INDIANAPOLIS, January 28, 2026 – Healthcare’s rush to embrace artificial intelligence is hitting a snag: the data itself. Many hospitals and health systems are discovering that their existing data is too fragmented and inconsistent to reliably power the complex AI tools they’re hoping will revolutionize patient care and operations.
Data Quality: The Unexpected Hurdle to AI Success
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The promise of AI in healthcare hinges on having trustworthy data,but many organizations are realizing their data foundations are shaky at best.
- AI models are only as good as the data they’re fed, a principle known as “garbage in, garbage out.”
- Fragmented data systems and inconsistent definitions have long plagued healthcare, but AI is amplifying these issues.
- A focus on data governance and stewardship-assigning ownership for data accuracy-is crucial for accomplished AI implementation.
- engaging frontline workers in data quality efforts is essential, as they are the first point of data entry.
- Centralized AI governance, combined with decentralized execution, offers a balanced approach.
Sarang Deshpande, VP of Data and Analytics at Franciscan Alliance, which operates 11 acute care facilities across Indiana and Illinois, along with a sizable physician network, explains the problem bluntly: “AI is not going to fix poor data. It actually is exposing more and more of it.” Deshpande oversees enterprise analytics, data platforms and engineering, interoperability, AI and automation, and data and AI governance within the organization’s IS leadership.
Existing issues with data quality, gaps in data lineage, and inconsistent definitions immediately surface as unreliable predictions or biased outputs when organizations build AI and machine learning models.The focus is now shifting toward data quality and interoperability as mission-critical capabilities rather of routine IT hygiene issues.
The need for “human in the loop” has become essential as stakeholders do not trust AI models to consume clean data and make autonomous decisions. Machines struggle with ambiguity, and the regulatory requirements in healthcare make errors in AI predictions or automation especially risky.
Balancing Innovation with a Solid Foundation
Healthcare organizations are under pressure from multiple directions to implement AI. Enterprise electronic medical record (EMR) vendors are pushing AI features, while operational leaders seek solutions for capacity management and forecasting. IT departments are exploring opportunities ranging from service desk optimization to contract analysis.
However, solutions must integrate into actual decision-making workflows to deliver value. Use cases that exist outside the natural flow of work for clinicians, operations leaders, or finance teams tend to fail despite initial enthusiasm.
Deshpande described a hybrid strategy that includ
es leveraging AI capabilities built into existing systems or acquired through partnerships.
Centralizing execution proves tough because AI capabilities embed across multiple systems. Though, governance and coordination should remain standardized and centralized. This centralized governance function coordinates with legal, privacy, security, and contracting teams to create an ecosystem for evaluating new use cases.
Platform Strategy and Point Solutions
Franciscan Alliance has made a conscious effort to maximize capabilities from standard enterprise systems, prioritizing adoption of features from major vendors. Integration complexity and ongoing management challenges make third-party point solutions less attractive.
Though, organizations accept that some capabilities will require custom development or specialized third-party solutions. The decision hierarchy typically follows this order: first, adopt features from enterprise platforms; second, implement specialized third-party solutions with low cost and minimal integration requirements; third, build custom solutions only for select use cases.
The path forward requires balancing ambitious pursuit of AI capabilities with disciplined attention to foundational issues. organizations must avoid letting perfect become the enemy of good enough while maintaining focus on governance, data quality, and clear ownership. Deshpande emphasized this balance. “Data, AI, these digital solutions, they’ll only succeed when they’re embedded in real workflows and aligned with real problems.”
