Riverbed, the leader in AIOps for observability, today announced the healthcare industry results from its Global Survey ‘The Future of IT Operations in the AI Era’. Despite the strong enthusiasm for AI, with 91% of healthcare leaders and technical specialists reporting that AIOps ROI has met or exceeded expectations, the industry faces significant barriers in enterprise-wide AI implementation. Only 31% of healthcare organizations say they are fully prepared to operationalize their AI strategy, with almost 90% of AI projects still not fully deployed enterprise-wide. Data quality remains a key challenge, with just 49% of decision-makers fully confident in their data to deliver accurate AI outcomes, with 88% agreeing that improving data quality is critical for AI success.
As AI continues to transform the healthcare industry through improving diagnostics, enabling personalized treatments, streamlining administrative tasks, and automating operations, organizations are rapidly increasing their AI investment. The research reveals that AI spending in healthcare has more than doubled, however, progress at scale remains limited as 60% of projects remain in the pilot stage. Data quality challenges continue to hold back progress, as just 32% of organizations rate their data as excellent for relevance and suitability, and only 38% for consistency and standardization. These challenges highlight a disconnect between leadership expectations and the complexities of implementation, reinforcing the need to bridge the gap between ambition and enterprise-wide AI adoption.
“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 preparedness, as well as poor data quality,” explains Richard Tworek, Chief Technology Officer, at Riverbed. “We address these challenges with safe, secure, and accurate AI built on high-quality data, delivering practical solutions that integrate seamlessly into complex IT environments. We are proud to be working with some of the largest healthcare providers around the world, helping them on their AI journey and enabling them to deliver a far superior patient experience.”
Healthcare organizations prioritize tool consolidation to reduce complexity
Healthcare providers are facing growing IT complexity as digital services and AI adoption expand, often resulting in fragmented environments that limit visibility and efficiency. The research revealed that healthcare organizations use an average of 13 observability tools from nine different vendors, driving operational silos across IT teams. In response, 95% of healthcare organizations are consolidating tools and vendors to reduce sprawl and streamline operations, with 93% also considering new vendors. Key drivers behind this shift include aligning IT with organizational strategy (51%), enhancing integration and interoperability (45%), and improving IT productivity (44%).
Unified communications performance impacts business operations
In the healthcare industry, unified communications (UC) tools have become essential to daily operations. Employees now spend 43% of their work week using UC tools, with 64% saying they are critical to operating effectively. However, challenges persist as only 42% of users are very satisfied with the performance of their UC tools, and 45% of healthcare organizations report issues with their video conferencing and messaging platforms. Limited call visibility (46%), dropped calls (45%), and high support needs (40%) remain the most common challenges, creating inefficiencies and impacting employee productivity. As a result, ensuring reliable, high-performing UC environments is becoming a business-critical priority for healthcare providers.
AI performance relies on efficient data movement across networks
As healthcare organizations progress their AI strategies, there is a focus on the movement and management of data at scale. 93% of healthcare respondents view the movement and sharing of AI data as important to their overall AI strategy, with 32% describing it as critical and foundational to how they design and execute AI. With data spread across increasingly complex environments, 72% plan to establish an AI data repository strategy by 2028. To move and scale data effectively, the top three considerations for healthcare organizations are the cost of data movement and storage (94%), data security and compliance (93%), and network performance and reliability (92%). Furthermore, 78% cited network performance and security as essential to their AI strategy, highlighting the need for resilient, high-performing infrastructure to support AI at scale.
Find out more about how organizations are overcoming gaps and navigating AI adoption and implementation by viewing the full report here.