Artificial Intelligence has progressed from theoretical to transformational in healthcare. Leaders are investing in AI to reduce costs, enhance outcomes, and reimagine the patient and clinician experience. The ambitions are bold – but the execution tells a different story.

According to a recent McKinsey report, while 62% of healthcare leaders view consumer experience and engagement as the most promising application for generative AI, only 29% have moved beyond pilots to real implementation. The gap isn’t a lack of vision – it’s a lack of readiness.

Across provider organizations and integrated health systems, the same patterns play out again and again:

Data discovery becomes a bottleneck: IT teams spend months just trying to locate and understand what data exists – across EHRs, billing systems, population health platforms, and legacy apps.

Data quality issues erode trust: Incomplete, duplicated, or stale data slows down analysis and undermines confidence in AI-driven recommendations.

Privacy and compliance concerns slow everything down: HIPAA obligations and regulatory uncertainty delay approvals and limit data sharing across teams and partners.

Every use case feels bespoke: There’s no reuse or scale – each new AI project requires new integrations, custom pipelines, and ad hoc governance reviews.

This is how promising ideas end up in “pilot purgatory” – initiatives that look good on slide decks but never reach patients or improve margins.

Externally, the pressure to modernize is accelerating. Payers are shifting toward risk–based contracts. Providers are moving towards value-based care. Consumers expect real-time digital engagement. Workforce shortages are forcing automation at scale.

But rushing into AI without clean, governed, interoperable data introduces new risks. As Press Ganey – an authority in patient experience and human-centered care analytics – has noted, AI deployments that skip governance and human oversight often erode clinician trust and add burden rather than relieving it. Ambition without data integrity backfires.

This is the inflection point: do we continue launching disconnected AI pilots, or do we invest in the infrastructure that makes those pilots succeed?

At Informatica, we work with healthcare organizations across the care and operational continuum – from academic medical centers to community hospitals, digital health startups to life sciences leaders. And the most successful of these all do the same thing: they start with data readiness.

The Intelligent Data Management Cloud (IDMC) is our platform designed for that exact purpose. It gives healthcare leaders a single place to integrate, govern, cleanse, and activate data across the enterprise – with the security and compliance that modern healthcare demands.

By solving the data problem first, the C-suite can unlock impact across clinical, financial, and operational domains:

Improved Patient Outcomes: Trusted, complete data enables better care coordination, early risk detection, and personalized pathways

Operational Efficiency: Automation of revenue cycle, credentialing, and scheduling processes eliminates waste and reduces staffing pressures

Compliance with Confidence: With built-in HIPAA support, access controls, and data lineage, you reduce legal exposure and accelerate approvals

AI That Scales: Instead of bespoke pilots, you create reusable, governed data pipelines that support an expansive set of AI use cases

A leading regional health system used IDMC to consolidate siloed data from over 50 sources – improving patient engagement workflows and accelerating their AI-powered discharge planning models.

A global life sciences organization adopted CLAIRE, our embedded AI engine, to automate metadata management, track data lineage, and support cross-cloud collaboration – resulting in faster regulatory readiness and AI model performance.

Multiple payer-provider networks now use Informatica’s pre-built mappings for HL7, FHIR, and claims data to standardize and accelerate analytics without burdening engineering teams.

Here’s what healthcare leaders can enable when data comes first: 

To unlock these outcomes, leading organizations are adopting a shared set of best practices:

Inventory and classify your data estate across clinical, operational, and third-party systems
Automate data quality, deduplication, and standardization at scale
Create governed, reusable pipelines aligned to common models like FHIR and HL7
Implement role-based access and data masking to meet HIPAA and enterprise policies
Make data discoverable via cataloging and self–service tools for business and clinical users
Layer AI on top only once the foundation is stable – so trust, scale, and performance are guaranteed

AI isn’t just technology – it’s a strategy. But a strategy without infrastructure is fragile. The most innovative healthcare systems in 2025 won’t be those chasing the next model – they’ll be the ones who invested early in data clarity, compliance, and control. At Informatica, we’re here to help you build that foundation: fast, governed, and future-proof.

For more information, please read this brief.