Align Technology with Institutional Priorities
When budgets tighten, technology roadmaps become even more important.
Daryl Ford, CIO at Roger Williams University, says modernization decisions begin with the university’s three-to-five-year strategic plan rather than with individual technology requests.
“Security is always number one,” Ford says. “If our systems get infiltrated or shut down, then we’re dead in the water.”
After security comes core infrastructure, including networking and switching, followed by classroom technology. Those investments are driven through capital planning, although emerging institutional priorities can quickly reshape the roadmap.
Ford adds that every major investment must also connect directly to institutional objectives.
“My pitch is usually about institutional value,” he says. “How is this fiscally responsible, and how does it support what we’re trying to do, whether that’s operational efficiency or student success?”
Sean Burns, corporate researcher at EDUCAUSE, says constrained budgets and staffing shortages are major barriers to data modernization and management.
“Addressing these challenges requires a coordinated approach that should be driven by a business needs assessment, resulting in an effort that combines modern infrastructure, strong governance and a commitment to fostering a data-informed culture across the institution,” he explains.
DISCOVER: A panel of experts discusses how to make every dollar count in higher ed.
Fix the Data Before Expanding AI
Many institutions are eager to deploy AI across advising, admissions and administrative operations. Ed Skoudis, president of the SANS Technology Institute, says that often puts the technology ahead of the foundation it depends on.
“The mistake I see people making is they want to modernize, and they say AI is the hot, big thing, so they sprinkle AI all over the place,” Skoudis says.
Instead, he recommends consolidating fragmented institutional data into centralized, queryable platforms before launching major AI initiatives.
“Pulling that data together before you embark on grand strategies of AI is the start of your AI strategy,” he says.
Skoudis also advises CIOs to modernize the most fragile systems first — particularly, outdated databases and unsupported platforms that create operational and security risks.
LEARN MORE: Is your data governance AI-ready?
“Find the most brittle data repositories you have,” he says. “Look for systems that have experienced outages or are running on end-of-life platforms.”
That strategy improves resiliency, simplifies cloud migration and creates cleaner data that AI systems can use.
Modernize Multiple Initiatives at Once
Although modernization requires sequencing, Ford says CIOs rarely have the luxury of addressing one project at a time.
“All these things have to happen at once,” he says. “You have to be an expert in parallel processing as a CIO.”
Roger Williams University is simultaneously planning a migration from a heavily customized on-premises ERP system to a cloud platform while expanding AI literacy across campus.
The ERP transition is necessary because future capabilities are increasingly cloud-based, but the migration will require significant funding, change management and technical effort.
READ MORE: Old Dominion University incubator promotes AI literacy.
To stretch limited budgets, Ford says the university actively pursues grant opportunities alongside institutional funding. In addition, the university joined Google’s AI for Education Accelerator, giving students access to AI tools, workforce training and certifications without requiring major new spending.
The university incorporated AI literacy into its learning management system after institutional leadership established AI competency as a graduation priority.
Ford says CIOs must also balance technology priorities against staffing realities, noting that as technology portfolios continue expanding, CIOs increasingly find themselves balancing infrastructure, cybersecurity, budgeting and strategic planning simultaneously.
Build Governance Into Every AI Initiative
For Ford, successful AI adoption begins with what he calls “guidance and guardrails.”
Roger Williams has created a centralized AI hub that identifies approved enterprise AI platforms, explains which services include institutional protections and provides training resources for faculty and staff.
UP NEXT: AI governance can help streamline responsible adoption.
“We consolidated all our resources into a single hub,” Ford says. “We can educate the community to make sure they’re using the ones that have guardrails.”
The university also relies on data governance councils to oversee how institutional information is stored, shared and prepared before being entered into AI systems.
“We’ve tried to clean up our data to make sure we’re feeding these models with clean, good data,” Ford says. “Garbage in, garbage out still holds true.”
Skoudis says governance should also establish clear policies governing student privacy, AI transparency, bias mitigation and where human oversight remains mandatory.
“Determine what requires human review and where humans must be involved,” Skoudis says. “Make it explicit.”
