The volume of AI products entering organizations can create a false sense that technology selection is the primary decision.
It isn't.
Before approving another AI investment, leadership should be able to answer six questions.
1. What business outcome are we trying to improve?
If the use case cannot be connected to an identifiable business problem, operational opportunity or strategic priority, the technology itself is unlikely to create clarity.
2. Who owns the outcome?
Technology may enable the initiative, but the business owner should remain accountable for what changes and whether value is realized.
3. What information will the AI access?
Leadership should understand the data involved, its sensitivity, where it will be processed and what obligations apply to it.
4. What level of human oversight is required?
Different use cases carry different consequences. An AI system drafting internal meeting notes should not necessarily be governed in the same way as one influencing consequential decisions.
5. What will change in the workflow?
If the process remains exactly the same, adding AI may simply add another tool rather than improving the work.
6. How will we know whether it worked?
Define the measure before scaling the investment.
The important board or executive question is therefore not simply:
“Do we have an AI strategy?”
It is:
“Can management explain where AI will create value, how the organization will govern it, and what must change for that value to be realized?”
— EXECUTIVE ADVISORY INSIGHT
Embedding AI into Enterprise Operations: The CNO Approach
2026 NCSBN IT/Operations Conference
Presented by Floyd Garvey, Director, Information Technology, College of Nurses of Ontario.
A practical look at embedding AI into an operating organization and addressing adoption as more than a technology deployment.
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From the Field
Turn Insight into Action
The purpose of these perspectives is not to provide another stream of AI commentary. It is to help leadership teams ask better questions and make better decisions.
If your organization is trying to determine where AI creates value, how to govern adoption, whether your technology environment is ready or how to move beyond isolated experimentation, let's start with the decisions currently in front of you.