a primary catalyst for this transformation, but all three components are needed for success.Footnote2

Fast forward five years, to today. The basic concepts of digital transformation still apply and in many ways are more relevant than ever. Our campuses continue to look to technology to help advance our missions, streamline operations, and provide critical insights and services for our students. We engage in conversations across the institution to help shape the culture, and we continually assess and redefine the roles, skills, and jobs needed from our workforces for success. True transformations are occurring across our campus communities, as evidenced by the examples in the EDUCAUSE Dx Journey.

Digital Transformation 2.0

The explosion in AI developments over the last year has introduced a third transformative set of factors influencing our Dx efforts and has ushered in the dawn of a new era of digital transformation for higher education. In the coming years, AI tools will power new and deeper transformations of the services that institutions offer, will change how those services are provided, and will affect all areas of the college or university. Campus leaders and IT service providers need to be thinking about those changes now and about the steps that can prepare our institutions for the transformations ahead.

Title: Digital Transformation 2.0. Half circle denoting Culture and Workforce divided into sections: Student Experience, Teaching & Learning, Campus Operations, Experiential Learning, and Research. Bar across the bottom of the half circle: Artificial Intelligence. Bar below that: Technology | Universal Connectivity + Robust Data Ecosystem + Scalable Computing Resources + Layered Support Services.

AI changes both sides of our Dx equation. It has the potential to accelerate and multiply the impacts of the technology, culture, and workforce shifts that are needed to achieve digital transformation and also to exponentially increase the positive effects of digital transformation for our institutions, students, faculty, and staff.

In addition, AI acts as a disruptor, requiring that we rethink many aspects of our teaching and learning, institutional operations, campus jobs, and service offerings. It serves as an impetus for change and as an opportunity for reinvention. It raises potential ethical and policy issues that require careful consideration and navigation.

While AI has been around for a while, we are in a period of rapid development and commercialization. Over the past year, much attention has been on public, broad-function large language model (LLM) chatbots such as Open AI's ChatGPT, Google's Bard, and Microsoft's Copilot. Their impact on teaching and learning has required rethinking about student assignments, course curricula, and needed skillsets for tomorrow's workforce. Receiving less attention than these chatbots, but in many ways equally disruptive (if not more), are specific-purpose AI tools and functionalities that are starting to be embedded into everyday workflows and applications. Also of importance are private LLMs that can securely leverage institutional data for AI-informed insights and actions.

Preparing for AI

To achieve the benefits of AI-accelerated Dx 2.0, we should be taking several steps in the areas of technology, culture, and workforce. These steps will ensure that we have the foundations in place to support these new Dx efforts.

Technology

Many of the technology investments we've made to support past transformations will continue to position higher education well for Dx 2.0. But a few additional areas can help us prepare for AI-powered transformations:

  • Build a robust data ecosystem. It's all about the data. The rapid development of AI tools that provide insights and guidance based on institutional data is one of the key drivers of Dx 2.0 benefits. Having a robust data infrastructure is vital. Invest in data repositories and circulatory systems to securely and appropriately move data and make it available throughout the institution, ensure that you can tap into data collected by your cloud-based and on-premises enterprise solutions, and build out your analytics framework. Another important foundational piece is establishing effective data governance processes and privacy policies and procedures to ensure that data is clean and being accessed and used appropriately by these tools.
  • Enable universal connectivity. AI relies on access to large datasets and computational power and often utilizes real-time information from sensors and other devices. Ensuring that your campus has universal connectivity is critical: indoor Wi-Fi everywhere, and outdoor Wi-Fi in key locations. Working with cellular providers will supply adequate cellular service across campus. Partnering with local municipalities on rural broadband efforts and other initiatives can bring connectivity to campus constituents no matter where they are located.
  • Work toward scalable computing resources. Leverage cloud partners and campus computing environments to understand the economics and scalability around your computing resources. The computational requirements and costs for tools based on institutional LLMs and other computationally intensive AI tools must be understood.
  • Ensure layered support services. To help prepare for Dx 2.0, you should build technology support services both in the central IT organization and in the functional departments. Layered support services staff who understand how the different pieces fit together, know the flow of the data, and assist with business process analysis and re-engineering will be important to the success of these transformation efforts.
  • Keep abreast of corporate partners' roadmaps. Many companies have announced ways in which they are planning to incorporate AI into their products, services, and platforms. Technology and business owners should work together so that campus leaders can better understand those directions and the potential impacts to the institution and users.

