Want AI that actually works? Fix your data first.

• AI’s potential in healthcare remains largely untapped — not because of the technology itself, but because the data behind it isn’t ready.
• Poor data quality, governance gaps, and siloed systems undermine clinical accuracy, increase regulatory risk, and delay progress.
• Health systems that build a strong, integrated data foundation are best positioned to use AI to drive safer care and smarter decisions.

AI holds enormous untapped potential in healthcare by offering new ways to anticipate risk, enhance care, and make systems run more efficiently. But for most organizations, that potential remains out of reach. The reason isn’t a lack of innovation. It’s a lack of data readiness.

Today, most health systems are trying to build AI on top of fragmented, incomplete, or poorly governed data. Huron research shows that only 17% of healthcare technology leaders say their organization has a comprehensive data strategy with fully integrated systems. That disconnect between ambition and infrastructure continues to hold AI efforts back.

With bold vendor promises and increasing market pressure, it’s easy to focus on what’s next. Yet the systems seeing real value from AI aren’t rushing into the newest tools. They’re building a strong foundation first. And when the data is right, the results follow.

In healthcare, poor data quality slows innovation and puts patient safety and clinical outcomes at risk. Issues like inaccurate timestamps, incomplete vitals, or outdated medication records may seem minor. But for an AI model making a real-time decision, even small gaps can escalate quickly.

One incorrect flag could send a stable patient to the ED instead of home care. One missing input might prevent a model from identifying someone at risk for readmission. While often viewed as technical problems, these are real-world breakdowns that chip away at trust and confidence in the system.

Weak data governance also creates regulatory risk. Can you trace where your data came from? Could you defend it in an audit? Without a clear lineage, even well-meaning AI efforts create exposure, from regulatory fines to reputational damage.

And then there’s bias. Incomplete datasets often carry forward existing inequities and make it harder to deliver equitable care across populations. These patterns can stay hidden until models are in use. By then, they’re harder and more expensive to fix. By strengthening data quality and representation upfront, organizations can reduce bias and improve equity from the start.

More data doesn’t mean better data. And in healthcare, quality matters far more than volume. To be AI-ready, data must be:

• Complete: Not missing key clinical or operational details
• Consistent: Structured and normalized across systems
• Correct: Free from error, duplication, or ambiguity
• Timely: Updated regularly enough to reflect real-world conditions

But quality alone doesn’t make data valuable. To support meaningful AI, data also needs to be usable by both the model and the people making decisions with it. When tools are transparent about how they generate recommendations, clinicians are more likely to trust and act on them.

That’s why structure and clarity matter. A chatbot built on consistent terminology can provide accurate, helpful responses. Predictive models trained on current, reliable data can surface real risk, not noise. And decision support tools that are easy to interpret are more likely to be used at the point of care.

Getting there requires collaboration. High-integrity data meets technical standards and reflects the full context of care. That happens when clinical, technical, and operational teams work together from the start to shape the data, not just extract it.

Even the best data loses its power when it’s stuck in silos. In healthcare, silos exist across clinical, financial, and operational systems and between inpatient and outpatient care, and among specialties like radiology and oncology. Each creates a different risk, from revenue integrity to care continuity to data quality.

AI learns from what it sees, and when it only sees fragments, it produces flawed insights. A patient might appear low-risk based on clinical data alone, while social or behavioral risk factors live in a disconnected system. The result is an output that feels precise but misses the whole picture.

Different stakeholders may view their silo as the most urgent. But in reality, all of them affect overall system integrity. Breaking them down requires more than surface-level data sharing. It takes investment in interoperability, governance aligned across teams, and pipelines that support comprehensive, consistent, and contextual data.

When data is complete and integrated, AI becomes a tool you can trust to surface the right insights, identify gaps, and support better decisions. It creates a system that works for everyone. And when teams align around shared, trusted data, care becomes more coordinated and the entire organization runs more smoothly.

AI reflects the quality of your data. If that data is siloed or inconsistent, AI will amplify what’s broken. But when your foundation is clean, connected, and aligned to your goals, AI becomes a powerful engine for safer care, faster decisions, and better results.

Getting there starts with a simple question: Where does your data stand today?

That answer might come from a focused audit, a governance review, or an honest look at how well your systems actually talk to one another. Just as important, your clinical, technical, and operational teams need to agree on what they’re trying to achieve and how data will support it.

This work may not be flashy. But it’s the difference between tools that disappoint and tools that deliver. In five years, high-performing health systems won’t be talking about pilots. They’ll be running AI-powered operations with confidence because they didn’t chase the next big thing. They built a foundation that made AI work the way it’s meant to.