What are health system leaders getting wrong about AI and nursing?

I think the biggest issue is that leaders are still treating this as a technology implementation, when it really shows up as a leadership moment. If you look at other technologies that have come into healthcare over the past couple of decades, most of them were layered on top of clinical work and focused on documentation or administrative burden. This shift feels different This is the first time we are working with a technology that has the potential to influence clinical judgment, and I think that changes how people experience their roles in a fundamental way.

One of the things I often try to emphasize is that AI is probabilistic rather than deterministic. It is offering suggestions, not answers. In a busy clinical environment, especially when there is sustained pressure, there is a real risk that people start to treat those suggestions like answers. That is why AI integration becomes more of a leadership question than anything else.

How should leaders think about building trust with nurses before introducing AI?

Trust has to be built before the technology ever shows up. I think one of the patterns you see is that organizations select a technology, make decisions about how they want to use it, and then introduce it to the workforce. At that point, people are reacting to something that already feels decided. A more effective approach is to involve nurses before the technology is selected: Start with the problem, identify where there is friction in the workflow, then bring them into that evaluation process.

There is also a broader perception issue that leaders must address directly. People are not coming into this conversation without context, and there is already concern that AI may replace roles or reduce headcount. Leaders need to reinforce that this technology is an opportunity to create more capacity and allow nurses to spend more time on clinical judgment and patient care.

What does meaningful nurse involvement look like during AI implementation?

Meaningful involvement has to be ongoing. Organizations often gather input and then quickly move to implementation, but with AI, that approach is not enough. The technology continues to evolve, and the way it is used in practice plays a huge role in how effective it becomes. There need to be continuous feedback loops between the people using the technology and the people responsible for implementing it, and that feedback needs to be acted on in visible ways.

It’s also important for nurse leaders to be actively and visibly involved in the process. When clinicians see that people who understand the work are shaping decisions, it builds confidence. In addition, every unit has individuals who influence how others respond to change. Bringing those people in early can help surface issues quickly and build momentum for adoption. Without that level of transparency and involvement, the technology can start to feel imposed rather than something staff helped shape.

What capabilities do leaders need to successfully guide AI adoption?

One of the most important leadership skills is the ability to think in an integrated way, because AI doesn’t affect just one workflow or function. It has implications across staffing, patient experience, and clinical decision-making, and leaders need to understand how those pieces connect instead of focusing on a single part of the implementation. There must also be strong alignment across groups like clinical leadership, IT, operations, and finance. Each of those teams touches a different part of the process, and none of them can effectively drive this on their own.

Additionally, leaders need to focus on building an adaptive workforce that can respond to ongoing change. That includes creating psychological safety so staff feel comfortable raising concerns and questioning how the technology is being used, and reinforcing that clinical judgment remains central — the technology is just there to support it.

Where are the most promising use cases for AI in nursing today?

Predictive analytics is one promising case. If a system can identify that a patient is declining earlier and give clinicians more time to intervene, that can make a meaningful difference in outcomes and reduce some of nurses’ cognitive burden when managing complex patients. Staffing is another area that gets a lot of attention, since matching nurse experience with patient acuity has always been challenging. AI can help uncover patterns to support those decisions.

These use cases also highlight where things can go wrong. There are situations where algorithms don’t perform as expected, which can lead to mismatches and frustration. Organizations need to continuously monitor how the technology is performing and how clinicians are interacting with it. Administrative applications like documentation support can also reduce burden.

What metrics should organizations use to measure the success of AI?

Patient outcomes are one metric, including improvements in care quality, safety, and overall experience. There are also the technology metrics, such as whether the system performs as expected and whether clinicians are actually using it.

Workforce opportunities are another metric, and I don't think they get enough attention. Retention, attrition, and engagement can provide early signals about how the technology is being received, especially if it is creating additional stress or uncertainty. Leaders should also consider whether these tools allow nurses to spend more time on higher-value work or open new pathways for growth. In practice, workforce signals often provide the earliest indication of whether adoption is happening in a meaningful way, even though organizations tend to focus more heavily on the technical side.

Looking ahead, what will determine whether AI succeeds in nursing workflows?

I think success ultimately comes down to how well organizations manage the human side of the transition. The technology will continue to evolve, and many organizations will have access to similar tools. The differentiator will be how those tools are integrated into workflows and the broader culture.

That requires building trust early, involving nurses in decision-making, and maintaining transparency throughout implementation. It also requires consistent monitoring and a willingness to adjust based on what is happening in practice. The workforce is already under significant strain, which makes leadership approach even more crtical. Organizations that stay focused on the workforce experience and treat this as a leadership challenge are much more likely to see sustained value over time.

AMN Healthcare is the leader and innovator in total talent solutions for healthcare, bringing together the people, processes and technology to deliver better care. Through a steadfast partnership approach, we solve the most pressing workforce challenges to enable better clinical outcomes and access to care. In 2025 our healthcare professionals reached more than 13 million patients at more than 2,300 healthcare systems, including 93 percent of the top healthcare systems nationwide. We provide a comprehensive network of quality healthcare professionals and deliver a fully integrated and customizable suite of workforce technologies.

Learn more.

This expert insight is sponsored by AMN Healthcare, an Advisory Board member organization. Representatives of AMN Healthcare helped select the topics and issues addressed. Advisory Board experts wrote the expert insight, maintained final editorial approval, and conducted the underlying research independently and objectively. Advisory Board does not endorse any company, organization, product or brand mentioned herein.

To learn more, view our editorial guidelines.