As AI becomes more embedded in clinical workflows, nursing leaders are clear on one point: AI will not replace clinicians. But clinicians who know how to use AI — and leaders who know how to govern it — will have a distinct advantage.
For many chief nursing informatics officers, the definition of “AI-savvy” is evolving alongside the technology itself.
Marc Benoy, BSN, RN, CNIO at Akron, Ohio-based Summa Health, sees the role shifting beyond traditional IT oversight.
“AI-savvy nursing leadership today is still defined by digital stewardship,” he said. “It is the transition from simply managing technology to governing intelligence.”
His focus is on ensuring AI serves as a clinical adjunct, filtering out administrative noise so clinicians can focus on patients rather than screens.
One of the challenges is that much of today’s AI literacy is designed for engineers or statisticians, not bedside clinicians, he noted. Formal training pathways remain limited, forcing informatics teams to learn in real time.
To navigate that environment safely, Summa Health relies on foundational informatics rigor: detailed workflow analysis, mapping future-state processes with measurable outcomes and close attention to “dependent inputs,” ensuring the data feeding AI systems is correct and validated.
“The system is only as effective as the information it receives,” Mr. Benoy said. Even as tools evolve, clinician-led oversight remains constant. At Summa Health, nurses retain final interpretive authority, verifying predictive triggers or AI-generated outputs before any change is made in patient care. He said he is upskilling by embedding informaticists in governance roles and challenging subject matter experts and super users with the same rigorous questions long used to evaluate clinical opportunities.
“While the tools are changing, our commitment to clinical excellence remains constant,” he said.
At Chicago-based Cook County Health, CNIO Benjamin Laughton, DNP, RN, said AI-savvy leadership begins with clarity about what AI is.
“AI is not a single tool or ‘thing,'” he said. “It is a broad set of technologies, including machine learning, natural language processing and computer vision.” Treating AI as monolithic, he said, risks overfearing or overtrusting its role in nursing care.
Because nurses already work alongside many AI-enabled systems, the challenge now is less about introducing technology and more about understanding its purpose, limitations and impact. Dr. Laughton’s teams ask pointed questions before adoption: How does this model perform in our patient population? Has it been validated? For large language models, what data were they trained on and what safeguards are in place? Does it meaningfully reduce clicks, duplicate documentation or nonclinical work?
Cook County Health is building foundational AI literacy for front-line nurses and leaders, with a focus on augmentation rather than automation. The goal, Dr. Laughton said, is to ensure technology supports clinical judgment, professional autonomy and patient relationships instead of eroding them.
At Charleston, S.C.-based Roper St. Francis Healthcare, CNIO Jared Houck, BSN, RN, said AI-savvy nursing leadership requires what he calls “disciplined curiosity paired with operational rigor.” As the organization explores AI-enabled capabilities such as smart room fall prevention and ambient documentation, each initiative moves through formal governance with defined pre- and post-implementation metrics. Nothing scales without measurable clinical, operational and financial impact.
Mr. Houck said large language model tools are already in clinicians’ hands. His focus is pairing hands-on AI fluency with structured education and guardrails that protect patients, professional licensure, data security and intellectual property. He said he remains engaged with emerging platforms and industry discourse to understand strengths, limitations and risk profiles.
“When done well, AI does not replace clinical judgment,” he said. “It augments it by reducing cognitive load, elevating meaningful clinical signals and reinforcing high reliability across complex care environments.”
Across all three systems, AI-savvy nursing leadership is not defined by how quickly tools are deployed, but by how deliberately they are governed. It requires data validation, workflow discipline, clinician-led oversight and structured literacy building. The nurse remains the final interpretive authority. The difference is that intelligence — once embedded in software — now requires the same level of scrutiny as any clinical intervention.
