Healthcare organizations are moving quickly from experimenting with artificial intelligence to operationalizing it across clinical, administrative and IT environments. From virtual nursing support to predictive staffing and automated incident management, AI is no longer on the periphery.
Yet this rush to embed AI in day-to-day operations is exposing an uncomfortable truth. In healthcare, being technically right is not enough. AI must also be clinically right. And that only happens when people, processes and technology move forward together.
This tension is especially evident as organizations begin adopting agentic AI systems that are capable of both analyzing information and acting autonomously. The promise of faster response times, fewer manual tasks and improved operational resilience is compelling. However, without strong guardrails and workforce readiness, agentic AI can unintentionally introduce friction into already complex clinical workflows.
Consider a healthcare organization that pilots an AI agent designed to proactively monitor clinician access and workflow issues across electronic health records (EHRs), scheduling systems and identity platforms. The agent’s role is to identify patterns that could disrupt care delivery, such as repeated access failures or delayed order entry, and automatically open IT service tickets to address issues.
Initially, the agent performs as intended. It detects a spike in clinician access issues during overnight shifts and autonomously creates and prioritizes incidents in ServiceNow. From a systems perspective, the logic is sound.
However, based on those signals, the agent may correlate access failures with security policy drift and recommend tighter access controls as a preventive fix — a response that could worsen the situation. Overnight and on-call clinicians often operate under edge-case conditions, covering multiple facilities, rotating between units or relying on emergency privileges to respond to critical care needs. Restricting access in those moments wouldn’t reduce risk; it could delay care.
In cases like these, the actions of an AI agent may be technically right based on the data it observes. But in moments where seconds matter, even small barriers can increase cognitive burden on clinicians and introduce unnecessary risk into patient care.
Designing agents that respect clinical reality
Healthcare organizations must carefully assess how AI agents evaluate information and decide when to act.
Context-aware policies allow agents to account for clinical role, shift type and emergency status before acting. Changes that affect access controls, like those in the example involving a clinician access agent, may require explicit human approval, while situations with ambiguous clinical context prompt the system to flag confidence gaps rather than act autonomously.
This approach reflects a fundamental principle for deploying AI agents in healthcare — systems should optimize not just for speed or efficiency, but for safety, accountability and trust. In healthcare, digital trust isn’t abstract. It’s built when clinicians know that systems will support them in complex and time-sensitive situations.
In regulated, high-risk environments like healthcare, this structure is essential. When designed thoughtfully, agentic systems can reduce administrative burden without undermining clinical judgment. They can surface issues earlier, route work more intelligently and allow care teams to focus on patients.
Why workforce readiness matters
Workforce readiness is also essential to getting AI clinically right.
In the clinician access scenario, workforce readiness would help ensure that clinicians know when to trust the system’s actions and how to quickly escalate concerns, while IT teams would be trained to interpret AI-generated signals through a clinical lens. This shared understanding helps teams smoothly navigate unintended barriers at the point of care.
Yet many organizations are underestimating the work required to deploy agents safely and effectively. Our People Readiness Report highlights this disconnect. While healthcare leaders are enthusiastically adopting AI, many workforces lack clarity on how these tools should be used, which recommendations should be trusted and when human oversight is required. This uncertainty results in uneven adoption, hesitation at the point of care and missed opportunities to deliver value.
In healthcare, the readiness gap is magnified because clinical workflows are dynamic, exception-driven and dependent on human judgment. On-call patterns, emergency overrides, patient acuity and regulatory requirements all require rapid decision-making. In this high-stakes environment, workforce readiness can’t be an afterthought.
Measuring what matters
As organizations evaluate whether AI is clinically right, traditional success metrics like deployment timelines, number of automations or volume of resolved tickets only tell part of the story. In healthcare, success is reflected in clinicians spending more time with patients, fewer workarounds during critical moments, consistent AI use across teams and confidence that systems will support care delivery.
The most successful healthcare leaders will resist the urge to automate first and contextualize later. They will embed governance into system design, invest in workforce readiness, and ensure that humans remain accountable for decisions that affect access, care and patient flow.
In other words, progress isn’t measured by how much AI is deployed, but by whether care teams feel safer, more supported, and more confident when it matters most. Only then does autonomy become an advantage rather than a liability.
Christine Landry
Christine Landry is Global Vice President for Healthcare for Kyndryl.
