From AI readiness to impact: Why a strong data foundation determines success in healthcare

Healthcare organizations have moved beyond asking whether AI belongs in care delivery. Across clinical, operational, and administrative environments, momentum is building as leaders invest in AI to improve coordination, reduce friction, and help strengthen outcomes.

Healthcare leaders are increasingly ready to deploy AI. Their data foundations often aren't.

Across the industry, organizations are discovering that scaling AI requires more than introducing new models or point solutions. It depends on whether intelligence can operate across the realities of a healthcare system-across departments, across data sources, and across the workflows where decisions are made every day. That is why the conversation is shifting from AI enthusiasm to AI readiness.

AI momentum is clear, but scale remains uneven

Healthcare leaders are increasingly aligned on the potential of AI to improve care delivery and operations. Many see it as a way to strengthen decision-making, improve coordination, and enable information to move more effectively across teams and care settings.

A recent global survey of 500 healthcare leaders across seven countries, commissioned by Microsoft, reveals that 58% say they are ready to introduce AI agents into care coordination and administrative workflows,¹ and nearly all believe AI initiatives can achieve scalable impact.² Yet a consistent challenge emerged in the research. While confidence is high, execution remains uneven. The same research found that 97% of healthcare leaders report that data silos already impact their ability to deliver timely care.³ That statistic underscores how deeply fragmentation can affect outcomes.

When critical data remains distributed across disconnected systems, teams spend time reconciling information rather than acting on it. In that kind of environment, AI struggles to scale beyond isolated use cases. Intelligence cannot consistently reach the people and moments where it matters most, which limits the ability to turn momentum into sustained, enterprise-wide impact.

Why the data foundation has become central to AI readiness

As healthcare organizations work to close this gap, strengthening the data foundation is emerging as a top priority. A unified and governed data environment enables interoperability and helps information flow across clinical, operational, and administrative systems. This allows AI to operate in context and support real workflows rather than remaining confined to pilots.

The urgency of that work is reinforced by the same global research: about 62% of healthcare leaders identify legacy technology as a primary source of fragmentation.⁴ That can include aging infrastructure and disconnected clinical, imaging, operational, and administrative systems that were not designed to exchange data. The finding points to a broader reality. Success with AI depends not only on advancing models and capabilities, but also on modernizing infrastructure, improving interoperability, reducing fragmentation, and creating connected data environments that can support AI at scale.

A stronger data foundation also supports trust. As AI becomes more embedded in care delivery and operations, leaders need systems that are transparent, auditable, and aligned with regulatory and organizational requirements. Governance, accountability, and responsible adoption are not separate from the foundation; they are part of it.

A unified data foundation can turn AI into a dependable, organization-wide asset

The organizations making the most progress are not treating AI as a standalone innovation. They are building the conditions that allow intelligence to operate across the system. That includes modernizing infrastructure, unifying data across domains, and improving interoperability so information can move across systems, partners, and care settings.

City of Hope offers a clear example. Doctors there spent significant time-often during nonworking hours-reviewing lengthy patient histories to prepare for appointments. Working with Microsoft, the organization built an AI solution on Microsoft Azure that processes and summarizes hundreds of pages of medical records, helping physicians onboard thousands of patients each year and spend more time face-to-face with the people they treat.

We want to take advantage of innovative technologies to support real-time decision making. We want to do it responsibly. We want to do it together. And, at the end of the day, we want to help improve healthcare for all.

Across organizations making this kind of progress, these efforts are paired with embedded governance-supporting transparency, accountability, and appropriate human oversight are part of everyday operations. These are not purely technical decisions. They are strategic choices that shape how healthcare organizations operate.

When these elements come together, AI can move beyond isolated initiatives and become part of the operating models that support coordination, improve efficiency, and help teams make more timely, informed decisions.

Turning AI momentum into real-world impact

Healthcare is entering a new phase of AI adoption-one defined less by experimentation and more by execution. The organizations best positioned for this phase treat data as a system asset, align stakeholders around shared outcomes, and embed AI into real workflows where it can deliver measurable value across care delivery, operations, and patient experience.

The opportunity ahead is significant. AI can help healthcare organizations reduce administrative burden, improve coordination, and support more timely, informed decisions. But realizing that potential will depend on what leaders do now to strengthen the foundation underneath it. In healthcare, the data foundation is no longer a background consideration. It is becoming one of the clearest indicators of whether an organization can scale AI safely, responsibly, and effectively.

Peterborough Regional Health Centre brought clinical, operational, and financial data together in Microsoft Fabric, connecting 18 production systems and moving from weeks-long waits for static reports to faster, iterative insight. With governed data and AI, teams applied that foundation to operational challenges, contributing to a 43% decrease in wait time to inpatient beds and a 20% quarter-over-quarter decline in unnecessary lab utilization. The result shows how a strong data foundation can move AI from ambition into practical, measurable impact while building the trust needed to scale responsibly.

Because we've built the right foundation, we're in a place where we can start to build at scale, move at speed, and do it with a lot more confidence as we prepare for a future where we're caring for a much larger patient population with the resources we have.

The shift from fragmentation to frontier starts with that foundation. The leaders who build it now are the ones who will turn AI momentum into lasting impact and create the conditions for AI to support care delivery and operational improvement at scale.

Explore the research and insights shaping this shift

As healthcare organizations move AI initiatives beyond pilot programs and into enterprise-scale deployment, a growing body of research and industry analysis is helping define what readiness looks like in practice.

To learn more about how healthcare leaders are addressing fragmentation, building unified data environments, and establishing the governance required to scale AI responsibly, explore the following resources:

Together, these resources offer a broader view of how healthcare organizations are navigating the shift from AI momentum to real-world impact and provide a useful benchmark for leaders evaluating their own data and AI readiness.

Microsoft products and services (1) are not designed, intended, or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.

All figures are from Microsoft, From Fragmentation to Frontier: Turning AI Readiness into Impact e-book (2026), a global survey of 500 healthcare leaders across seven countries:

¹ 58% of healthcare leaders say they are ready to introduce AI agents into care coordination and administrative workflows.

² Nearly all healthcare leaders believe AI initiatives can achieve scalable impact.

³ 97% of healthcare leaders say data silos already affect their ability to deliver timely care.

⁴ About 62% of healthcare leaders identify legacy technology as a primary source of fragmentation.

Research methodology: Global quantitative online survey conducted on behalf of Microsoft by OnePoll from January 7, 2026 to January 14, 2026 among 500 healthcare decision-makers with responsibility for AI and/or technology decision-making within hospitals and healthcare organizations with 400+ beds across the United States, United Kingdom, Germany, France, Australia, the Netherlands, and Sweden.