Despite decades of increasing technology investments, healthcare systems have struggled to achieve corresponding gains in productivity and efficiency. While IT spending as a percentage of operating budgets has increased 3-5x the past 40 years (growing five times faster than healthcare costs overall) the return on these investments has been limited. The average cost per billing provider has now reached $32,000, yet performance improvements have lagged expenditure.
AI Changes the Paradigm
Artificial intelligence is fundamentally changing this equation. Our recent data shows that health system executives across the board have elevated AI as a strategic priority for 2025, making it the largest "gainer" among prospective priorities compared to 2024.
This enthusiasm isn't simply theoretical, it’s driven by tangible results. While sentiment just 18 months ago registered as cautious optimism or even skepticism, health systems are now reporting significant ROI from targeted AI implementations such as:
Revenue boosts from improvements in revenue cycle management
Reductions in preventable events such as patient falls
Decrease in care gaps
These improvements would traditionally have required substantial investment in unscalable human capital, but AI is proving to be a more efficient alternative.
The Bifurcation of AI Priorities
Interestingly, our research reveals a bifurcation in executive perspectives on AI progress. While many C-suite leaders feel they've made considerable strides relative to their goals, there's a notable division on future priorities. For Chief Information Officers, AI remains the number one area for improvement, while its ranking has dropped among other executives, including Chief Strategy Officers. This disparity signals potential misalignment that organizations will need to address.
Table Stakes vs. Strategic Differentiation
Although most health systems are still early in their AI journey, implementation is accelerating rapidly. A set of "table stakes" use cases is emerging where early adopters are gaining financial, care delivery, and competitive advantages.
In operational domains, revenue cycle automation, claims management, and capacity management have become basic requirements. More progressive applications include drafting high-fidelity patient messages, automating call centers, and providing patient room assistants.
Clinically, we're seeing similar stratification between foundational and advanced applications. The key takeaway is that health systems must adjust to a technology evolution that's accelerating more intensely than any previous era in healthcare.
The Widening Gap Between Systems
This acceleration is creating a widening performance gap. Slower adopters risk further marginalization, and even those with historically strong financial performance may find themselves at a disadvantage if they delay AI implementation.
Some forward-thinking organizations are using AI to leapfrog competition. Nebraska Medicine's partnership with Palantir illustrates this approach, building solutions that address bottlenecks, improve workflows, and enhance care delivery. Mayo Clinic, Mass General Brigham, and Emory are pursuing AI for revenue diversification and commercialization opportunities, developing specialized models in pathology, radiology, and other clinical domains.
Three Critical Success Factors
AI's transformative potential is ultimately limited by health system infrastructure, which depends on three key factors:
Governance that evolves and scales at the speed of AI
Technology that accelerates competitive edge and drives ROI
Frontline Buy-In that ensures adoption and scales impact
Most organizations are approaching AI with an "EHR + 1" platform strategy. While the electronic health record remains the digital backbone driving technology decisions, progressive systems are supplementing EHR capabilities with specialized AI solutions.
These organizations recognize when their EHR vendor's AI tools are "good enough" versus when specialized solutions offer strategic advantages. They're selectively building or implementing best-of-breed capabilities or partnering with flexible enterprise AI platforms that offer foundational capabilities they can extend.
Gaining clinician acceptance remains crucial for successful AI implementation. Our research shows that while frontline nurses initially express skepticism about AI solutions, their support increases dramatically with proper messaging from the right messengers. Including these stakeholders early in the process proves essential for successful adoption.
We stand at a turning point where AI is redefining healthcare competition. Each organization's approach to AI—whether as a differentiator, commoditizer, or democratizer—will determine its place in the evolving landscape. The gap between leaders and laggards is widening, making strategic AI implementation not just an opportunity but a competitive necessity.
Click here to access Themes 2025: Health Systems at a Liminal Moment for more on AI, patient access, and the broader macroeconomic factors impacting healthcare.