Healthcare leaders face significant gap between AI expectations and scaled adoption
In the healthcare sector, there is a significant gap between the high expectations for AI and the actual ability of organizations to scale these tools. 88% of organizations agree that data quality is crucial for AI success, despite only about half saying they are confident in their data, according to a study from Riverbed.
While 91% of healthcare leaders and technical specialists report that the return on investment for AI tools has met or exceeded expectations, the road to full implementation remains difficult. Only 31% of healthcare organizations describe themselves as fully prepared to operationalize their AI strategies.
Over the past year, organizations around the world have nearly doubled their investment in AI. In 2024, companies spent $14.7 million on AI technologies, but in 2025 this rose to $27 million (including $10 million within IT services). Reflecting this trend, 78% of respondents reported that their organization’s investment in AI has increased over the past year.
Despite the enthusiasm, nearly 90% of AI projects in the healthcare sector are not yet fully deployed across the entire enterprise. Currently, 60% of these projects remain in the pilot stage. This slow progress is often due to a disconnect between the ambitions of leadership and the technical complexities involved in making AI work across large, fragmented systems.
Data quality hinders progress
A major barrier to successful AI adoption is the quality of the data used to power these systems. Only 49% of decision-makers said they are fully confident their data is accurate enough to deliver good results. The vast majority recognize the important of accuracy: 88% of respondents agree that improving data quality is critical for the success of AI.
When looking at specific metrics, the numbers highlight serious concerns. Just 32% of organizations rate their data as excellent for relevance and suitability. Even fewer (38%) believe their data meets high standards for consistency and standardization.
These figures suggest that while healthcare providers are eager to use AI for better diagnostics and personalized treatments, the underlying information is often not ready for advanced automation.
Consolidating IT complexity
The healthcare industry is also struggling with an increasingly complex IT environment. On average, healthcare organizations use 13 different observability tools from nine separate vendors. This fragmented approach often creates operational silos that limit visibility and reduce efficiency.
To combat this, 95% of organizations are actively consolidating their tools and vendors. The goal is to align IT operations with organizational strategy and improve productivity. Integration and interoperability are also top priorities, as 45% of leaders look to streamline how their various platforms communicate.
Reliable communications become critical
Unified communications tools, such as video conferencing and messaging platforms, have become essential to daily healthcare operations. Employees now spend 43% of their work week using these tools, and 64% of respondents state they are critical to effective work. However, performance remains a concern. For example, only 42% of users are very satisfied with how these tools perform.
The vast majority of organizations (96%) are consolidating the number of tools and vendors they rely on. Adoption levels vary by role with 57% of business leaders reporting that tool and vendor consolidation is already underway in their organization, compared to 40% of technical specialists. The primary drivers are improving IT productivity and improving tool integration
Common issues include dropped calls, limited visibility, and high support needs. These technical problems can lead to inefficiencies that impact patient care and employee productivity. As a result, ensuring a reliable communication environment has become a business-critical priority.
The future of data movement
As AI strategies move forward, the focus is shifting toward how data is moved and managed. Almost all healthcare respondents surveyed view the movement and sharing of data as an important part of their AI strategy. To handle this, 72% of organizations plan to establish a formal AI data repository strategy by 2028.
When scaling data across networks, leaders are most concerned with the cost of storage, data security, and network reliability. In fact, 78% of respondents cited network performance and security as essential to their future AI goals. Healthcare providers are now looking toward resilient infrastructure to support the huge amount of data that AI demands.
“As healthcare organizations accelerate AI adoption to drive better clinical outcomes and operational efficiency, many are already seeing strong returns on their AI investments. However, our research revealed that progress is often constrained by gaps in readiness and poor data quality,” explains Richard Tworek, Chief Technology Officer at Riverbed.
