Managing the Revolution of AI in Nursing

An Article by Fred Neis, MS, RN, FACHE, FAEN

and

Mar 20, 2025

For decades, the practice of a nurse and the interactions with consumers (patients) have been not radically updated. Electronic health records (EHRs) deployments have been one of the more seismic updates and seemingly brought additional workload and complexity to process. Virtual nurse models are emerging as an innovative way to care for people. We tend to continue to focus on getting to a productivity target, which in turn pushes the limits on workload, roles, and ultimately the way care is delivered. The perennial conversation with executives is there are not enough nurses, those working are burned out, and financial pressures result in lower investment in the workforce. AI is coming hard and fast as the solution to get us out of the loop. A once-in-a-century innovation for nurses.

As artificial intelligence (AI) cements itself as a leading investment and operational strategy in healthcare, nurses are finding themselves on the frontlines of a rapidly shifting practice landscape. The infusion of AI into clinical practice brings both opportunities and challenges, requiring a delicate balance of technological advancement and human preparedness. Without the voice of the nurse, nurse-involvement in the process of change, and strategic implementation, AI risks becoming another layer of complexity rather than a tool for transformation. As a health system, our early and disruptive experiences with electronic health records provides evidence for the critical need to consider human factors in the creation and implementation of AI-technologies at the bedside and in the clinic.

Nurses form the scaffolding of the healthcare system, representing the largest workforce in U.S. healthcare—nearly 20% of all healthcare workers. Their role is unquestioned in importance, yet as AI-driven solutions enter clinical settings, nurses increasingly interrogate the impact on their workload, patient care, and professional autonomy. The concerns are valid. Historically, new technologies have been introduced with promises of efficiency, only to be found later to increase administrative tasks and add steps and complexity to workflows.

2024 marked a pivotal moment, as nurses more actively gained a voice in AI’s role in their practice. Bargaining agreements started incorporating AI-related conditions, and nurse-led protests questioned the value and implications of these emerging capabilities. Opposite these indicators, a survey of nurses in 2024 showed two-thirds would like to see an uptick in use of AI. Any AI integration must aim for at least a net-neutral impact—ideally, it should enhance efficiency, streamline processes, and support better patient outcomes. Whether by eliminating waste, standardizing processes, or reassigning tasks within the workflow, AI should deliver tangible benefits to both the clinician and consumers receiving care.

However, true transformation cannot happen by merely working around the edges or in a “bottoms up” approach. AI holds early promise in extending clinical capabilities, improving decision-making (both speed and quality), and alleviating administrative burdens. If implemented effectively, AI has the potential to give nurses more time to focus on patient care, an increasingly crucial factor as the profession grapples with workforce shortages and burnout. On one hand, poor AI implementation may exacerbate burnout and overwork thus mitigating any gains in productivity and economic value. On the other hand, if done right, it can augment the human clinician to improve accuracy and automate repetitive tasks that contribute to workload and job stress. Well implemented AI is positioned to move nurses closer to top-of-license effectiveness. A perennial issue for the profession. Yet, achieving this vision is far from simple.

Drawing from past experiences with implementing significant changes in healthcare, success begins with a hardened foundation. Organizational readiness is critical, ensuring AI is not just another tool but a deeply integrated asset in the daily practice of a nurse.

Organizational Readiness: Setting the Stage for AI in Nursing

For AI to be a sustainable, trusted, and effective co-pilot in healthcare, organizations must cultivate a culture that supports curiosity, innovation, trust, and prioritizes governance.

1. Culture for Change

A collaborative, transparent, and inquisitive organizational culture is essential. Healthcare organizations must prepare for AI adoption by assessing their readiness and fostering an environment where experimentation and learning are encouraged. Not to mention confirming the financial resources needed for the innovation.

Assessing Readiness: Many organizations remain in the early adoption phase but are eager to advance. Establishing AI readiness as a strategic priority can set institutions apart as leaders in nurse innovation. It starts with examining the current state, moving to codify the gaps, and launching to explore how AI’s various capabilities can close the gaps.
Encouraging Innovation: Nurses require a safe space to explore, challenge, and experiment with AI tools. Not unique to nurses, the enterprise must be hardened to the desire to constantly innovate, with leaders providing the environment.
Clear Communication: Providing aligned rationale for AI implementation with business objectives—explaining the 'why,' 'what,' and expected outcomes—ensures harmonization of the enterprise.
Involve All Levels: All levels of the organization affected by the proposed implementation should have a seat at the table during assessment, planning, and implementation. From registered nurses to patient care technicians, it is critical for both assessment and change management to publicly involve affected parties.

