Artificial intelligence is already delivering measurable efficiency gains in healthcare, helping clinicians reduce administrative workload, improve decision-making, and spend more time with patients. However, a new global survey suggests that many healthcare organizations are struggling to keep pace with the training and governance required to support widespread AI adoption.

According to the latest Philips Future Health Index, nearly half of healthcare professionals surveyed reported saving at least 132 hours annually through AI-powered tools. At the same time, 50% said AI has increased their capacity to care for patients, highlighting the technology’s growing role in clinical workflows.

The study gathered responses from more than 2,000 healthcare professionals and over 20,000 patients across ten countries, providing a broad view of how AI is being integrated into modern healthcare systems.

AI Is Becoming Part of Everyday Clinical Work

Healthcare professionals are increasingly using AI for both administrative and clinical tasks.

On the operational side, common use cases include generating and transcribing clinical notes, scheduling patient appointments, and using AI assistants to discuss work-related ideas or explore potential solutions.

In clinical environments, AI is being used to identify potentially dangerous drug interactions, assist with diagnosis based on symptoms, and support the analysis of medical imaging such as X-rays, CT scans, and MRIs.

Many clinicians report that AI helps them work more accurately, stay informed about medical research, and evaluate complex patient cases more thoroughly. Rather than replacing expertise, the technology is increasingly functioning as a decision-support layer that helps healthcare professionals process information more efficiently.

Adoption Is Moving Faster Than Organizational Readiness

While AI usage continues to grow, organizational support appears to be lagging behind.

The survey found that 64% of clinicians rely on personal AI tools when the solutions provided by their organizations fail to meet their needs. This highlights a growing gap between employee demand for AI capabilities and the ability of healthcare institutions to deploy approved tools at scale.

According to Philips Chief Innovation Officer Shez Partovi, many organizations are simply not moving fast enough to provide both the technology and the training required for successful adoption.

This creates a familiar challenge seen across many industries: employees discover value in AI independently, while governance, security policies, and structured implementation efforts take longer to catch up.

The Training Gap Remains Significant

Perhaps the most important finding is that AI adoption is advancing faster than AI education.

Seventy percent of healthcare professionals said training for AI-enabled tools is unavailable, limited, or inconsistent within their organizations.

As AI becomes more deeply embedded in clinical decision-making and patient care processes, the need for structured training becomes increasingly important. Healthcare professionals need more than technical familiarity with AI systems. They also need to understand model limitations, validation requirements, bias risks, and when human intervention is necessary.

The report argues that role-specific AI training programs can help clinicians build both the digital competencies and professional judgment required to use these technologies effectively.

Human Oversight Remains Essential

Despite growing confidence in AI-powered tools, healthcare professionals remain remarkably aligned on one principle: human oversight remains critical.

Ninety percent of respondents said it is essential to keep a human involved in AI-assisted processes, while 86% believe all AI-generated outputs require human review.

These findings reflect a broader trend across regulated industries. As AI capabilities improve, organizations are increasingly focusing on how humans and machines collaborate rather than viewing automation as a complete replacement for expert judgment.

Healthcare may become one of the clearest examples of this model, where AI accelerates analysis, surfaces insights, and reduces administrative burden, while clinicians remain responsible for interpretation, context, and final decisions.

What This Means for Organizations

The healthcare sector offers a valuable lesson for organizations adopting AI in any domain.

Deploying AI tools is only part of the challenge. Real value comes from creating the processes, governance frameworks, training programs, and operational practices that allow people to use those tools effectively.

As AI adoption accelerates, the organizations that invest in structured enablement alongside technology deployment are likely to see the greatest gains in productivity, quality, and user trust.

The message from healthcare professionals is clear: AI is already delivering results, but successful adoption depends just as much on people and processes as it does on the technology itself.