Healthcare leaders are expressing significant confidence in their AI preparedness, but new research reveals a troubling disconnect between perception and reality. A recent Nordic Consulting report based on a survey of 127 healthcare leaders showed that 70% feel at least somewhat confident in their organization’s AI governance frameworks, but only 15% report having scalable infrastructure in place.
This striking disparity highlights what experts are calling the “AI governance gap”—where hospitals and healthcare systems believe they’re ready for AI deployment while lacking the fundamental infrastructure needed for successful, scalable implementation.
The Confidence vs. Reality Disconnect
The Nordic Consulting findings paint a concerning picture of overconfidence in the healthcare AI space. While enthusiasm for AI runs high across the industry, scaling AI across a healthcare enterprise is proving to be an incredibly complex process, according to Kevin Erdal, Senior Vice President of Transformation and Innovation Services at Nordic.
This confidence-reality gap isn’t unique to healthcare, but it carries particularly serious implications in an industry where AI decisions directly impact patient safety and care quality.
Many organizations underestimate the ongoing management needs of AI models, especially custom tools that consume high computing resources, Erdal noted. The challenge extends far beyond simply implementing AI tools—it requires sustained infrastructure, continuous monitoring, and ongoing optimization.
Data Infrastructure Challenges
Many survey respondents cited the lack of infrastructure to access and process data from across disparate systems as a major barrier to AI scalability. The issue isn’t just about having data available—it’s about creating interoperability across healthcare systems.
As Erdal explained, “It might be a scenario in which you already have the data readily available or stored, but you don’t necessarily have the interoperability to reach out and grab some of the data from your pertinent systems across the collective institution. It’s one thing to be able to store data, and it’s another to be able to process data.”
Building the Four Essential Pillars
When it comes to AI success in healthcare, there’s plenty of vendor hype around flashy tools, but sustainable implementation requires mastering four fundamental building blocks. Healthcare providers must understand that effective AI deployment isn’t about adopting the latest technology—it’s about creating a comprehensive foundation that supports long-term success.
Use Cases: The Strategic Foundation
“It all begins with use cases,” notes Spencer Allee, Chief AI Officer at Aspirion. “Organizations must clearly define specific problems they aim to solve, whether reducing initial denials, improving appeal success rates, or identifying denials trends.”
Identifying specific issues to resolve ensures that AI initiatives align with organizational objectives rather than pursuing technology for its own sake. Well-defined use cases transform consistent challenges into actionable problem statements, providing clear direction for AI implementation.
Data: The Essential Fuel
Robust data serves as the lifeblood of reliable AI solutions. Hospitals and health systems should evaluate their institutional data against broader datasets accessible through partnerships and vendor relationships. Combining data from multiple providers under the same health plan significantly enhances value compared to individual datasets, while exploring dormant data within larger datasets can extract predictive insights through machine learning workflows.
Platform: The Unified Engine
A strong platform serves as the engine where use cases and data unite effectively. Essential platform features include unified data access and management, intelligent workflow automation, predictive analytics capabilities, and customized insights and reporting. Without a robust platform, even the best use cases and richest data cannot deliver optimal results.
Talent: The Strategic Driver
Accessing expert talent, especially data scientists, proves vital yet challenging for successful AI strategy. While roles like operations experts and learning specialists are more accessible, the high demand for data scientists reflects their significant impact on designing effective solutions and extracting optimal value from data.
The Governance Imperative
AI in healthcare is only as reliable as the data it is trained on, making robust governance frameworks essential. Only a quarter of Nordic’s respondents report having a well-established governance framework, with the remainder acknowledging the need to create or improve formal governance.
Effective AI governance requires establishing policies, forming evaluation committees, ensuring regulatory compliance, training staff, and assessing outcomes regularly. Healthcare data constantly evolves, requiring AI training models to reflect those changes through continuous updates and bias monitoring.
Strategic Partnership Considerations
As Jim Bohnsack, Chief Strategy Officer at Aspirion, explains: “It’s like building a great engine and you don’t have any fuel for that engine, which is the data. And you don’t have a race to put it in, which is the use case, and you don’t have a driver—the talent.”
Strategic partnerships with industry leaders often prove vital for success. Partnering with experienced organizations that possess direct data access, well-defined use cases, robust platforms, and top talent significantly accelerates progress, reduces risk, and allows for more impactful results.
Key Questions
Before embarking on an AI strategy, providers should focus on realistic goals and consider these essential questions:
- What are the critical business challenges that require addressing?
- In what ways can AI contribute to solving these challenges?
- What approach will be taken to acquire or develop the AI solution?
- How will success be defined and measured
- What is the timeline for evaluation?
Moving Forward
This latest AI preparedness research serves as a wake-up call for healthcare leaders who may be overconfident in their AI readiness. Success requires moving beyond flashy demonstrations to build proper governance frameworks and infrastructure.
“While payers may have a technological and financial head start, providers can still find creative ways to harness the power of AI to enhance their RCM performance,” says Bohnsack. “But it’s about more than just building an impressive engine. Simply having the most advanced technology platform doesn’t guarantee success.
“Providers must ensure they have the right data to power the engine, the optimal use cases mapped out, and the talented workforce capable of pushing the system to its full potential. Only then can healthcare providers truly accelerate their RCM efforts and outpace the competition.”
Hospitals that recognize this governance gap and take proactive steps to address it will be better positioned to harness AI’s transformative potential. Rather than focusing on if or when to adopt AI, leaders must concentrate on how, how fast, and with whom to implement these technologies effectively. All four pillars—use cases, data, platform, and talent—must work together synergistically to unlock AI’s full potential. More detailed information on the four pillars of revolutionizing denials management can be found here.
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