Todd Owens is CEO/co-founder of Kevala.AI, with 20+ years leading high-growth companies across healthcare and enterprise tech.
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The healthcare industry is still feeling the aftershocks of the pandemic, especially when it comes to staffing. By 2028, the U.S. is projected to face a shortage of more than 73,000 nurse assistants (NAs) and 63,000 registered nurses (RNs) by 2030. However, nurses aren’t leaving because of long hours—they’re walking away from broken systems.
Heavy administrative loads, unpredictable schedules and limited managerial support are burning teams out. A 2022 U.S. Surgeon General advisory stated it reached “crisis levels.” Nearly half of RNs and licensed practical nurses (LPNs) continue to feel burned out multiple times a week, according to the National Council of State Boards of Nursing (NCSBN).
Hiring alone won’t fix it. Leaders in skilled nursing, health systems and senior living are scrambling to fill shifts while balancing staff preferences, operational budgets and quality of care. A significant opportunity is in offloading the work that’s pulling healthcare staff away from patients and residents, and that’s where AI agents are starting to make a real impact.
The Technology Gap: Why Traditional Tools Fall Short
Most healthcare organizations still build schedules the old-fashioned way, on spreadsheets and paper. Even those who use modern software-as-a-service (SaaS) platforms often fall short because they aren’t connected to the data that matters, including electronic health records (EHR), core HR platforms and staff preference and availability.
These 1.0 approaches can’t keep up with the speed and complexity of today’s healthcare operations. They ignore the real-life inputs that matter—like when a nurse flags she’s unavailable but still gets scheduled. That’s how you get last-minute callouts, shift gaps and a cycle of burnout, costly turnover and mandatory overtime for the teams left behind.
McKinsey research shows that up to 30% of nurses’ tasks could be automated or delegated—unlocking time for more meaningful care. That kind of efficiency shift matters more than headcount alone when the system is already stretched.
Unlocking Waste: Labor Utilization As The New KPI
The conversation around staffing often focuses on how many new hires are needed. However, the deeper issue is how effectively the existing workforce is being utilized. Unlike generative AI, which creates content, agentic AI systems take action. They can operate autonomously on analyzing data and optimizing workflows with minimal human intervention.
Deloitte projects that by 2025, 25% of companies using generative AI will adopt agentic systems, and that number will double by 2027. When an AI agent takes on a task, it’s not just assisting—it’s replacing entire categories of repetitive work, allowing care providers to focus on care and team development instead of chasing paperwork.
AI agents can help get more value out of the team you already have by making smarter decisions at scale. They can analyze credentials to ensure a med tech isn’t taking a shift below their license and factor in staff preferences and overtime risk to build the perfect schedule every time. They don’t just support humans—they fill in for them and never call in sick.
The American Medical Association reports that 57% of physicians believe reducing administrative burden through automation is AI’s top key performance indicator (KPI).
Real-World Examples: AI Agents In Action
By handling high-volume, repetitive workflows, AI agents move beyond assistance and fully take over entire categories of work. That gives providers more time to deliver quality care and build stronger teams.
Here are some real-world examples already in motion:
• Cleveland Clinic is rolling out AI agents to record patient appointments, generate medical notes and create after-visit summaries—tasks that previously took up hours of clinician time.
• Mayo Clinic is piloting agents to make its own business-to-business (B2B) calls for insurance verification, prior authorization, claims processing and appeals.
• AI agents are helping nurses free up time by sending text message reminders to patients to pick up their medication or gather information about their recovery.
Looking Ahead: AI Agents As A Labor Strategy
The World Economic Forum estimates that by 2030, AI and other emerging trends will create 170 million new jobs and displace 92 million. Work isn’t disappearing—it’s being redistributed.
The next wave of healthcare labor strategy will include:
• Shifting repetitive admin tasks to AI systems that can do the job faster and more consistently.
• Embedding digital teammates into daily operations to reduce burnout and staffing friction.
• Closing gaps between scheduling, credentialing and government compliance with connected systems.
• Prioritizing agents that give time back to caregivers, because happy teams deliver better care.
To ensure AI agents deliver the right value, leaders in healthcare tech should start with a clear map of their most repetitive, high-friction workflows—especially those that don’t require clinical judgment. Prioritize use cases where automation can immediately reduce admin load or mitigate compliance risk.
From there, pilot in a focused environment before scaling. Success depends on aligning your data systems—HR, scheduling, EHR—so agents can act with context. And don’t underestimate change management. Even the best AI needs buy-in from the people it’s designed to support. Build trust by showing how it removes busywork—not jobs.
By taking on the heavy administrative workload that too often drives people out of the healthcare industry, AI agents can help protect healthcare workers rather than replace them. When the busy work disappears, the focus can shift to ensuring the happiness of staff and better care and outcomes for patients.
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