The integration of artificial intelligence (AI) in healthcare is accelerating rapidly. Tools designed to assist with documentation, clinical decision-making, scheduling, and patient monitoring are being implemented in hospitals and healthcare systems faster than many nurses can adapt to, understand, or influence their deployment.
In our 2026 State of Nursing Survey, we asked nurses about their experiences with AI. This survey explored how frequently they use AI, their level of trust in these technologies, whether they received sufficient training, and how much input they had in selecting the tools their employers implement. The findings reveal a concerning trend: technology is often being introduced to nurses without their collaboration or consent.
Nurses and AI: 2026 Survey Results:
- 25% of nurses have personally used AI tools at work in the past 30 days.
- 60% of nurses with experience using AI say their employer hasn’t provided adequate training.
- 22% of nurses trust AI tools to support safe patient care.
- 40% feel they have no meaningful input in the selection of AI tools.
According to our survey, just 25% of nurses reported having used AI tools in their practice over the past month. The vast majority—68%—indicated they haven’t used AI at all, while 7% were uncertain. This raises questions about whether this reflects institutional decisions, limited availability, or personal hesitation. Nonetheless, it highlights that most nurses aren’t yet regularly working alongside AI systems.
It’s important to note that both statements—”Most nurses aren’t using AI yet” and “AI is being implemented in healthcare”—can coexist, creating a gap where issues arise.
Among nurses who reported any interaction with AI tools, only 40% believe their employer has equipped them with adequate training. Conversely, 60% indicated they have not received sufficient training.
60% of nurses with exposure to AI report a lack of adequate training for the tools they are expected to utilize.
This statistic is alarming in the clinical context. A nurse lacking a thorough understanding of how an AI-driven documentation system works may not recognize any inaccuracies. A nurse untrained in the limitations of clinical decision support tools might ignore valuable alerts or follow flawed recommendations based solely on algorithmic outputs.
The Institute for Healthcare Improvement has noted that relying solely on clinicians to verify AI results is “an unreliable safety strategy,” suggesting that deploying AI without adequate safeguards can pose real risks to both patients and healthcare personnel.
One nurse highlighted the generational aspect of this gap, stating, “No training on AI for intergenerational nurses. No support for intergenerational RNs on computer skills. No plan from government to support RNs — healthcare workers that survived the COVID epidemic.”
The training gap is noticeably more pronounced in high-complexity areas. Emergency Room, ICU, and medical-surgical nurses—those managing the most critically ill patients—are less likely to report feeling adequately prepared for the AI tools used in their settings.
When asked about their trust in AI-driven tools for safe patient care, only 22% of nurses responded affirmatively. Nearly 40% expressed active distrust, while 39% were uncertain about AI’s safety for patient care.
It’s critical to pay attention to the “unsure” percentage, which indicates that nearly 39% of nurses lack confidence in the safety of the AI tools available in their workplace. This is not a slight implementation hurdle; it signifies that healthcare facilities are advancing more rapidly than the necessary trust and evidence can support.
Interestingly, nurses who actively use AI tools tend to trust them significantly more than those who do not: 53% of AI users express trust, compared to a mere 12% among non-users. This disparity could either stem from positive experiences with well-designed tools or indicate a familiarity bias. Regardless, it highlights the importance of exposure and training; nurses pushed into using AI without proper preparation are unlikely to develop trust autonomously.
A 2024 survey by National Nurses United revealed that AI frequently contradicts nurses’ clinical judgment, with 69% stating that when their employer employs algorithmic systems to assess patient acuity, the outcomes often do not align with their evaluations. The NNU advocated for stricter regulations and greater nursing influence on AI deployment. Unfortunately, our data suggests this situation has not improved in the past year.
One nurse reflected, “We are witnessing a considerable decline in critical thinking among nurses. AI and algorithms have increasingly relegated nurses to task-oriented roles rather than professional practices.”
Another nurse voiced more explicit concerns: “After 34 years in nursing, I feel the profession is changing too much. I doubt AI can truly benefit our field.”
A crucial insight regarding trust and training is that 40% of nurses believe they have no meaningful role in choosing the AI and automation tools used in their work environments. Furthermore, 41% expressed uncertainty on this matter. Only 19% felt that nurses have a significant voice in these decisions.
Consider the implications of this. Those who spend the most time with patients—who will rely on these tools in high-pressure situations and who grasp the clinical context of their use—are often excluded from decisions about which technologies are acquired, how they’re implemented, and how workflows are shaped.
A 2024 survey by McKinsey and the American Nurses Foundation discovered that while almost two-thirds of nurses hope for more AI tools in their workplaces, their primary concerns include accuracy, lack of human interaction, and insufficient training. Nursing leaders noted that “technology companies frequently operate without any nurses on staff, nor do they include nurses in creating these technologies.”
This trend—technology designed without nurse input and imposed on nurses—permeates our findings. The predictable outcome is that tools misaligned with clinical needs, inadequate training, and a skeptical workforce result from this approach.
