Why AI in Healthcare Will Depend on Better Data, Not Bigger Models

Artificial intelligence (AI) in healthcare has moved from experiment to expectation. Across hospitals and health systems, leaders are no longer asking if AI has a role; they are asking where it belongs, how it should be governed, and what value it truly delivers.

Recent themes published from the 2025 Becker’s CEO + CFO Roundtable AI Summit, a gathering of hospital leaders discussing real-world AI adoption, underscored an important shift: AI must earn its place as part of the hospital’s core operating strategy, not just its innovation portfolio.

For finance, operations, and clinical leaders, that means moving beyond pilots and proofs of concept to a future where AI in healthcare, AI in RCM, and healthcare automation are measured by the same standards as any other enterprise investment:

• Are we improving financial performance?
• Are we reducing friction for our teams?
• Are we making care better, safer, and more sustainable?

And at the center of all of this is one simple, difficult truth: AI in healthcare is only as powerful as the data we give it.

How is Data Quality the Quiet Gatekeeper of AI in Healthcare?

Industry conversations about AI in healthcare often revolve around models and algorithms. But when hospital leaders talk candidly about what makes AI actually work, they start somewhere else: data quality.

Healthcare is rich in data yet still poor in insight. EHRs contain narrative notes, partial documentation, and manual workarounds. Revenue cycle systems often reflect what was coded or remembered, but not what truly happened in the clinical environment. The result is familiar to every CFO and COO:

• Unreliable case costing
• Gaps between clinical reality and financial data
• Difficulty trusting analytics and dashboards
• AI tools that promise a lot but deliver inconsistently

In those roundtable discussions, leaders reinforced that standardized, interoperable, well-governed data is the precondition for any successful AI initiative, especially in revenue cycle optimization and hospital operations.

That aligns with what we see every day. AI in RCM, predictive analytics, and automation fail quietly when the underlying data is incomplete or inconsistent. Poor data doesn’t just affect accuracy; it undermines trust, governance, and ultimately adoption.

Redefining ROI: From “What Did We Save?” to “What Did We Enable?”

The conversation around the ROI of AI in healthcare is expanding. Leaders remain accountable for margins, denials, and days in A/R, but they are increasingly looking at broader measures of value: time savings, trust built, and workforce well-being.

• Executives in the roundtable conversations described AI wins not solely in terms of revenue lift, but in terms of:
• Fewer hours spent on manual documentation
• Reduced cognitive load for clinicians
• Better coordination across finance, clinical, and operational teams
• Lower burnout where routine tasks were automated

One leader summarized it well: AI’s job is to “give clinicians the headspace to be doctors again,” not to replace them.

The principles of AI in healthcare apply just as much to revenue cycle management and hospital automation as to clinical decision support. If AI isn’t making life easier for the people doing the work and improving the care they provide, then it will not scale.

AI as an Enterprise Strategy, Not a Point Solution

Another shift highlighted from the roundtable is how C-suites are approaching AI investment. There is a clear movement toward tying AI to business outcomes and enterprise-wide strategies, rather than isolated, department-level tools.

Leaders described three patterns that separate successful AI programs from stalled ones:

1. They start with a strategic problem, not a technology.
   Questions like: “How do we reduce denials?” or “How do we give clinicians an hour back in their day?” drive better outcomes than searching for a place to plug in a model.
2. They treat AI as part of the operating model.
   Whether in RCM, supply chain, or staffing, AI requires the same rigor as any mission-critical system: metrics, risk frameworks, explainability, and cross-functional oversight.
3. They prioritize transparency.
   Hospitals increasingly expect what some call an “AI nutrition label”: clear, auditable answers to what a model does, how it was trained, and what data it depends on.

This is not a story about more algorithms. It’s about more alignment.

Building Readiness for What AI in Healthcare Will Demand

In my years working with health systems, I’ve seen new technologies arrive with great promise, only to fall short because the operational foundation wasn’t ready for them. AI in healthcare is entering that same phase: not a question of what it is, but a question of how hospitals prepare to get the most out of it.

AI is becoming woven into how hospitals think about revenue cycle management, supply chain, care coordination, documentation, and operational forecasting. But for AI to reach its potential, hospitals will need to strengthen the systems that feed into it:

• accurate, structured, real-time data
• transparent and auditable workflows
• automation that reduces friction rather than adds to it
• dependable alignment between clinical activity and financial outcomes

At IDENTI, our work reflects this emerging reality. We focus on helping hospitals get their operational data right at the source, because as healthcare organizations increasingly lean on AI to support financial and clinical decisions, he reliability of that information becomes indispensable.

This isn’t about replacing people or redesigning the system overnight. It’s about ensuring that as AI becomes a standard in healthcare operations, hospitals have the clarity, accuracy, and visibility required to make those tools effective.

Our commitment is to helping build the operational foundation that enables AI to enhance decision-making, support teams, and strengthen margins in a way that scales.

Looking Ahead: From Hype to Discipline

In the coming years, we will see AI in healthcare continue expanding across hospital finance, supply chain, and operations. Revenue cycle automation, intelligent agents, and predictive tools will become standard in the same way EHRs did.

The differentiator will not be who adopts the most tools. It will be who demonstrates the clearest discipline:

• The highest data quality
• The strongest governance
• The most human-centered design
• And the clearest understanding of what AI is actually for

The next era of AI in healthcare will belong to organizations that treat data as infrastructure, automation as standard practice, and AI as a discipline – not a trend.

At IDENTI, we believe the future of AI in healthcare is not about replacing people or chasing every new model. It is about building the foundations that allow AI to enhance decision-making, strengthen margins, and support teams who keep healthcare running.

Hospitals deserve AI that works as hard as they do, and the data that makes that possible.

Insights referenced here draw from leadership discussions held during the 2025 Becker’s CEO + CFO Roundtable AI Summit.