12/06/2026
This Friday’s story kicks off now.
We continue our Friday series where we review and announce studies that we find interesting and that we recommend investors and founders pay attention to. Today, we will be providing an overview of the report “Global tech report 2026: healthcare” by KPMG.
Investment levels have risen dramatically. 40% of healthcare organizations are committing between $50 million and $100 million per year to digital technologies. In countries such as Australia, investment is growing at approximately 25% annually. Despite this financial commitment, return on investment remains modest. 57% of executives report ROI at or below breakeven. Only 30% exceed their initial investment, though most achieve breakeven after roughly 12 months.
AI adoption has moved from experimentation toward enterprise scale. 66% of healthcare executives report actively deploying AI use cases, a significant increase from 32% one year ago. 76% expect to be deploying AI at scale within the next 12 months, the highest percentage of any sector surveyed. 86% are embedding AI into workflows, services, and value streams. Healthcare leaders report that AI and intelligent technologies contribute 31% to 40% of total digital value gained.
However, several obstacles prevent faster progress. The most significant challenge cited by 42% of respondents is weak governance and limited expertise, which leads to fragmented decision making and slow ex*****on. Cybersecurity is the top concern, followed by unreliable data and hallucinations from AI systems. Regulatory compliance and data sovereignty rules add further complexity, particularly in Europe and the Middle East. 41% of healthcare leaders plan to increase cybersecurity spending by more than 10% in the coming year, putting security ahead of AI and data analytics in terms of investment priority.
Data management remains a fundamental weakness. Healthcare accounts for approximately one seventh of all data generated globally, yet only a tiny fraction is actively used. Executives rated data analytics as relatively poor in terms of full optimization. The top data related priorities are data powered forecasting, better data accessibility across the enterprise, and data security. 69% of executives agree that traditional KPIs are not sufficient for tracking AI performance.
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