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The Al Readiness Imperative: Why
healthcare data strategy is now
a C-Suite priority

Produced by Everest Group I Supported by EXL

Only 10-15% of AI pilots reach production. For most payers and providers, the barrier isn't the model. It's the fragmented data underneath it. We partner with you to close the data readiness gap and move AI from isolated pilots to enterprise scale.

The numbers tell the real story
 

10-15%
of AI pilots successfully scale into production
70-75%
of AI initiatives are held back by poor data quality
75-80%
of healthcare data remains unstructured
15-25%
of total U.S. health care spending goes to administrative costs

Source: Everest Group, The AI Readiness Imperative, 2026

Why AI ambitions stall

Healthcare data isn't the problem - access to it is.

Your clinical records, claims systems, and operational platforms generate enormous volumes of information every day. But that data sits in silos, follows inconsistent standards, and rarely reaches the workflows where decisions happen. Build AI on a fragmented foundation, and the results are predictable: inaccurate predictions, low adoption, and limited trust.

The shift to value-based care raises the stakes. Real-time, outcome-driven decisions demand more than incremental analytics upgrades. They require a deliberate data strategy that integrates every source, connects every system, and embeds intelligence where it counts.

The question isn't whether you need AI. It's whether your data is ready for it.

From fragmented data to enterprise AI: the roadmap

You know where you want to go. We help you find where to start. Our journey runs across six phases, each building on the last.

01
<p><strong>Discovery and alignment.</strong> Assess your data landscape, identify key sources, and define the use cases that matter most.</p>
02
<p><strong>Integration and foundation.</strong> Connect core systems, ingest structured and unstructured data, and stand up a scalable platform.</p>
03
<p><strong>Interoperability and access.</strong> Enable FHIR-based exchange, establish real-time pipelines, and extend sharing across partners.</p>
04
<p><strong>Trust and governance.</strong> Apply quality frameworks, establish lineage and ownership, and enforce security and access controls.</p>
05
<p><strong>AI activation.</strong> Deploy models across priority use cases and embed insights into daily workflows.</p>
06
<p><strong>Scale and optimize.</strong> Expand adoption, build feedback loops, and tune performance against outcomes and KPIs.</p>

Ready to move beyond the pilot?

Most health plans already have the data. What they lack is the foundation to turn it into enterprise AI value – across architecture, governance, and execution.

Download the Everest Group report and get the blueprint

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