Nelson Advisors: 20 Key Points from the KPMG Global Tech Report 2026 Healthcare
KPMG Global Tech Report 2026: Healthcare — Summary
KPMG’s 2026 healthcare technology report, based on interviews with 128 senior healthcare technology executives at organisations with revenues above US$1 Billion, finds a sector investing heavily in digital transformation but still struggling to convert that spend into measurable value. KPMG describes organisations as sitting in one of three modes: stabilising core platforms and data integrity, synchronising systems and governance, or scaling AI across workflows at enterprise level.
Investment is substantial. Forty percent of respondents spend between $50 million and $100 Million a year on digital technology, and a further 36 percent spend more than $100 Million. In markets such as Australia, spending is growing by around 25 percent annually. Much of this money goes into foundational platforms, electronic health records, ERP and cloud, which executives believe deliver roughly half of all digital value. Yet returns remain disappointing: 57 % report ROI below breakeven, although most reach breakeven after about 12 months and 30 percent exceed their initial investment.
The headline shift is in artificial intelligence. Two-thirds of executives are now actively deploying AI use cases, up from 32 percent a year earlier. Seventy-six percent expect to be deploying AI at scale within 12 months, the highest proportion of any sector KPMG surveyed and 86 percent are embedding AI into workflows, services and value streams. Early gains are concentrated in administrative and back-office functions such as HR, finance and procurement, with KPMG citing NHS staff onboarding (currently up to 64 days) as a prime candidate for automation. The larger prize lies in clinical pathways: AI-enabled triage, dynamic prioritisation of elective waiting lists, and remote pre-operative assessment.
Despite this ambition, healthcare remains cautious. 55% of organisations identify as fast followers and only 35% as early adopters, among the lowest rates of any sector. 44% prefer to wait for proven technology, and just 23% are willing to adopt disruptive technology at scale. Ninety percent take a long term rather than reactive investment approach. KPMG argues that this incremental posture risks leaving healthcare behind other industries and calls for “controlled acceleration.”
The barriers are familiar. Cybersecurity, unreliable data and AI hallucinations and regulatory complexity, particularly data sovereignty rules in Europe and the Middle East, top the list of concerns. 42% cite weak internal governance and limited in-house expertise as their most significant challenge, resulting in fragmented decision-making. Legacy systems and a proliferation of small proofs of concept that never scale further suppress returns. 69% agree that traditional KPIs cannot adequately measure AI’s distributed benefits. Technology governance is almost evenly split between centralised and federated models, and KPMG recommends a deliberate blend, with greater centralisation for whole-system change.
Resilience is rising up the agenda: 41% plan to increase cybersecurity spending by more than 10% next year, ahead of AI and data analytics. Business continuity planning must guarantee minimum access to patient information even when systems fail. Meanwhile, 29% intend to hire more onshore technology talent to reduce offshore dependency.
Looking ahead, executives expect remote monitoring, virtual-first care, digital twins, patient peer networks, robotic surgery and real-time command centers to grow sharply, shifting engagement toward integrated, predictive care systems.
KPMG’s conclusion is that the constraint on AI is no longer algorithms but operating models. It recommends five actions: define a target operating model, strengthen data foundations, embed security and ethics in AI development, build an AI-fluent workforce, and pursue strategic ecosystem partnerships.
Source: https://assets.kpmg.com/content/dam/kpmgsites/xx/pdf/2026/05/global-tech-report-2026-healthcare.pdf
20 Key Points
Three technology modes — Organisations sit in one of three stages: stabilising (fixing core platforms, data integrity), synchronising (connecting systems, standardising governance), or scaling (embedding AI across workflows at enterprise level).
Heavy investment — 40% of healthcare organizations spend $50–100 million a year on digital technology; a further 36% spend $100 million or more.
Spend is rising fast — In Australia investment is growing around 25% a year, with similar patterns in other countries, as healthcare catches up with other sectors.
ROI is still weak — 57% report ROI of 91–100% (i.e. below breakeven), though most reach breakeven after roughly 12 months and 30% exceed their initial investment.
Foundations deliver half the value — EHR, ERP and cloud platforms account for around 50% of digital value, while 45% of executives say AI and intelligent technologies contribute a significant share (31–40%) of total value.
AI adoption has doubled — 66% are actively deploying AI use cases, up from 32% a year earlier.
Highest AI scaling ambition of any sector — 76% expect to deploy AI at scale within 12 months, and 86% are embedding AI into workflows, services and value streams.
Top AI barriers — Cybersecurity, unreliable data/hallucinations, and regulatory/compliance risk are the three biggest concerns.
Fast followers, not pioneers — 55% describe themselves as fast followers vs 35% early adopters, among the lowest early-adopter rates of any sector; 44% wait for proven tech, 33% adopt selectively, and only 23% embrace disruptive tech at scale.
Long-term investment mindset — 90% take a long-term rather than reactive approach to technology investment.
Volatile environment — 58% say they are often or frequently affected by market, regulatory or technology shifts; regulation (data privacy, data sovereignty, emerging AI rules) is a major barrier, with some AI vendors avoiding the UK/EU over regulatory burden.
Governance gaps are the biggest challenge — 42% cite weak internal governance and limited in-house expertise, leading to fragmented decision-making and slow execution.
Centralised vs federated is an even split — Across decision areas (vendor selection, talent, architecture, investment prioritisation) roughly 34–42% are fully centralised and 37–45% federated; KPMG argues the right blend is key, with whole-system change needing more centralisation.
Legacy systems and pilot sprawl hold back ROI — Too many small proofs-of-concept that don’t scale; the recommendation is to modernise core platforms, connect them through the data layer, and pursue whole-system change via a target operating model.
The constraint is operating models, not algorithms — AI delivers value only where it is embedded in workflows and governed with intent, built on an operating model, governance model and mature data model.
Traditional KPIs don’t capture AI value — 69% agree conventional KPIs are insufficient, since AI outcomes are distributed and show up in different places at different times.
Data priorities — Data-powered forecasting (predictive analytics) is the top data priority, followed closely by data security, better analytics and enterprise-wide accessibility; healthcare generates roughly one-seventh of global data but uses only a tiny fraction.
Cybersecurity leads spending growth — 41% plan to raise cyber spend by over 10% next year, ahead of AI (34%) and data & analytics (34%); business continuity plans must ensure minimum patient information access even when systems fail.
Talent localisation — 29% plan to hire more onshore technology talent to reduce offshore dependency and geopolitical risk; the workforce strategy should focus on up-skilling and AI fluency.
Future care models — Remote monitoring and virtual-first care, digital twins, patient peer networks, robotic surgery and real-time command centres are all expected to surge, shifting engagement from digital touch points to integrated, predictive care, contingent on resilient infrastructure and uninterrupted connectivity.
KPMG’s five recommended actions: define a target operating model; strengthen data foundations; put security (plus trust and ethics) at the heart of AI development; build an agent-empowered workforce and pursue strategic ecosystem partnerships.
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