AI technology can genuinely change how an OR — and, even, how a hospital — runs. I've seen it happen. But, over several implementations at Houston Methodist, UC Davis Health, and Virginia Mason, I've also learned that even the right tool, deployed well, can go unused if the weeks leading up to go-live are treated as an afterthought. The contract gets signed, the technology gets deployed, and an all-staff email goes out… A few weeks later, I'm looking at low adoption numbers and a tool that isn't being used the way we hoped.
The lesson I keep coming back to is that the mistake usually isn't the technology. It's treating AI like a software rollout rather than what it actually is: a change in how people work, how they're measured, and whether they trust the organization introducing it.
My experience is applicable to anyone overseeing perioperative change management, whether that’s the chief nursing officer, vice president of periop services, or OR director. Successful adoption doesn’t start at go-live. It's built in the weeks before, in every conversation, decision, and unanswered question that shapes whether the care team sees the technology as a tool built for them or as a decision made without them.
The clock starts at signing
The behavior change underlying positive outcomes doesn't begin when the software is turned on. It begins the moment the contract is signed.
That window is one of the most underused opportunities you have as a CNO. Used well, it's a chance to introduce the technology, surface concerns, and build trust before anyone interacts with the tool. Missed, it's often why, months later, adoption has stalled.
The work here isn't teaching staff how to use the technology. It's helping them understand why the organization is investing in it and what it means for their day-to-day work.
The difference between resistance and momentum is credibility
One of the most common mistakes in perioperative AI implementation is too narrowly defining stakeholder engagement. Leadership gets briefed, department heads get a demo, and the people who spend every day in the OR find out when cameras start appearing in their workspace.
Successful adoption often hinges on the people nurses and others naturally look to for guidance. Their opinions carry weight because they've earned credibility over time. It is important to identify these people early and bring them into the room before anyone else because winning them over can make the difference between resistance and momentum.
Stakeholder engagement should reach beyond the nurses that will populate the power-user contingent of adopters to include beneficiaries, as well: surgeons, anesthesiologists, perioperative leaders, as well as EVS and support staff. Each group has different concerns, and addressing them directly — rather than leading with broad and ill-defined goals like efficiency — is one of the fastest ways to build credibility.
Learn how Houston Methodist used its block schedule to 3x one surgeon's daily cases.
Read the case study
Leadership has to own the message
Staff want to hear why AI matters from leaders they already know, not from a vendor.
The vendor's role is to demonstrate the technology and explain the workflow. The larger conversation — why the organization is investing and what it means for staff — is yours to own. The role of the chief nursing officer matters most for anchoring the introduction of AI tooling in shared values like avoiding late-ending days, avoiding multitasking, ensuring safety and care quality, and improving patient access.
Where past implementations may have been led by a technology expert and been oversold or underdelivered, leading with trusted clinical leadership and avoiding vendor-speak can overcome skepticism and create an entirely different starting point. The CNO's role isn't just driving adoption. It's creating a shared understanding across the team and stakeholders of why the change matters and how it benefits everyone.
Trust requires honest answers
One of the biggest unspoken questions staff have is about labor: Is this going to create more work for me? It's a fair concern; healthcare has a long history of tools that promised efficiency and delivered administrative burden instead.
The value proposition has to be translated into both clinical and human terms. Don’t just say, "This improves OR utilization." Instead, try saying, "You won't have to walk down the hall to check if a room is ready because you'll already know." Although, as the CNO, you may think, "This tool reduces documentation burden," speak to the team’s actual experience and pain points: "Remember that audit that used to take half a shift? It’ll now take a fraction of the time. Get it done, then go home on time."
The same transparency should apply to patient safety. Teams need to understand what the technology can do, its limitations, how it fits into existing clinical workflows, and where human judgment remains essential. Building trust means making it clear that new technology is there to support clinicians and should not introduce unnecessary risk or ask them to compromise the standards of care they’re responsible for upholding.
Research shows that nurses’ fears about increased workload or about AI eventually replacing them are among the biggest barriers to technology adoption. Those fears, along with concerns about patient safety, shouldn't be dismissed. Rather, they should be answered directly: what gets easier, what stays the same, and how the gains benefit both patients and staff.
Governance sustains trust
Before anyone interacts with a new AI tool, you should have clear answers to four questions:
- What is the tool measuring?
- How will that data be used?
- Who owns its ongoing management?
- How will success be measured?
Staff need to know upfront that the data will not be used against them. When that isn't made clear from the start, ambiguity tends to harden into distrust that persists long after go-live. Equally important is establishing clear ownership of the tool before implementation begins. Without a named person or team accountable for ongoing management, adoption tends to quietly erode even when the launch goes well.
People-focused measures should be defined before implementation — not just operational metrics like turnover times, but also workforce measures such as staff satisfaction and confidence in using the technology. Those baselines don't just guide improvement; they make it possible to tell a fuller story about long-term ROI.
The bottom line
The biggest barrier to AI adoption in the OR isn't the technology. It's the culture around it.
The CNO is uniquely positioned to lead the transformation that comes from setting the right values and benefits early and often. That influence has less to do with owning the technology and more to do with integrating change with the culture around it. The organizations that succeed start well before go-live by engaging trusted frontline voices, answering hard questions honestly, establishing clear governance, and ensuring staff hear about the technology from credible leaders they know.
Learn how Houston Methodist used its block schedule to 3x one surgeon's daily cases.
Read the case study