Artificial intelligence has become part of nearly every conversation in higher education. It touches teaching and learning, cybersecurity, administrative operations, software development and student services. This doesn’t mean every project needs AI. Rather, institutional leaders should consider how technology can help accomplish work that has become increasingly difficult given limited resources.
At Pima Community College (Arizona), we’ve found that the most successful AI initiatives didn’t begin with a search for the latest technology but instead leaned into the institutional challenges we already faced. Here are a few lessons we’ve learned along the way.
Start with the problem
One of our first successful AI projects grew out of a conversation about disaster recovery — not artificial intelligence. During a discussion with our learning management system team, we realized we had a well-designed process for auditing online courses, but we simply didn’t have the staff capacity to review all of them. With nearly 1,000 courses in Brightspace D2L, our LMS, only a fraction could realistically be audited each year.
That challenge became our first “aha” moment.
We’re now building our first agentic workflow to evaluate course content against established standards and generate reports for faculty. AI will help us scale a process that already works so our instructional designers and faculty can focus on improving courses instead of manually reviewing hundreds of them.
The experience reinforced an important lesson: start with a problem worth solving and then look for the technology.
Help people become comfortable with AI
Technology adoption depends as much on culture as it does on software. That’s why we created an AI Community of Practice where faculty and staff could experiment with AI, ask questions and learn from one another. Those conversations changed the tone across campus. Instead of focusing primarily on concerns about cheating, faculty began discussing how AI could support teaching while also encouraging us to rethink assessment.
Assignments that rely heavily on recalling information are becoming less effective measures of learning. As AI evolves, colleges have an opportunity to move students toward higher levels of analysis, evaluation and application. Those conversations will continue for years, but they are conversations worth having.
Use AI to expand capacity
Many colleges pour heavy investments in institutional data, yet relatively few employees leverage it effectively because accessing that information can be difficult.
As we migrate our data warehouse to Snowflake, we’re piloting conversational analytics that allow employees to ask questions in natural language instead of navigating reports and dashboards. Within IT, we’re also using AI to accelerate software development, helping our team deliver solutions more efficiently.
Neither initiative replaces people. Both allow people to spend more time solving problems and less time completing repetitive tasks.
Don’t forget the student experience
Community colleges serve an incredibly diverse student population. Some learners come directly from high school. Others are returning adults balancing work and family responsibilities. At Pima, we also serve refugees beginning new lives in the United States, many of whom arrive with little more than a mobile phone.
This reality shaped how we think about enhancing digital engagement to help students navigate college more confidently.
Students expect information to be easy to find, whether they’re accessing it through a portal, a chatbot or another digital channel. Our campus experience platform powered by Pathify has become an important part of creating that digital front door, and we’re actively working to extend trusted institutional knowledge from the platform into our AI chatbot so students can receive consistent answers regardless of where they begin.
Lead the conversation
AI is rapidly changing higher education, but community colleges have always adapted to meet evolving student and workforce needs.
I believe the CIO’s role is changing as well. Technology leaders can’t simply wait for requests to arrive. We have an opportunity to bring people together, help institutions distinguish genuine innovation from marketing hype and guide conversations about where AI can create meaningful value.
For institutional leaders just beginning their AI journey: start small, solve real problems and build from there. One successful project creates momentum, confidence and a clearer understanding of where AI belongs. Those lessons will ultimately matter far more than adopting the newest tool.
