AI Should Work for Nurses, Not the Other Way Around

Editor’s note: The author is Chief Nursing Officer of the American Nurses Enterprise, whose AI training initiative is discussed in this article. Nurse.org received no compensation for this article.

Artificial intelligence (AI) is no longer a technology of the future. It is already changing how patient care is documented, how workflows are designed, and how clinicians access information to support decision-making.

As AI becomes more integrated into healthcare, nurses must help ensure it strengthens safe, high-quality, patient-centered care.

The central question is whether nurses will meaningfully shape how these tools are used.

That responsibility was at the center of the American Nurses Association’s inaugural AI in Nursing Practice Think Tank in early 2026. Leaders examined AI’s growing role in nursing and the guardrails needed to protect patients, nurses and the profession. The resulting consensus highlighted concerns including overreliance on AI-generated outputs, unclear accountability, algorithmic bias and increased cognitive burden.

Innovation must be guided by nursing expertise, clinical judgment and patient safety.

This is particularly important in rural and resource-constrained settings. For communities navigating workforce shortages and limited resources, AI may improve efficiency, expand access to expertise, and support nurses at the point of care. But without nurses’ input, these tools can add complexity and risk.

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Responsible AI adoption begins with a simple question: What problem does this solve for nurses and patients?

Too often, healthcare organizations start with the technology rather than the clinical need. A new capability becomes available, and leaders look for how to use it.

Nurses can, and should, help change that approach. As the clinicians closest to patients and the realities of day-to-day care, nurses bring an essential perspective on where technology adds value and where it may create unintended consequences.

Consider documentation, for example. If nurses are spending significant time on administrative tasks that could otherwise be spent with patients, an organization might evaluate whether an AI-enabled documentation tool could safely reduce that burden.

But the availability of a technology does not make it the right solution.

Any AI tool must be evaluated in the clinical environment where it will be used. Does it support, rather than disrupt, nursing workflow? Does it improve efficiency without shifting work elsewhere? Does it complement clinical judgment rather than encourage overreliance on an algorithm? Most importantly, does it contribute to safer, better patient care?

These questions are especially important in rural communities, where staffing resources, infrastructure and access to specialized expertise may differ from larger health systems. The goal is to determine whether a tool meets a meaningful clinical need safely, equitably and effectively.

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Once a meaningful use has been identified, nurses should be involved throughout the process. Nurses should have a voice in evaluating solutions, designing workflows, testing technology, developing training and determining how success will be measured.

Bedside nurses understand where workflows break down, which alerts are useful and how seemingly minor changes can affect patient care.

The American Nurses Foundation recently awarded three microgrants to nurse-led teams evaluating AI applications related to patient safety, clinical throughput, and documentation. One project, for example, is examining ambient AI listening tools in inpatient nursing to understand their impact on documentation, workflow fit and cognitive load.

This project will help demonstrate the value of having nurses evaluate AI in practice.

One guardrail should be fundamental to every AI implementation affecting nursing practice: AI should support professional nursing judgment, never replace it.

AI can identify patterns, organize information and generate recommendations. It cannot assume the professional accountability that belongs to the nurse.

This is especially important when AI influences clinical decision-making, documentation, escalation, staffing or patient communication. Nurses need to understand what a tool does, recognize its limitations and know when to question its output.

An AI-generated recommendation should never become a shortcut around clinical judgment. The nurse remains responsible for assessing the patient, considering the full clinical picture and determining the appropriate course of action within their scope.

Build AI Literacy and Ongoing Governance

Responsible adoption also requires AI literacy. Nurses do not need to become computer scientists or informaticists, but they do need to recognize that AI outputs may be incomplete or inaccurate, understand how bias can affect results, and know when their judgment should override or challenge AI-generated recommendations.

Training should extend beyond operating a software platform to include the clinical, ethical and professional implications of AI.

For example, the American Nurses Enterprise, which comprises the American Nurses Association, the American Nurses Credentialing Center, and the American Nurses Foundation, is launching a three-year, $5 million initiative focused on strengthening AI skills among nurses, with a priority on rural, remote and underserved communities. The initiative will provide co-designed education to help nurses apply AI in clinical settings.

Implementation also cannot be the finish line. Organizations need governance structures that evaluate AI over time, including its safety, effectiveness, workflow impact and equity.

Nurses must have meaningful mechanisms to report inaccurate information, unnecessary alerts, increased cognitive burden or other unintended consequences. If a tool is not working for nurses or patients, organizations should modify it, retrain users, change the workflow or stop using it.

ANA’s call for nurse-led guardrails reinforces this approach. The organization’s AI Think Tank identified priorities that include clear nurse-led guardrails, greater AI literacy, an AI playbook, policy and regulatory advocacy, and continued collaboration across sectors.

Nurses have adapted to every major technology, from paper charts to electronic health records. AI must likewise support, not sideline, them.

Nurses must help define the problems technology is meant to solve, shape its design and governance, and ensure it strengthens rather than replaces professional judgment.

Responsible adoption means using the right AI for the right purpose, with safeguards.

Done well, AI can reduce burden, support decisions, protect patients and preserve nursing’s human connection with patients.

🤔 Has your unit been part of evaluating an AI tool before rollout? What would nurse-led adoption look like where you work? Share your thoughts in the comments below.