U.S. AI In Nurse Scheduling Software Market (2025 - 2033) Size, Share & Trends Analysis Report By Deployment Mode (Cloud-based, On-premises), By Application (Shift Scheduling & Optimization, Demand Forecasting & Staffing Prediction), By End-use (Hospitals, Ambulatory Surgical Centers, Home Healthcare Agencies), And Segment Forecasts

Market Size & Trends

The U.S. AI in nurse scheduling software market size was estimated at USD 55.58 million in 2024 and is projected to reach USD 516.41 million by 2033, growing at a CAGR of 28.40% from 2025 to 2033. The rising demand for operational efficiency and the growing shortage of nursing professionals are significant factors contributing to market growth. In addition, advancements in AI and machine learning are other factors fueling market growth.

Rising demand for operational efficiency drives the U.S. AI nurse scheduling software industry. Hospitals and clinics face complex staffing demands, driven by increasing patient influxes and fluctuating care needs. AI-powered scheduling solutions automate routine tasks, enhancing accuracy and enabling real-time adjustments. These systems optimize nurse allocation, reduce administrative burdens, and enhance shift coverage, leading to improved patient outcomes and reduced nurse fatigue.

AI-based nurse scheduling solutions automate manual scheduling, allowing managers to focus on patient care. Advanced algorithms adjust staffing in real-time based on census trends, patient acuity, and skill mix, thereby reducing overtime and agency costs. For instance, Epic Systems is developing AI-powered clinical documentation tools, expected to launch in early 2026, aimed at reducing the time nurses and clinicians spend on documentation and administrative tasks. The native AI charting tool will automatically draft parts of patient records using Microsoft’s Dragon Ambient AI integrated within Epic’s apps.

Moreover, the growing shortage of nursing professionals across the U.S. presents a significant challenge for healthcare systems, driving the adoption of AI-driven nurse scheduling software. Hospitals and long-term care facilities are increasingly struggling to maintain adequate staff-to-patient ratios while complying with labor regulations and ensuring high-quality care. For instance, according to the data published by the American Association of Colleges of Nursing (AACN), federal authorities project a shortage of 78,610 full-time registered nurses (RNs) in 2025 and 63,720 in 2030.

Furthermore, advancements in artificial intelligence (AI) and machine learning drive the market by enabling predictive analytics and decision support. These tools help forecast shift demand using historical data, seasonality, and patient acuity. AI platforms generate actionable insights for proactive staffing and workforce optimization. Mobile and cloud-based systems enable easy access and real-time communication, enhancing the user experience for nurse managers and staff. For instance, vflok, launched by Catalyst by Wellstar and High Alpha Innovation, is a workforce optimization platform that uses advanced machine learning and generative AI to empower nurses to collaboratively manage schedules, source shift coverage, and enhance schedule transparency.

Market Concentration & Characteristics

The chart below illustrates the relationship between industry concentration, industry characteristics, and industry participants. The x-axis represents the level of industry concentration, ranging from low to high. The y-axis represents various industry characteristics, including industry competition, level of partnerships & collaboration activities, degree of innovation, impact of regulations, and regional expansion. The U.S. AI in nurse scheduling software industry is fragmented, with several emerging players entering the market, thereby contributing to increased competition within the market. The degree of innovation is high. The level of merger & acquisition activities is moderate. Moreover, the impact of regulations and the regional expansion of industry is high.

U.S. AI in nurse scheduling software industry is characterized by constant innovation, with a strong focus on launching new platforms and devices to leverage administrative tasks, improve diagnostic accuracy, and enhance care delivery. Prominent players are launching advanced software to sustain a competitive advantage. For instance, in July 2025, M7 Health raised USD 10 million to expand its nurse-first, AI-powered staffing and scheduling platform for hospitals.

The industry is experiencing a moderate level of merger and acquisition activities undertaken by several key players. This is due to the desire to gain a competitive advantage in the industry, enhance technological capabilities, and consolidate in a rapidly growing market. For instance, in July 2025, Symplr acquired Smart Square scheduling software from AMN Healthcare, integrating AI-driven nurse scheduling with Symplr's operations suite. The deal adds predictive analytics, real-time staffing, and open-shift management features.