2. Human-AI Collaboration

For AI to be an effective co-pilot, nurses must have a voice in every step of innovation, infusing trust and integration it into their activities of daily living, so to speak. The key is establishing AI as an augmentative tool, not a replacement for nurses. Recent statements by Nurse Unions suggest that there is a growing and tangible fear of job replacement. Emphasizing the human components is critical:

Clinical Decision Support: AI-powered predictive analytics can anticipate patient deterioration, suggest interventions, and improve outcomes. For example, Seattle Children's Hospital is developing an AI application to predict peripheral IV failures, potentially reducing complications and enhancing patient care.
Building Trust: Trust in AI is earned, not given. Nurses should critically evaluate AI-generated recommendations, verifying their accuracy before adopting them into practice. Over time, as AI proves its reliability, trust will naturally develop.
Maintaining Human Oversight: Healthcare is a zero-defect environment, built on the human touch. AI should never replace clinical judgment and the ability to intervene and override. Especially in the early years of adoption, humans must control final decision or risk adverse outcomes. Instead, AI should be the support system, enabling nurses to work more efficiently and effectively. Taking more precise and speedy action.

3. Governance: Responsible AI Implementation

Bringing AI into the clinical space requires a structured governance framework that ensures safety, accountability, and continuous improvement.

Purpose-Driven Architecture: AI governance should be embedded within core business strategies and objectives, starting at the board level, not treated as an afterthought, an independent strategy with fragmented governance, or as a question through process improvement as “AI do this for me.”
Multidisciplinary Oversight: A governance structure inclusive of direct-care nurses at every stage—design, testing, implementation, and evaluation. Tactical AI solutions should be developed with direct input from those who with interface with it routinely.
Accountability and Evaluation: Establishing clear metrics for AI performance, safety, and impact is crucial. Criteria and authority delegated, as with other scientific process discipline, a procedure to immediately stop AI participation when safety triggers are breached. Continuous assessment will ensure that AI solutions remain reliable, effective, and aligned with the practice of the nurse.

Key Areas of Focus for AI in the Clinical Nurse Enterprise

To effectively integrate AI as a value-add operational tool, healthcare organizations should prioritize areas where AI can provide immediate impact. Early wins can gain trust required for future complex innovation. Recent literature highlights three core areas as strong starting points:

1. Clinical Decision Support

AI can enhance patient care by predicting risks, customizing treatment plans, and streamlining decision-making.

Predictive Analytics: AI-led insights can identify early warning signs of patient risk or deterioration, allowing for a more rapid set of interventions. By leveraging AI for risk stratification, nurses can focus on the most in need individuals.
Care Model Optimization: Imagine an ICU where nurses manage four critically ill patients with AI-assisted monitoring and decision support. Practicing at real top-of-license (the pinnacle of their ability to use their training and experience, and where able delegate appropriately), AI could analyze patient data in real time, adjust medication administration, flag concerning trends and suggesting interventions well before a nurse on their own could spot the need for intervention. Or the nurse manager can have a staffing model which matches demand, moving to more predictability and nurse satisfaction.
Reduce Gaps in Care: Possibly even eliminate gaps as AI works continuously. It could close hand-off gaps between shifts, ensure follow up of care, confirm all elements of surgical readiness. And more.

2. Administrative Support

One of the most immediate and impactful applications of AI is in reducing administrative burdens, particularly documentation.

Reduce Documentation Workload: Studies indicate that nurses spend 20-40% of their shifts on documentation. Natural language processing (NLP) documentation tools can transcribe, summarize, and synthesize patient information. Automating this process could free up valuable time, leading to greater job satisfaction and improved patient interactions.

3. Workforce Optimization

AI can help address workforce challenges by identifying burnout risks, optimizing scheduling, and supporting wellness initiatives.

Burnout Detection: AI-driven analytics can flag signs of burnout, allowing for early interventions such as workload adjustments, mental health resources, and schedule modifications.
Cost Implications: The cost of replacing a nurse ranges from $28,400 - $51,700. Leadership supported by AI tools are finding the ability to better engage, thus retaining, nurses. Yielding significant financial and operational benefits.
Challenging Traditional Norms: AI adoption may serve as a catalyst for re-evaluating outdated practices in the field. If proven inefficient, the practice should be replaced in favor of those supported by evidence-based models and activities. Additionally, licensure rules, types of roles, and job descriptions will need to be reconfigured with greater flexibility to evolve quickly.

The Future of AI in Nursing: Shifting from Tasks to Strategic Thinking

While current AI applications focus on task automation and decision support, the future will demand a more dynamic interaction between nurses and AI. Nurses will need to instead challenge AI with higher-order clinical and operational questions. A paradigm shift will occur when nurses and leaders begin to ask AI applications:

"If you were me—a clinical nurse or leader—what would you do to solve this problem?"

This shift represents a more strategic, proactive engagement with AI, where nurses leverage its capabilities not just for task execution but for advanced problem-solving. Rather than AI being a passive tool, it will become an active component in shaping a holistic care delivery system, optimizing workflows, enhancing professional practice, and solving a portion of the workforce supply need.

Conclusion: A Call to Action

AI’s role in healthcare is expanding rapidly, and nurses are at the epicenter of this transformation. The integration of AI must be intentional, disciplined, and guided by principles of trust, transparency, and collaboration. By focusing on organizational readiness, human-AI collaboration, and governance, healthcare leaders can ensure that AI serves as a force multiplier rather than an additional burden. The key lies in actions designed to empowering nurses as innovators for those they care for and their own practice. AI is not the future of nursing—it is the present.

Health Tech Happy Hour

Discussion about this post