The Most Impactful AI in Healthcare Isn’t Clinical, It’s Operational

AI is showing up across healthcare, but not every use case is delivering real value. Ambient scribes are being celebrated as AI’s first big success in healthcare, but the impact is mostly perceived, not structural. They ease documentation and improve workflow, but they don’t reduce labor costs or create new capacity.

If we want AI to help solve healthcare’s biggest problems, we need to attack the line item that makes up more than half of a hospital’s total budget: workforce operations. It’s the largest and most unpredictable expense, and the area with the most room for improvement.

Nurse managers spend as much as 60-80% of their time just coordinating schedules. These are clinical leaders responsible for patient flow, quality metrics, compliance, and team morale. But instead of focusing on care, they’re stuck in spreadsheets and group texts, manually working to fill shifts.

Where AI Is actually making a difference

While much of the attention around AI in healthcare has focused on diagnostics or documentation, the most immediate impact is happening in workforce optimization. AI is helping hospitals route shifts more efficiently, anticipate gaps in coverage, and give time back to the people who keep care moving.

Workforce platforms use real-time data to match shifts with the right staff based on availability, skills, and cost. Internal teams get first priority. Float pools are engaged early. Contract labor is used only when necessary or in specialized care areas. This makes scheduling proactive, instead of reactive, while helping reduce burnout and leading to tangible operational gains.

This approach to labor optimization also helps forecast and prevent problems before they start. Because workforce platforms utilize AI, they’re capable of learning over time. That means flagging shifts or units with high call-off rates and automatically building in backup coverage. They track patient census trends to help staffing align with demand. That kind of visibility helps nurse leaders make better decisions in real time.

The cost of doing nothing

Hospitals are facing renewed financial pressures from proposed Medicaid cuts, which could result in more than 11 million people losing coverage and a sharp rise in uncompensated care. Safety-net and rural hospitals stand to lose over $70 billion in reimbursements over the next decade. Any reduction in Medicaid reimbursement compounds the strain of rising labor costs and workforce shortages.

Between 2019 and 2022, hospitals saw contract labor costs spike 213%, largely due to pandemic-driven demand for travel nurses. While those costs have started to come down, they remain well above pre-pandemic levels. McKinsey estimates that nurse staffing shortages will cost U.S. hospitals $170 billion by 2027.

The financial pressure is clear. A travel nurse can cost $90 an hour or more, while an internal nurse might cost just $60 to $70 for the same shift. When patient demand drops, internal staff can be flexed down or reassigned. But with most travel contracts, hospitals are locked into paying premium rates even when the need subsides.

Workforce AI helps fix this imbalance. These platforms orchestrate a smarter mix of full-time and part-time employees, PRN or per diem workers, internal float pools, and contract labor. They prioritize internal coverage, activate flexible staff when needed, and reserve contract labor for specialty cases or last-resort coverage. The result is a more responsive, cost-effective staffing model that aligns labor with both patient demand and financial sustainability.

Making the most of the biggest investment

There’s always going to be a new AI tool promising to save healthcare, optimize a workflow, or save someone a few minutes a day. But before we chase every emerging use case, we should tackle the biggest, most expensive part of the system: labor optimization.

McKinsey reports that organizations that embed workforce strategy into core operations significantly outperform those that don’t, but many hospitals still treat staffing as a back-office task. 

Workforce AI delivers outcomes hospitals can measure. It gives nurses more control over their schedules, takes administrative pressure off managers, and gives leadership the data they need to plan ahead, retain staff, and stay responsive in a constantly shifting environment.

Photo: Cecilie_Arcurs, Getty Images

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.