An AI Adoption Imperative: Centralized Sources of Governed Truth
A Q&A with Cody Irwin
As the barriers to entry in AI dip lower and analytics are offered as self-service, information designers contemplate the actions to making AI adoption prosper with centralized, governed data. Here, we talk to Cody Irwin, Domo’s AI adoption director, to request for his insights about adoption strategies for enterprise teams who intend to construct an information foundation that will move the institution from AI experimentation to real-world execution.
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img height =”429″alt =”robots organizing stacks of papers “width =”644″src =”https://campustechnology.com/-/media/EDU/CampusTechnology/2026/05/20260511centralizedgoverneddata.jpg”/ > Mary Grush: We’ve found out about the perils of siloed information for years. How does this change now, with AI?
Cody Irwin: Data gain access to and governance hold some of the biggest hurdles to opening the guaranteed effectiveness behind generative AI. Leaders have actually been informed for years that building an information warehouse, or lake, is vital to exposure and choice producing analytics. That requirement has now become a vital. The barrier to entry is no longer “Do you understand SQL, data science, and visualization methods?” It’s merely, “Do you know words?” To empower the enterprise, data leaders need to develop central sources of governed truth.
Grush: Is AI well understood in the context of college information governance? Exists a “trust deficit” to conquer?
Irwin: The trust deficit exists all over however is especially intense in college. Education organizations count on data to manage admissions, financial aid, research, publications, accreditations, fund raising, compliance, and operations. A misstatement of information is frequently public and can have severe implications on institutional trustworthiness. Considering that data gain access to and analytics are becoming ever more self-service, information leaders have a burden of duty to create and handle centralized, accredited information.
Grush: I know these are complicated concerns, and our time is brief here, but what are some of the qualities of information models that develop leaders should be working towards?
Irwin: We’ve seen worth in data modeling that has AI– and versatility– in mind. Specifically, institutions need to consider embracing a “medallion architecture” where “gold datasets” are exposed for usage by decision makers. Also, AI flourishes on context, which needs more than just making the data available– the data models ought to surface semantics that supply organizational context that AI can take advantage of to provide more meaningful and precise responses.
Grush: Could you offer an example of how designers can work effectively with information productivity platforms?
Irwin: Step one for a lot of data designers is to make the data centrally readily available through a governed user interface. They must supply the ability to recover or incorporate information from almost any source environment. That centralization must enable the execution of policy, security, logging, and accreditation. The centralization needs to be empowering and not limiting for choice makers. If it’s hard, people tend to find a method around it. My company, Domo, offers controlled interfaces on top of that centralized data fabric for self-service analytics and easy AI interactions.
Grush: How can people be strong design leaders in a moving AI culture with massive information requirements?
Irwin: The data structure is important. The faster designers can get a foundation in place, the more quickly their internal clients will feel empowered. We advise not letting perfection be the enemy of progress. Design leaders should prioritize what they feel would be the most impactful and move rapidly to get that released.
[Editor’s note: Image by AI. Microsoft Image Developer by Designer.]
About the Author
Mary Grush is Editor and Conference Program Director, School Innovation.