One nurse articulated the negative impacts: “AI has taken over our scheduling and disrupted it. Some of our security staff are older, and staffing is diminishing, leading to more paperwork for us.”
Another nurse pinpointed a structural issue: “Technology firms lack nurses on their teams, and they don’t involve nursing staff in developing these technologies.” This observation aligns with sentiments from nursing leaders heard in external research, revealing a pattern: the most affected stakeholders are often the last to be consulted.
AI adoption in nursing is not homogeneous; specialization and educational background influence how likely nurses are to utilize AI tools.
Nurse educators lead significantly, with 55% reporting the use of AI tools in the last 30 days, followed by administrative and leadership roles (49%) and case management (43%). These positions often involve more documentation and non-bedside functions where AI currently offers the most utility.
Conversely, nurses in long-term care (8%), oncology (10%), and PACU recovery rooms (12%) report the least AI usage. Bedside-focused, high-acuity specialties—like ICU at 15% and ER at 18%—also remain below the overall average of 25%.
Educational background appears to correlate with AI exposure as well. Nurse practitioners (47%), nursing students (50%), and Doctor of Nursing Practice candidates (45%) report the highest AI usage, while RN-ADNs (17%) and LPN/LVNs (16%) register the lowest. Higher education aligns with increased AI exposure, likely shaped by the nature of roles and settings where these nurses operate.
Age also plays a role. Nurses under 35 are roughly twice as likely to utilize AI at work, with 35% of those aged 25-29 reporting usage compared to just 16% of nurses over 65. The nursing population skews older; the 2024 National Nursing Workforce Survey recorded the median age of RNs at 50, up from 46 in 2022, with older nurses constituting 18.3% of the workforce—the largest single age group. This indicates that AI introduction is occurring in a profession where the most experienced members are also the least likely to engage with it or receive training.
Comments from nurses about AI consistently reflected three key themes: a concern over diminishing clinical judgment, frustration over exclusion from decision-making, and anxiety that the tools being implemented prioritize institutional efficiency over patient care.
- “Technology and the EMR have distanced nurses from the bedside. We now prioritize completing numerous checkbox tasks over engaging with our patients. I long for simpler tools like pen and paper that allow me to convey patient conditions and concerns in my own words. I often see my staff caught up in data entry instead of focusing on patient care.”
- “After over 45 years in nursing, I feel we have sacrificed the human connection in care. While we spend more time managing algorithms and checkboxes, genuine interactions with patients are dwindling. Sometimes dedicating just 10 minutes to patient concerns can negate the need for anxiety medications.”
- “This profession isn’t for everyone. My concern is that some individuals enter nursing purely for a paycheck. Integrating AI raises a valid question: if the AI provides incorrect information, how can we ensure we recognize that?”
- “After 40 years in nursing, I see how patient care has shifted. There’s now an overwhelming reliance on AI over personal interaction.”
However, a small but notable minority of nurses remain optimistic about AI’s potential:
- “I look forward to AI applications simplifying our tasks and advancing nutrition and functional health.”
- “I love being a nurse—it’s been my lifelong career for 38 years. I’m ambivalent about AI in nursing, but I believe it might assist with understaffing challenges. Still, the human aspect remains essential.”
Among those who actively engage with AI, 56% report that it has lessened the time spent on documentation and administrative duties. This benefit is significant, particularly as documentation burden is a consistent source of dissatisfaction among nurses. However, only a fraction of the workforce experiences this advantage, and it is introduced without the training, trust-building, or nurse input necessary for its sustained success.
Nurses exhibit a skeptical stance toward technology, but not out of opposition to it. They challenge poorly implemented technology—systems chosen without nurse involvement, launched without adequate training, and evaluated based on operational efficiency rather than patient outcomes or user experience.
What could a responsible approach to AI implementation look like? Nurses have long articulated that effective adoption must include their perspectives in procurement and implementation—not as mere token contributors but as vital clinical experts whose everyday experiences largely determine the effectiveness of the tools. This also entails comprehensive training before a tool is rolled out, the inclusion of user experience in evaluation metrics, and ensuring that clinical judgment remains paramount rather than surrendering it to algorithms.
National Nurses United has emphasized that nurses welcome worker-centric technologies that augment bedside abilities and enhance care quality, while opposing those that undermine clinical judgment. Their viewpoint promotes an implementation strategy founded on a precautionary principle: the obligation lies with the institution to prove a tool’s safety and effectiveness, not with nurses to disprove it.
The tools being introduced today will shape nursing for future generations. Whether they enhance the profession—making it safer for patients and more sustainable for nurses—or instead become another layer of obligation imposed on nurses rather than developed in collaboration with them, hinges on the decisions being made today. Currently, 40% of nurses believe they lack a voice in these critical discussions.
This is the challenge that demands urgent attention.
TThis article is part of Nurse.org’s ongoing coverage of the 2026 State of Nursing Survey.
