Insights from the Nurse Leaders Summit AI Forum Roundtables
During the AI Forum at the Nurse Leaders Summit, nurse leaders, clinicians, educators, and technology experts gathered for a series of presentations and roundtable discussions exploring how artificial intelligence may shape the future of nursing practice.
ANA\California Advocacy Institute Fellows who are currently developing policy campaigns related to artificial intelligence and equitable workforce practices attended these sessions, facilitated roundtable discussions, and captured insights from the presentations and conversations throughout the forum.
The summary below reflects a compiled synthesis of notes from all seven Fellows, bringing together observations, themes, and perspectives shared across the sessions to provide a comprehensive overview of the discussions.
Participants represented a wide range of healthcare settings, including hospitals, ambulatory clinics, academic institutions, public health agencies, and healthcare technology companies. While the level of AI adoption varied across organizations, the discussions revealed several shared themes about where artificial intelligence may support nursing practice, where challenges remain, and where thoughtful leadership and policy development will be needed moving forward.
1. Reducing Administrative Burden and Improving Documentation
One of the most consistent themes across the roundtables was the potential for AI to reduce documentation burden and administrative workload.
Nurses currently spend a significant portion of their shifts documenting care, navigating electronic health records, and completing administrative tasks. Participants noted that these responsibilities often limit the time available for direct patient care.
Several emerging technologies may help address this challenge:
- Ambient listening and automated documentation tools
- AI-assisted clinical note generation and summarization
- Automated auditing and compliance checks within the EHR
- Integration of wearable or monitoring devices that automatically populate clinical data
Participants emphasized that reducing documentation burden could allow nurses to refocus on patient care, care coordination, and clinical decision-making rather than administrative tasks.
2. Workflow Efficiency and Workforce Optimization
AI was also discussed as a tool that could improve operational workflows and workforce planning.
Potential applications included:
- Staffing and scheduling optimization using patient acuity and demand data
- Balanced patient assignments based on workload indicators
- Automated medication refill workflows and routine task management
- Predictive forecasting for staffing needs and patient volume
Some leaders noted that scheduling and staffing decisions are often reactive, with limited visibility into long-term patterns. AI-driven forecasting could help organizations plan staffing more strategically and reduce inefficiencies.
At the same time, participants cautioned that efficiency gains must not simply shift additional work onto nurses. AI implementation should support sustainable staffing models rather than intensifying workloads.
3. Clinical Workflow Support and Care Coordination
Participants also discussed how AI may support clinical workflows and care coordination.
Potential clinical applications include:
- AI-assisted triage during patient phone calls or symptom reporting
- Early warning systems that detect clinical deterioration earlier
- Integration with remote monitoring devices and wearable technologies
- Automated summarization of complex patient histories or medical records
These capabilities may allow clinicians to detect health changes earlier and intervene sooner. AI may also strengthen care coordination across the healthcare continuum by supporting discharge planning, follow-up scheduling, and remote monitoring.
However, participants emphasized that AI must support, not replace, clinical judgment. Nurses remain essential for interpreting patient context, assessing risk, and making clinical decisions.
4. Protecting the Human Side of Nursing
Many participants shared that increasing documentation requirements have shifted nursing work away from patient interaction.
If implemented thoughtfully, AI may help restore time for the relational aspects of care that nurses view as central to their profession.
Potential benefits include:
- More time for patient education and family communication
- Greater ability to address social determinants of health
- Improved patient satisfaction through more meaningful interactions
However, participants also expressed concern that technology could unintentionally create additional responsibilities for nurses if workflows are not carefully designed.
Protecting the time gained through automation will be critical to ensuring that AI improves nurse well-being and job satisfaction.
5. Governance, Policy, and Implementation Challenges
Across all discussions, governance emerged as a critical factor in successful AI adoption.
Many organizations reported that they are still in early stages of AI exploration and lack formal policies guiding its use.
Participants raised several important governance questions:
- How should healthcare organizations evaluate and vet AI tools before implementation?
- Who is responsible for oversight when AI contributes to documentation or clinical recommendations?
- How should organizations monitor performance and bias in AI systems?
- What policies are needed to ensure patient privacy, transparency, and consent?
Participants emphasized that nurses and clinical informatics professionals must be included in AI decision-making processes. Without frontline input, technologies may fail to align with real clinical workflows.
6. Education and Competency Development
Participants also discussed the need for structured education and competency frameworks to support responsible AI use.
Potential strategies discussed included:
- Competency assessments to identify knowledge gaps
- Simulation-based training programs
- Just-in-time learning delivered through staff education and clinical huddles
- AI literacy education within nursing programs
Participants noted that clinicians must understand how AI tools function in order to evaluate outputs responsibly and maintain professional accountability.