Culture

As is often the case with transformation efforts, achieving shifts in culture can be one of the hardest aspects of the work. Due to the rapid developments in the AI field and given the mysterious nature of many AI tools, moving from a culture of skepticism and hesitation to one of understanding and embracing can be difficult. Some of the following techniques can help:

  • Be intentional. Changing any type of culture takes purposeful effort. Write out a communications and interaction schedule and strategy. Create deliberate opportunities for conversations about AI and various use cases that will resonate with each audience.
  • Work to establish a culture of understanding about AI throughout the institution. "AI" is a broad category of technology tools that go beyond ChatGPT and other LLMs, including tools built into existing applications. Helping people understand what AI is and what it encompasses is critical. Encourage faculty and staff to experiment with AI and to hear from one another about capabilities, use cases, concerns, and limitations.
  • Foster trust, transparency, understanding and partnership. Establish communication pathways, campus partnerships, and opportunities to build shared understanding. These will go a long way to creating a culture that will be open to change and transformation. Be transparent about limitations and current challenges.
  • Help the campus community understand ethical, legal, privacy, and intellectual property issues around AI. Partner with faculty, institutional counsel, and other stakeholders to develop policies around the use of AI. Set up working groups and tap into existing governance structures to explore these issues and develop frameworks for the institution. This not only will help your institution evolve its policies but also will bring people together and give attention to these issues while socializing them across the organization.
  • Seek out early adopters and champions, and find ways to showcase their work and how they are using AI. Be sure to include the good, the bad, and the ugly. Full transparency is important and helps others understand use cases and risks. Support the work of early adopters, and ask them to help in engaging with others.
  • Create opportunities to explore AI use cases that align with your institution's core values. The use of AI tools may not be appropriate in all situations, and each use case needs to align with the institutional philosophy around student engagement and services. For example, does using AI tools to make admission decisions or to reach out to students at academic risk fit with the overall culture and approach for how your institution serves its students?

Workforce

We are just starting to understand how AI will influence the workforce and the jobs that will be needed (or no longer needed) as a result of Dx 2.0 efforts. For now, these steps will help your IT and campus teams prepare:

  • Build an understanding of how AI may affect the workforce. Have people across the institution work with the AI tools currently available to understand how they function, develop competencies to leverage them, understand their limitations, and be intentional about including the tools in their workflows. Hold "AI Exploration Sessions" that bring together stakeholders and technology in a lab environment. Ask people to do guided experimentation with various AI tools and approaches, then debrief participants on what they learned, and discuss how they can apply the tools to their work.
  • Think about how jobs and tasks might change. Start looking at every position within a department or organization, and map out ways that the work may change in one, two, and five years as tools evolve. For each position, ask three questions: What tasks might change? How might they change? What new or meaningful work might the person in the position be able to do as a result of the change? For example, within the next twelve months, admissions counselors might be able to use AI for initial reviewing and scoring of applications (the "what"), their work will change in that they will be spending less time reading applications (the "how"), and they will be able to do more targeted outreach and follow-up that they did not have the time for in the past (the "result").
  • Consider the impact on your organizational structure. If AI can take care of some of the work, how will job functions in your organization change? Will you still need the roles and positions you have today, or can some be repurposed? What different roles will emerge? Again, think in the one-year, two-year, and five-year timeframes. As AI evolves and improves, what might be the effect on your organization? What training, advancement, roles, and career ladder adjustments will be required?
  • Provide opportunities to enhance skillsets. What skills will your workforce need in order to be able to leverage AI? Some employees will need training in the specific AI tools embedded in applications; others may need to become versed in prompt development for LLMs such as ChatGPT, Copilot, and Bard.

Staff should be able to see AI tools as offering opportunities to shift the focus away from rote or semi-rote tasks to higher-order work and enabling new ways to improve customer service, focus on more strategic efforts, and add value in innovative ways. These advances do not necessarily mean that employees' jobs are going away but, rather, that AI will enhance how they do their work.

The Impact of Digital Transformation 2.0

Dx 2.0 efforts, leveraging the power of AI and shifts in technology, culture, and workforce, have the potential to impact all areas of higher education. Some examples include the following:

