Your campus and AI: 6 Questions to Prepare for Success
By Keith P. O’Brien
8 minutes estimated time to read
“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”
— Andrew Ng, co-founder of Coursera and Adjunct Professor of Computer Science, Stanford University
About a century ago, we replaced steam powered machines with those using electricity, transforming everything from transportation to healthcare. Equating Artificial Intelligence (AI) with electricity is apt given its impact on industry and markets. I’m not trying to up the ante on AI’s importance but I was taken by the assessment of Google CEO, Sundar Pichai, “AI is one of the most important things humanity is working on. It’s more profound than electricity or fire.” The reference to fire resonated because I’d read where a professor used fire as an analogy with AI: fire became life changing for humans not for our understanding of its chemical properties but because humans discovered remarkable ways to use fire. Leaders across all industries including higher ed have to determine how to apply AI in the most consequential ways without getting distracted by technological minutiae.
AI presents colleges and universities with the opportunity to deliver personalized experiences across the student lifecycle. But many administrators are wary of the hype and perceived complexity of AI. Such concerns are overstated—only AI can translate the terabytes of student data on campuses into experiences that students value and benefit from. The time to act is now; the rewards of adopting AI far outweigh the risks from procrastination given today’s enrollment and tuition revenue challenges. So how does a college get started?
Where and How Should We Use AI? Six Questions to Get Started
AI can do many things on campus from automating routine administrative tasks to identifying the mindsets of students that drive their behaviors. Use the following six questions to shape how AI is deployed strategically and operationally on campus:
- What are we trying to do here? The starting point for any AI project is pinpointing the business problem the technology can address. You must consider the university’s strategic priorities, the nature and urgency of the problems, and the organizational and cultural implications. Problem identification and prioritization are critical because deployment of AI solutions often raises unrealistic expectations. TIP: Campus leaders can start by addressing these five basic decisions: forge consensus on the problem, identify the project owner, pinpoint the operations and campus units involved, clarify the role of the IT department upfront, and define the performance metrics.
- Do we have leadership buy-in? The AI project needs sponsors who clearly and publicly commit to the initiative. The project sponsor(s) must have the authority to allocate resources to the project and the backing of campus leaders to drive implementation across campus units. TIP: Communicate regularly to foster support and dispel misinformation. At a minimum, communicate the project’s strategic importance, operational impact (faster, smarter, cheaper), and benefits for students and the institution.
- What’s the timeframe for expected returns? Some administrators expect substantial returns quickly given the aura around AI. The timeline and ROI are a function of the problem being tackled first and foremost. For example, deploying AI to help more students complete FAFSA verification targets a well-defined issue, a set timeframe and a clear performance metric. The campus units involved and the performance metrics are critical factors too. TIP: Groundbreaking technologies don’t fit neatly with existing ways of operating. Remember the advent of the Internet? At first it was very disruptive but now is a competitive necessity. Don’t let false precision over timing or outcomes let your institution get blindsided: early adopters of AI gain a market advantage while skeptics must play catch up.
- Have we addressed data readiness and governance issues? AI generates vast new troves of information. Data readiness means that data can be connected across systems and campus units throughout the institution. Matching data using student id represents one such opportunity. Admissions systems don’t employ a student id and so alternate match keys are required. Policy readiness requires that data governance, identity resolution, privacy and other policies authorize the university to use specific data and systems for specific purposes, including informing student communications.
- How receptive is the Institution to change? Every AI project operates in the context of an institution’s unique culture, governance models, and campus unit processes. AI project sponsors must gauge the institution’s cultural receptivity to change, and plan accordingly. TIP: Here’s two criteria help assess campus receptivity:
- Has a data-driven culture—the ability to test new data driven initiatives in a controlled environment indicates organizational readiness for AI. Taking a data-driven approach helps test, validate and implement process improvements. Data-driven Institutions tend to generate greater returns from AI.
- Embraces continuous improvement—a track record of embracing change is a key marker of process readiness. Process driven self-evaluation indicates leadership has fostered a “growth-mindset” campus climate that embraces new technologies and decision-making approaches.
- How do we educate the campus on AI? While all technology projects and integrations pose challenges on campus, AI-led projects can generate unease among some staff: a common misconception is that AI serves to automate work and erode job security. However, as one university CTO observed about the reality of AI: “Nothing will ever replace the human touch aspect of an educational professional interacting directly with a student.” Higher ed has many federal- and state-mandated reporting processes that can be automated by AI, freeing up campus staff to focus on the more value-adding aspects of their roles. Project leaders must communicate proactively to set and manage expectations by sharing the goals, expected benefits, and the exact role the AI performs across campus.
AI can streamline red tape, reduce or eliminate data siloes, and improve staff capacity to engage students one-on-one. It turns disparate student data sets into unparalleled insight on individual behaviors and needs. Just as it’s impossible to imagine a campus without electricity, AI has to be part of how colleges and universities engage students. In my next blog, I’ll look at how institutions can approach working with vendors on AI-led initiatives.
This blog is based on our free eBook about AI’s game-changing potential, Artificial Intelligence and Higher Ed: Cutting Through the Noise
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