7. Equity, Bias, and Data Integrity
Equity considerations were also raised throughout the discussions.
AI systems rely on large datasets, and if those datasets contain bias, the resulting outputs may reinforce inequities in healthcare delivery.
Key concerns included:
- Bias in AI training data
- Data security and HIPAA compliance
- Transparency in how patient data is used
- Ensuring tools are validated for local patient populations
Participants emphasized that equity must be intentionally designed into AI systems through governance frameworks, validation processes, and ongoing monitoring.
Opportunities Moving Forward
While AI adoption in healthcare remains uneven, the roundtable discussions identified several opportunities for leaders and organizations to begin preparing for responsible integration.
These ideas represent early considerations that may inform future policy recommendations.
1. Establish Governance Frameworks for AI in Healthcare
Healthcare organizations should develop governance structures that guide the evaluation, adoption, and monitoring of AI technologies.
These frameworks may include:
- Multidisciplinary oversight committees that include nursing leadership and clinical informatics experts
- Structured evaluation processes for selecting and vetting AI tools
- Ongoing monitoring systems to identify safety concerns, bias, or performance issues
- Clear policies regarding accountability when clinicians rely on AI-supported documentation or recommendations
Strong governance will help ensure that AI technologies are implemented responsibly and align with clinical practice standards.
2. Develop Nursing AI Competency Frameworks
As AI tools become more common in healthcare, organizations should establish competency expectations for nurses and nurse leaders.
Potential approaches include:
- AI literacy education within nursing professional development programs
- Specialty-specific competencies related to documentation, clinical decision support, and workflow tools
- Simulation-based training for new technologies before deployment
- Just-in-time learning strategies that provide short updates on emerging tools
Competency frameworks can help ensure nurses maintain clinical judgment while responsibly integrating AI tools into practice.
3. Identify High-Impact Use Cases that Reduce Administrative Burden
Participants consistently identified documentation and administrative workload as key areas where AI may provide meaningful improvements.
Organizations may benefit from prioritizing implementation in areas such as:
- Automated documentation support
- Chart auditing and compliance monitoring
- Clinical note summarization for complex records
- Administrative workflow automation
Targeting high-burden workflows first may improve adoption while demonstrating measurable value.
4. Integrate Nurses Early in AI Design and Implementation
Successful AI implementation requires input from clinicians who understand real-world workflows.
Organizations should consider:
- Engaging nurses in technology selection and pilot testing
- Including frontline clinicians in design and workflow integration decisions
- Identifying nurse champions or superusers who can support adoption
- Ensuring feedback loops exist to refine tools after implementation
Early involvement can reduce resistance, improve usability, and ensure technologies address real clinical needs.
5. Protect Time Reclaimed Through Automation
If AI successfully reduces administrative workload, healthcare organizations should intentionally protect that time rather than replacing it with additional tasks.
This may include:
- Reinforcing expectations that documentation efficiency should improve work conditions
- Supporting time for patient engagement and care coordination
- Allowing nurses greater participation in professional development or shared governance
Protecting these gains will be essential to improving nurse well-being and retention.
6. Implement Transparency and Bias Monitoring Mechanisms
Healthcare organizations should also establish systems that monitor the safety and fairness of AI tools.
Possible strategies include:
- Incident reporting systems for unsafe or biased AI outputs
- Audit trails and validation dashboards
- Regular evaluation of model performance within local patient populations
- Clear communication with patients regarding how their data is used
Ongoing monitoring will help ensure AI technologies support equitable and safe patient care.
Looking Ahead
The insights shared during the Nurses Leaders Summit AI Forum roundtables highlight both the promise and complexity of integrating artificial intelligence into nursing practice. While many organizations are still exploring where to begin, the discussions made clear that thoughtful leadership, governance, and interdisciplinary collaboration will be essential.
ANA\California’s Advocacy Institute Fellows are currently working to translate these discussions, research findings, and frontline insights into policy recommendations that support responsible AI adoption in healthcare. These recommendations aim to ensure that emerging technologies strengthen patient care, protect nursing practice, and promote equitable and sustainable healthcare systems.
ANA\California Advocacy Institute Fellows 2026
AI + Documentation & Charting
Theodore Fletcher MSN, RN, PHN, ACRN, CCM
Stephanie Chmielewski DNP, MSN, MSCJ, RN, PCCN, HNB-BC
Nilima Mohapatra RN, BSN, CCRN
Jennifer Baird PhD, MPH, MSW, RN, NEA-BC, NPD-BC, CPN
Sunshine Joyce Batasin BSN, RN, PCCN, PHN
AI + Equitable Staffing
Sotera Delo Santos DNP, NEA-BC, CPHQ
Adrienne McIntyre DNP, RNC-NIC, CNS
Sarah K. Wells MSN, RN, CEN, CNL