What's Happening?
In the healthcare sector, there is a significant gap between the high expectations for artificial intelligence (AI) and the actual ability of organizations to scale these tools effectively. 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, only 31% of healthcare organizations describe themselves as fully prepared to operationalize their AI strategies. Over the past year, global investment in AI has nearly doubled, with companies spending $27 million in 2025 compared to $14.7 million in 2024. Despite this enthusiasm, nearly 90% of AI projects in healthcare are not fully deployed, with 60% still in the pilot stage. A major barrier to successful AI adoption is data quality, with only 49% of decision-makers confident in their data's accuracy. Additionally, the healthcare industry faces challenges with a complex IT environment, using an average of 13 different observability tools from nine vendors, leading to operational silos.
Why It's Important?
The struggle to fully implement AI in healthcare has significant implications for the industry. AI has the potential to revolutionize healthcare by improving diagnostics and enabling personalized treatments. However, the current gap between expectations and reality could slow down these advancements. The reliance on accurate data is crucial, as poor data quality can lead to ineffective AI applications, ultimately affecting patient care and operational efficiency. Furthermore, the complexity of IT systems and the need for reliable communication tools are critical issues that healthcare organizations must address to ensure seamless operations. The consolidation of IT tools and vendors is a step towards improving productivity and integration, but the ongoing challenges highlight the need for strategic planning and investment in infrastructure to support AI initiatives.
What's Next?
As healthcare organizations continue to pursue AI adoption, the focus is shifting towards improving data management and infrastructure. Almost all healthcare respondents view data movement and sharing as essential to their AI strategy, with 72% planning to establish a formal AI data repository strategy by 2028. Key concerns include the cost of storage, data security, and network reliability, with 78% of respondents citing network performance and security as crucial to their future AI goals. Healthcare providers are now looking towards building resilient infrastructure to support the vast amounts of data required by AI. Ensuring a reliable communication environment remains a priority, as technical issues like dropped calls and limited visibility can impact patient care and employee productivity.