The regulatory framework for the U.S. AI in nurse scheduling software industry is governed by evolving federal and state laws focused on safety, transparency, accountability, and data privacy. Federal oversight is primarily conducted by the Food and Drug Administration (FDA), which regulates AI-enabled software classified as medical devices under the Software as a Medical Device (SaMD) program. Vendors must obtain FDA clearance through pathways such as 510(k), De Novo, or Premarket Approval, depending on the software's risk level. At the state level, legislation focuses on AI transparency, ethical use, and the prevention of misleading claims.

The industry is witnessing high geographical expansion. Companies within the U.S. AI in nurse scheduling software industry seek geographic expansion strategies to maintain their foothold in the market.

Case Study Insights: Mercy Health Harnesses AI for Efficient Nursing Workforce Management, Saving $30 Million

Mercy Health System experienced significant challenges, including nursing shortages, elevated labor costs, and increased nurse burnout, which were intensified by dependence on premium agency staff during the pandemic. In response, the organization implemented AI-powered workforce management technology that enabled more efficient scheduling, improved shift coverage, and reduced administrative workload across its 50-hospital network, directly addressing the staffing crisis.

Challenges:

• Rising nurse labor costs due to overtime, contract labor, and inefficient shift allocation

• Difficulty predicting staffing needs and matching schedules to fluctuating patient demand

• Administrative burden resulting from manual workforce management

• Risks of nurse burnout and decreased job satisfaction

Solution:

• Mercy deployed advanced AI-driven nursing workforce management tools that leveraged predictive analytics and real-time data integration. These platforms automated shift scheduling, improved forecast accuracy for staffing requirements, and dynamically matched nurse supply to patient care needs across facilities.

Outcomes:

• The adoption of AI workforce management technology enabled Mercy to save USD 30 million in 2023. Outcomes included streamlined shift allocation, reduced overtime and contract labor dependence, improved patient coverage, and enhanced nurse satisfaction. The system’s predictive analytics and real-time optimization helped Mercy sustain operational excellence, financial savings, and high-quality care.

Deployment Mode Insights

Based on deployment mode, the cloud-based segment held the largest market share of 84.23% due to its flexibility, scalability, and cost-effectiveness. Cloud deployment enables healthcare facilities to access sophisticated AI scheduling tools without heavy upfront investments in IT infrastructure. This model also provides seamless integration with existing health IT systems such as EHRs and telehealth platforms, facilitating unified workforce management and real-time data updates.

In addition, this segment is anticipated to grow at the fastest CAGR during the forecast period. Increasing demand for efficient scheduling amid workforce shortages drives hospitals and clinics to adopt agile, cloud-hosted platforms. The cloud’s ability to handle large data volumes enables advanced predictive analytics and machine learning models that dynamically optimize nurse rostering and workload distribution. Cloud infrastructure supports advanced predictive analytics and machine learning, which optimize nurse rostering and workload distribution. 

Application Insights

Based on application, shift scheduling & optimization held the largest market share of 41.66% in 2024. Healthcare providers prioritize scheduling and optimization to streamline workforce management in response to ongoing nursing shortages and increased burnout rates. AI platforms use predictive analytics and machine learning to forecast staffing needs by analyzing historical patient data, seasonal trends, and workload variation, thereby enabling proactive staffing adjustments. Furthermore, shift optimization enhances nurse satisfaction by incorporating individual preferences and availability, thereby reducing turnover and promoting a better work-life balance.

The demand forecasting & staffing prediction is anticipated to grow at the fastest CAGR over the forecast period. Healthcare organizations are increasingly adopting data-driven workforce optimization to meet operational demands. Predictive analytics help anticipate patient admissions, seasonal trends, and emergency surges.  For instance, in May 2024, In-House Health raised USD 4 million in seed funding to launch its AI-powered nurse scheduling platform. The tool uses predictive analytics to forecast future staffing needs, streamline workflows, and reduce labor costs.

“Saving manager time on scheduling is a huge win and relieves burnout among nursing leaders, but the real prize is improved staffing outcomes. When hospitals fail to properly predict the future, it costs money in overtime pay and agency use. We can reduce both through precision staffing.”

