Deploying the technology may be the easiest part. Changing how the organization works is the transformation.
Many organizations are approaching AI through a familiar technology playbook: select a platform, issue licences, provide training and encourage employees to experiment.
Those steps may be useful. They are not, by themselves, an AI adoption strategy.
An organization can have widespread access to AI while still lacking clarity about where it creates value, which use cases should receive investment, how risk should be governed, what workflows need to change and how success should be measured.
The distinction matters because AI reaches well beyond the technology function.
It affects how people work.
It affects how decisions are made.
It affects how information is created, accessed and trusted.
It affects accountability.
It affects risk.
And, increasingly, it affects how organizations design work itself.
That makes AI adoption a leadership and organizational transformation challenge enabled by technology.
Start With the Business, Not the Tool
The first question should not be:
“Which AI platform should we buy?”
It should be:
“Where is there an important business problem or opportunity that AI may help us address?”
That shift sounds simple, but it changes the entire conversation.
Instead of collecting interesting AI use cases, leadership can evaluate opportunities against business priorities, operational pain points, customer or stakeholder outcomes, risk and measurable value.
Prioritize Ruthlessly
Organizations do not need fifty AI pilots.
They need a small number of well-chosen opportunities that can teach the organization how to adopt AI responsibly and demonstrate whether meaningful value exists.
A useful use case should have a clear owner, a defined problem, appropriate data, a realistic implementation path and an outcome that can be measured.
Governance Must Enable Adoption
Governance is often treated as something that arrives after innovation.
That is backwards.
Organizations need enough governance early to define acceptable use, accountability, decision rights, human oversight, risk thresholds and escalation.
Good governance should make responsible adoption easier — not create a separate bureaucracy that nobody knows how to navigate.
Adoption Requires Workflow Change
Giving an employee an AI tool does not automatically change the way work gets done.
Leaders need to ask:
What part of the workflow should change?
What should remain human?
Where should AI assist rather than decide?
What new skills are required?
How will quality be checked?
What happens to the time or capacity that AI creates?
Without those questions, organizations may generate individual productivity improvements while leaving the underlying operating model largely unchanged.
Your Technology Foundation Still Matters
AI cannot compensate indefinitely for fragmented data, weak integration, inconsistent identity controls, legacy platforms or unclear architecture.
The more deeply AI becomes embedded in business processes, the more important the underlying technology environment becomes.
Technology modernization and AI adoption therefore cannot be treated as completely separate strategies.
Measure More Than Usage
Login counts and licence activation tell you whether people have access.
They do not tell you whether the organization is creating value.
AI initiatives should eventually be measured against outcomes such as time saved, quality improved, risk reduced, capacity released, service improved, decisions accelerated or revenue enabled — depending on the use case.
The objective is not maximum AI usage.
The objective is better organizational performance.
A Practical Adoption Cycle
At GarveyTech, I think about enterprise AI adoption as a continuous six-stage discipline:
UNDERSTAND → PRIORITIZE → GOVERN → ADOPT → SCALE → MEASURE
Understand the organization before prescribing technology.
Prioritize the few opportunities worth pursuing.
Govern them proportionately.
Build adoption into the workflow.
Scale what actually works.
Measure whether the organization is receiving meaningful value.
Then learn and repeat.
That is how AI moves from experimentation to organizational capability.
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.
View the NCSBN Conference Listing →
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.