Healthcare organizations are experiencing a stark divide in AI adoption, with large hospital systems rapidly scaling artificial intelligence while smaller providers struggle to implement even basic applications. A new assessment from FTI Consulting and the Healthcare Information and Management Systems Society (HIMSS) reveals that organizations with annual revenues exceeding $1 billion are significantly more likely to be actively scaling AI across multiple use cases, while smaller facilities face mounting barriers to entry.

Why Are Large Healthcare Systems Pulling Ahead in AI Adoption?

The gap is most visible in revenue cycle management, a common AI application that handles billing and insurance claims. Among large health systems with over $1 billion in annual revenue, 54% report actively scaling AI-enabled revenue cycle management solutions. In contrast, only 15% of organizations with less than $1 billion in annual revenue have reached that scaling phase. This disparity reflects a broader pattern across nearly all major AI use cases in healthcare.

Kaiser Permanente exemplifies this trend, having deployed ambient documentation tools that automatically transcribe clinical conversations, explored AI-driven risk identification for patient populations, and invested in AI research initiatives across the healthcare ecosystem. Meanwhile, smaller and rural hospitals face infrastructure gaps, limited data quality, and unclear implementation pathways that make deployment prohibitively expensive.

The implications are significant. As large systems capture efficiency gains and cost savings through AI, smaller organizations risk falling further behind in workforce retention, clinical performance, and overall cost structure. This consolidation pressure may intensify over time, potentially widening the urban-rural divide in technology-enabled care.

What's Blocking Smaller Healthcare Organizations From Adopting AI?

Implementation costs represent the most immediate barrier for smaller providers. Unlike large health systems with dedicated technology teams and capital budgets, small facilities must choose between investing in AI infrastructure or maintaining existing operations. Data limitations compound this challenge; many smaller hospitals operate with fragmented, legacy systems that cannot easily integrate with modern AI platforms. Without interoperable systems and robust IT infrastructure, organizations struggle to prepare the clean, organized data that AI applications require to function effectively.

Beyond technical barriers, smaller organizations often lack clear adoption pathways. Large systems can afford consultants, pilot programs, and dedicated staff to manage AI implementation. Smaller facilities must navigate this landscape with limited resources, making it difficult to identify which use cases offer the highest return on investment or how to measure success once a system is deployed.

How to Build a Sustainable AI Strategy for Your Healthcare Organization

• Define Organization-Specific Objectives: Rather than adopting AI broadly, healthcare leaders should identify use cases aligned with their unique operational pressures and financial constraints. Operational efficiency ranks as the top priority for 82% of healthcare leaders, followed by improving clinical outcomes (72%), enhancing patient experience (66%), and reducing administrative burden (64%).
• Establish Clear ROI Measurement Frameworks: Organizations that tie AI investments to measurable outcomes are better positioned to secure funding and expand successful pilots. Clinical documentation automation has emerged as both the most widely adopted and highest-impact use case, helping organizations build foundational governance structures and stakeholder trust needed for more advanced applications.
• Treat AI as a Workforce Strategy: With 66% of healthcare leaders viewing AI as essential for addressing workforce shortages and burnout, AI adoption is increasingly shifting from a strategic option to an operational necessity. Solutions that reduce administrative burden and support clinical workflows are gaining traction as high-value applications that improve retention.
• Invest in IT Infrastructure and Data Governance: Organizations with strong information technology infrastructure, robust security postures, and interoperable systems are best positioned to scale AI effectively. Those with legacy systems and fragmented data environments risk falling further behind.
• Prioritize Change Management and Stakeholder Alignment: Effective governance structures, workforce training, and sustained communication are essential to driving adoption and ensuring AI initiatives deliver measurable value. Clear governance allows innovation to scale responsibly.

The cost of physician replacement underscores why workforce-focused AI matters. The average cost of replacing one physician amounts to 2 to 3 times their annual salary, making investments in AI solutions that reduce burnout and improve retention not only strategically important but also financially compelling.

Healthcare leaders should adopt a deliberate, organization-specific AI strategy that accounts for system scale and readiness. The path to value is not one-size-fits-all; success depends on matching AI investments to organizational capacity and clearly defining what success looks like before implementation begins.

"Regardless of AI maturity, identifying use cases with measurable and defensible impact is critical to securing resources and building stakeholder alignment across the organization," according to FTI Consulting's assessment.

FTI Consulting, in partnership with HIMSS

As healthcare systems navigate this critical crossroads, the divide between well-resourced organizations and smaller providers will likely deepen unless smaller facilities receive targeted support, clearer implementation guidance, or access to shared AI infrastructure. The question is no longer whether healthcare organizations should adopt AI, but whether they have the resources and infrastructure to do so responsibly and at scale.