-Ari Brenner, In-House Health technical leader

End-use Insights

Based on end-use, the hospitals segment held the largest market share of 60.34% in 2024, owing to the complex and high-volume staffing needs characteristic of hospital settings. Hospitals require robust and dynamic scheduling solutions due to their diverse nursing roles, fluctuating patient acuity, and 24/7 care obligations. AI-powered scheduling software enhances operational efficiency by optimizing nurse allocation based on skills, availability, and patient demand patterns.

Hospitals face significant workforce challenges, including nurse shortages, burnout, and high turnover, making AI-driven scheduling solutions crucial. For instance, in September 2025, Wellstar Health System implemented Swift, an AI-powered scheduling copilot developed by vflok Inc., across its 11 hospitals in Georgia, benefiting over 8,000 nurses and non-clinical staff. In a six-month pilot, Swift reduced scheduling change management time by 86% for nurses and support staff, and 96% for nurse managers.

"Swift has shown a reduction in nurse manager burden, allowing nurse leaders to spend less time on administrative tasks associated with staffing/scheduling and more time focused on patient care and team support."

-Susan Thurman, VP Nursing Practice & Clinical Integration at Wellstar

The ambulatory surgical centers (ASCs) segment is anticipated to grow at the fastest CAGR from 2025 to 2033. AI-powered scheduling solutions optimize nurse allocation in ASCs, balancing fluctuating patient volumes, diverse surgical specialties, and staffing requirements, improving operational efficiency and patient outcomes.

Key U.S. AI In Nurse Scheduling Software Company Insights

Key players operating in the U.S. AI in nurse scheduling software industry are undertaking various initiatives to strengthen their market presence and increase the reach of their products and services. Strategies such as new product launches and partnerships play a key role in propelling market growth.

Recent Developments

• In October 2025, OutcomesAI secured USD 10 million in seed funding to launch a scalable nursing model that combines AI voice agents with licensed nurses. Its Glia engine automates administrative tasks, increases nursing capacity by 3-5 times, and reduces costs.

“With OutcomesAI, we’re not replacing nurses - we’re multiplying them. By combining AI voice agents with licensed nursing teams, we give back time to the people at the center of care, reduce burnout, and build a sustainable, scalable model for the future.”

• In September 2025, Nursa launched an AI-powered nurse scheduling assistant (“NIA”) for healthcare facilities, enabling rapid creation of single or bulk shift postings. The tool can create shift listings by voice commands, photos, or spreadsheet uploads, significantly reducing manual data entry and staffing delays.

• In July 2025, SourceNow launched an AI-powered vendor management system (VMS) for healthcare staffing, featuring real-time float pool management and shift matching.

• In January 2024, ShiftMed launched ShiftAdvisor, an AI-powered personalized scheduling solution for nurses designed to improve staffing efficiency in healthcare facilities. It provides healthcare professionals with tailored shift recommendations based on past shift data and personal preferences such as day, time, pay, and location. This AI-driven system enhances provider satisfaction by giving users control over their schedules while helping facilities minimize last-minute cancellations and optimize staffing levels.

U.S. AI In Nurse Scheduling Software Market Report Scope

U.S. AI In Nurse Scheduling Software Market Report Segmentation

This report forecasts revenue growth at country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the U.S. AI in nurse scheduling software market report based on deployment mode, application, and end-use:

• Deployment Mode Outlook (Revenue, USD Million, 2021 - 2033)

  • Cloud-Based

  • On-Premises

• Application Outlook (Revenue, USD Million, 2021 - 2033)

  • Shift Scheduling & Optimization

  • Demand Forecasting & Staffing Prediction

  • Leave & Absence Management

  • Analytics & Reporting

  • Others

• End-use Outlook (Revenue, USD Million, 2021 - 2033)

  • Hospitals

  • Ambulatory Surgical Centers (ASCs)

  • Long-Term Care Facilities

  • Home Healthcare Agencies

  • Clinics & Specialty Centers

  • Others (Rehabilitation & Mental Health Centers)