The report is a strategic blueprint to transform systems from Promise to Practice
KPMG International has unveiled a new research – Intelligent healthcare: A blueprint for creating value through AI-driven transformation. The new report forms part of a series of eight sector-specific reports based on research conducted with almost 1400 industry leaders, across key global markets and industry sectors, including healthcare, where executives reveal the steps, they are taking to dismantle legacy barriers and position their organisations for an AI-driven future.
The report is the result of extensive research into the value being created by artificial intelligence (AI) within the healthcare sector and is designed to provide actionable insights for leaders at every stage of their AI journey, from those deploying their first pilots to healthcare organisations seeking to scale enterprise-wide artificial intelligence for healthcare initiatives.
It is based on insights from 183 global healthcare leaders and extensive research and outlines a three-phase journey — Enable, Embed, and Evolve — to help healthcare systems move from pilot projects to enterprise-wide AI adoption.
Four strategic recommendations:
- Design AI strategies aligned with core competencies to enhance outcomes and reduce costs – To help maximize value creation from AI, healthcare organisations should develop an AI strategy aligned with their clinical and operational strengths, focusing on enhancing patient and workforce experiences, improving population health and reducing costs. AI initiatives should target diagnostics, administrative automation and personalised care, prioritising projects based on scalability, interoperability and impact.
- Build trust into your roadmap – Healthcare organisations should implement transparent, explainable AI (XAI), ethical governance frameworks and robust regulatory compliance.
- Build a culture that uses AI to uplift human potential – When it comes to taking a longer-term strategic view on AI, half of respondents say their organizations are currently developing a clear vision of how the tech can support their transformational ambitions in the next five years. AI should augment, not replace, human expertise.
- Create sustainable technology and data infrastructure for AI adoption – Modernise legacy systems, unify fragmented data sources and enable real-time AI integration.
Dr Anna van Poucke, Global Head of Healthcare, KPMG International said, “AI has the potential to fundamentally reshape healthcare — not by replacing the human touch, but by enhancing it. By integrating AI across different clinical and community settings and different operational streams, we can improve outcomes, ease the burden on healthcare workers, and create more resilient, patient-centred health systems.”
Lalit Mistry, Partner and Co-head, Healthcare Sector, KPMG in India, said, “AI is no longer a future promise — it’s a present imperative. By embedding intelligent technologies into clinical and operational workflows, healthcare organisations can unlock transformative value, improve outcomes, and build more resilient, patient-centred systems. The key is aligning AI with core value streams to ensure it drives measurable impact across the entire gamut of care.”
The future of healthcare lies not in optimising individual organisations, but in building intelligent, AI-enabled ecosystems that prioritize prevention, early intervention, and seamless patient journeys. Multidisciplinary collaboration — orchestrated by AI — will be central to this evolution. Leaders must act now to design enterprise-wide AI strategies, invest in workforce readiness, and deliver initiatives that create tangible, system-wide value.
Highlights include:
- 59 per cent have systematically incorporated AI into product and service development
- AI is having the biggest impact in IT (68 per cent), customer service, (66 per cent) and R&D (65 per cent)
- 72 per cent say their organisations have achieved efficiency improvements with the technology
- 84 per cent have faced operational challenges spanning, data issues and lack of skills and legal issues when implementing AI