  • The Student Experience. Developments in AI will provide for a more personalized and connected student experience. For example, over the past few years, a number of institutions have deployed chatbots or other similar services to help students find answers to common questions and take care of routine tasks. With further advancements in AI, these services are becoming more encompassing and capable, ultimately streamlining and strengthening students' experiences in navigating the institution and connecting with others.
  • Teaching and Learning. Personalized learning, AI tutors, customized lesson plans, virtual role-playing, ideation tools, new visualization tools, and other AI-powered developments create opportunities to enhance the teaching and learning experiences for faculty and students both in and out of the classroom.
  • Campus Operations. AI enables operating efficiencies and frees up staff to focus on delivering higher-level service and higher-order tasks. For example, AI will increasingly be used to read entrance applications and make more consistent preliminary assessments more quickly than is possible today. AI functions that are built into applications can offer insights, allowing staff to dive deeper into their work and services. Tasks that used to require a lot of manual manipulation of data or information will be done more efficiently and effectively.
  • Experiential Learning. The ability of AI to build realistic simulations and custom scenarios for students will open up opportunities for immersive and experiential learning across all disciplines.
  • Research. The development of LLMs and generative AI tools is leading to new ways to analyze huge datasets, unstructured data, and natural language information to provide insights and exploration paths that were not available before or were out of reach for many.

AI at Ithaca College

At Ithaca College, we have been exploring how these new AI tools can provide insights and services in ways that were not available to us before.

We've been using machine learning to build a web-based tool that helps first-year students connect with other first-year students who have similar interests. The tool leverages various pieces of information that we've learned about incoming students to suggest possible students with whom they may want to connect. While not super-sophisticated, the tool has been well received, at least as a way to get students over that initial hesitance to make some connections.

We also have developed a proof-of-concept using a private AI tool to provide insights about a student's academic record (program of study, courses taken, grades received, degree requirements satisfied, and future course schedules). Our initial use case was to have an advisor use this service to quickly get a summary of any performance or retention concerns. But we've also found that if we ask the tool to suggest possible career paths based on the student's academic work, it provides very astute observations and suggestions for the student to explore—a benefit that we didn't expect. We foresee making this tool available directly to students for them to be able to explore career options themselves and receive direct feedback on their academic performance.

Another pilot project is giving a different AI service access to a wealth of Ithaca College public information, such as our entire course catalog and program definitions, admission policies and timelines, financial aid specifications, and public rankings, as well as details about the town of Ithaca. Our goal is that prospective students will be able to use this tool to ask questions about Ithaca College and the town of Ithaca and receive specific and relevant information. Our initial testing has been very positive.

These new tools represent Dx 2.0 examples that will transform the experiences of our students and the information and insights to which they have access. To make these transformations happen requires intentional shifts in technology, culture, and workforce.

Moving Dx 2.0 Initiatives Forward

Many Dx 2.0 efforts will leverage AI developments to enhance the transformational impact of technologies already in place. Other efforts will be part of new months- or years-long campus-wide efforts. The following are a few suggestions to get initiatives started:

  • Create opportunities for conversations to occur across campus. Technology leaders can help start the discussions, but the various stakeholders need to take an active role in understanding the potential influence of AI on their areas. Go on a roadshow to visit business and academic units to provide a baseline understanding of AI and then highlight some use cases in those areas. Encourage further exploration, and establish partnerships on specific next steps.
  • Work with institutional stakeholders and governing bodies on AI-related policies and guidelines. What's "acceptable use" of AI by students and faculty? How will AI be used to inform or make institutional decisions? Privacy, intellectual property ownership and copyright, bias, access to training data, and other related issues need to be discussed and thought through.
  • Acknowledge the limitations, hype, and ethical concerns. AI is not a magic solution. Being upfront about its limitations and problems is important to establishing trust and realistic expectations. But AI is also not going away, and those who are able to embrace aspects of Dx 2.0 will have an advantage in the long term.
  • Focus on three timeframes: one, two and five years out. We're still early in the Dx 2.0 journey. AI capabilities will get better and become more deeply embedded in our solutions and processes over time. Developing an understanding of where things are heading while acknowledging the limitations of today's tools will help you develop a strategy for tomorrow.

Digital Transformation 2.0 builds on the basic tenants of the current Dx model and leverages the rapidly developing AI advances to accelerate transformations and magnify their impact. Maximizing these developments requires a renewed focus on technology, culture, and workforce shifts. Using AI, Dx 2.0 efforts can create positive transformations for our campuses and throughout higher education.

Notes

  1. Report from the 2018 EDUCAUSE Task Force on Digital Transformation, EDUCAUSE, November 2018. Jump back to footnote 1 in the text.
  2. Dave Weil, "Putting All Three Components of Digital Transformation to Work on Campus," EDUCAUSE Review, May 18, 2021. Jump back to footnote 2 in the text.

David Weil is Vice President, Information Technology & Analytics, at Ithaca College.

© 2024 David Weil. The content of this work is licensed under a Creative Commons BY-NC-ND 4.0 International License.