Intelligent healthcare

To understand how you can maximize impact of artificial intelligence (AI) in healthcare, this report is the result of extensive KPMG research into the value being created by AI in the sector.
The Intelligent healthcare report is designed to provide actionable insights for leaders at every stage of their AI journey, from those deploying their first pilots to healthcare organizations seeking to scale enterprise-wide artificial intelligence for healthcare initiatives.

•      AI is already well-established in healthcare, with top applications including generative AI, speech recognition, agentic AI, machine learning, and robotics.

•      0% of surveyed organizations have fully integrated AI into service and product development—highlighting a major opportunity for growth.

•      84% of healthcare organizations face operational challenges with AI, such as data quality, skill gaps, and legal concerns.

•      77% are increasing their AI investments, despite these hurdles.

•      69% feel pressure to prove ROI from their AI initiatives, showing the need for measurable outcomes.

•      KPMG proposes a three-phase value framework for AI transformation:

• Enable: Build foundations—appoint leaders, create strategy, identify use cases, and ensure ethical alignment.
• Embed: Integrate AI into workflows, services, and technologies like robotics and wearables.
• Elevate (implied): Prepare for next-gen AI and scale impact across the organization.

To help ensure that AI serves as a catalyst for meaningful transformation, healthcare organizations need a clear, structured approach to adopting the technology. In this report, we propose a value-based approach that can help to maximize value in population health, patient experience, workforce wellbeing and cost effectiveness, integrating AI across all operations and workstreams.

Through this report, we introduce the three phases of AI value — a framework designed to help clinical providers maximize value by aligning AI investments to patient and operational outcomes, prioritize scalable use cases, and prepare for the next generation of AI technologies.