Healthcare leaders report strong AI returns, but struggle to scale beyond pilots
Healthcare organizations are seeing the returns they expected from AI investments, yet most remain stuck in early-stage testing. Ninety-one percent of healthcare leaders say their AI tools have met or exceeded return-on-investment expectations, according to research from Riverbed. But only 31% describe themselves as fully prepared to operationalize AI strategies across their organizations.
The gap between expectation and execution is stark. Nearly 90% of AI projects in healthcare have not been fully deployed enterprise-wide. Currently, 60% remain in the pilot stage.
Healthcare spending on AI has nearly doubled year-over-year. Organizations spent $14.7 million on AI technologies in 2024, rising to $27 million in 2025. Seventy-eight percent of respondents reported increased AI investment over the past year.
Data quality is the primary barrier
Poor data quality is blocking progress. While 88% of organizations agree that data quality is crucial for AI success, only 49% are fully confident their data is accurate enough to deliver results.
The specifics are concerning. Just 32% of organizations rate their data as excellent for relevance and suitability. Only 38% believe their data meets high standards for consistency and standardization.
This matters because healthcare providers are counting on AI for better diagnostics and personalized treatments. Without clean, reliable data, these applications cannot work as intended.
For organizations serious about AI Data Analysis, addressing data foundations must come before scaling deployments.
IT fragmentation slows adoption
Healthcare IT environments are fragmented. The average healthcare organization uses 13 different observability tools from nine separate vendors. This creates operational silos that limit visibility and reduce efficiency.
Most organizations recognize the problem. Ninety-five percent are actively consolidating their tools and vendors to align IT operations with organizational strategy. Forty-five percent of leaders prioritize streamlining how their various platforms communicate.
Communications infrastructure under strain
Unified communications tools-video conferencing, messaging platforms-have become essential to daily healthcare operations. Employees now spend 43% of their work week using these tools, and 64% of respondents say they are critical to effective work.
Performance remains a concern. Only 42% of users are very satisfied with how these tools perform. Common issues include dropped calls, limited visibility, and high support needs. These technical problems create inefficiencies that impact patient care and employee productivity.
Ninety-six percent of organizations are consolidating the number of communications tools and vendors they rely on. Adoption varies by role: 57% of business leaders report tool consolidation is already underway, compared to 40% of technical specialists.
Data movement becomes critical
As AI strategies mature, the focus is shifting toward how data is moved and managed across networks. Nearly all healthcare respondents surveyed view data movement and sharing as an important part of their AI strategy.
Seventy-two percent of organizations plan to establish a formal AI data repository strategy by 2028. When scaling data across networks, leaders are most concerned with storage costs, data security, and network reliability. Seventy-eight percent cited network performance and security as essential to their future AI goals.
Healthcare providers are now looking toward resilient infrastructure to support the massive volumes of data that AI requires.
For those responsible for AI for Healthcare, the path forward requires addressing data quality first, then consolidating fragmented systems, and finally building the infrastructure to move and protect data at scale.
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