AI & Nursing: What Nurse Leaders Are Learning Now
By: Sarah K. Wells MSN RN CEN CNL
Artificial intelligence (AI) is rapidly moving from a concept discussed in innovation labs to a technology that nurses are encountering in everyday practice. I recently attended the 2nd Annual Nurse Leader’s Summit hosted by ICD Events and ANA-California in La Jolla, California. During nursing leadership discussions and roundtables, one theme was clear: AI is already shaping healthcare, but many organizations—and many nurses—are still figuring out how to use it safely, effectively, and responsibly.
AI Is Already in the Workflow
In many healthcare settings, AI is quietly embedded in existing tools. Ambient documentation systems can listen to clinical conversations and generate notes, predictive analytics can detect patient deterioration earlier, and AI-enabled systems can help analyze staffing needs or chart audits.
These tools have the potential to dramatically reduce administrative burden. Nurses currently spend a significant portion of their shifts documenting care rather than delivering it. AI documentation tools may reduce charting time, allowing nurses to spend more time with patients and families.
AI is also emerging in operational areas such as staffing, scheduling, and workforce planning. Data-driven scheduling tools may help reduce bias, balance workloads, and predict staffing needs based on patient acuity and demand. However, as Dr. Katie Boston-Leary shared, important strategies to support appropriate staffing must be integrated into any AI technology used to support staffing. These include reforming the work environment, valuing the unique contributions of nurses, innovating models of care, improving regulatory efficiency, and establishing staffing standards that ensure quality care.
Competencies for the AI Era
Despite these opportunities, many organizations acknowledge a gap in AI competencies among nurses and nurse leaders. One of the biggest challenges is that many professionals “don’t know what they don’t know” about AI.
Building AI readiness requires structured education and competency development. Key skills for nurses may include:
Understanding how AI systems generate recommendations
Evaluating whether AI outputs are accurate and clinically appropriate
Recognizing bias and data limitations
Protecting patient privacy and maintaining HIPAA compliance
Knowing when AI should not be used
One organization supporting nurses in leading the way with AI is Nurses for AI. Co-founded by Dr. Susan Deane and Dr. Irina Koyfman, Nurses for AI is committed to:
Nurse-led perspectives
Ethical leadership in AI
Transparency and responsible use
Collaboration over competition
Keeping the human at the center of innovation
Meanwhile, some healthcare organizations are beginning to incorporate AI education into simulation, competency frameworks, and just-in-time learning methods such as shift huddles, newsletters, and brief training modules.
Governance, Safety, and Accountability
Another consistent theme across discussions was the need for clear governance structures. Many institutions currently lack formal AI policies, even as AI tools are being introduced into clinical workflows.
Responsible AI implementation requires leadership oversight and structured frameworks that address:
Tool selection and validation
Risk and bias assessment
Data privacy and security
Ongoing monitoring of performance
Reporting systems for unsafe or inaccurate AI outputs
Importantly, clinicians remain responsible for the final clinical decision. AI may assist with documentation or recommendations, but accountability still rests with the licensed professional who signs the record.
Preserving the Human Side of Nursing
While AI promises efficiency, nurse leaders emphasized that the goal is not to replace nursing judgment. As Dr. Sharicca Miller emphasized in her talk, AI should augment clinical insight—not substitute for it.
Nursing remains a relational profession built on empathy, communication, and critical thinking. Many participants noted that the true opportunity of AI is not automation alone, but the possibility of returning time to the most meaningful parts of nursing: listening to patients, supporting families, and coordinating complex care.
The Road Ahead
AI adoption will likely look different across healthcare settings. Large health systems may invest in advanced predictive analytics, while smaller organizations may begin with modest tools for documentation or education.
What is clear, however, is that nursing must remain actively involved in shaping how AI is implemented. When nurses are included in governance, design, and evaluation of AI systems, these tools are far more likely to support safe care, equitable workflows, and sustainable nursing practice.
The future of AI in healthcare will not be defined solely by technology. It will be defined by how well nurses lead its integration.
Want to join the conversation about AI and nurse staffing?
Take the ANA-California Survey on how AI may be impacting nurse staffing at your facility.
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About the Author: Sarah K. Wells, MSN, RN, CEN, CNL is an experienced nurse career strategist dedicated to helping nurses and nurse practitioners of all experience levels and specialties achieve success in their nursing and NP journeys. Sarah founded New Thing Nurse to help provide support and guidance to the nursing community in a simple and direct format. Sarah’s vision is to foster a more supportive and fulfilled nursing world that spreads throughout healthcare and beyond.
Sarah is serving as a 2026 Advocacy Fellow with ANA-California, focusing on AI and equitable nurse staffing. Learn more about the 2026 ANA-California Advocacy Fellowships.
