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What Deloitte's 2026 Global Health Care Outlook reveals about the automation bet health systems are making in 2026.
A single statistic tells you almost everything about where health system leaders are placing their bets for 2026: 64%.
That is the share of executives who told Deloitte they expect AI to cut costs by standardising and automating workflows next year, more than any other savings lever on the table. For an industry that spent the last few years talking about AI in the abstract, that is a striking shift toward something far more concrete: process automation that touches how a hospital, health plan, or clinic runs day to day.
Deloitte's 2026 Global Health Care Outlook, based on a survey of 180 C-suite executives across Australia, Canada, Germany, the Netherlands, the UK and the US, paints a picture of cautious optimism shadowed by three persistent pressures: financial performance, workforce shortages and cybersecurity risk. Around 70% of non-US health system executives expect operating revenue and margins to rise in 2026, but that confidence rests on finding real efficiencies, not just hoping the numbers improve on their own.
When Deloitte asked where those savings would come from, standardising and automating workflows through AI came out on top at 64%, ahead of predictive analytics for workforce optimisation (55%) and tech-enabled patient engagement and remote monitoring (49%). In other words, health leaders are not waiting for a single AI breakthrough. They are looking at the unglamorous, repetitive processes buried inside claims, scheduling, intake and administration, and asking whether automation can finally sort them out.
The case for automation as the top 2026 priority isn't about chasing a trend. It is about arithmetic. Manual, inconsistent workflows are expensive precisely because they are inconsistent: every variation in how a claim is processed, a referral is logged or a patient record is updated adds cost, delay and risk of error. Standardising those processes through automation removes that variability at scale, which is exactly why it edges out even AI-driven workforce optimisation in Deloitte's survey.
Crucially, this argument holds regardless of whether an organisation is chasing gen AI headlines or not. As Deloitte puts it, health systems are focused on a holistic approach to margin improvement that spans strategic growth, cost reduction and efficient capital deployment rather than tackling each in isolation, and standardised, automated workflows sit at the intersection of all three.
For regulated businesses in particular, this is a familiar argument from adjacent sectors: insurance and financial services firms have spent the past few years discovering that compliant, rules-based conversational automation can resolve high-value processes, like policy updates or benefit queries, far faster than a web form or a call centre queue, without sacrificing the auditability that regulators demand.
Health systems chasing the same 64% opportunity face an almost identical design question: how do you automate a workflow so it is faster and cheaper, while keeping every step defensible to a regulator, an auditor or a patient who wants to know exactly what happened to their data.
Yet it would be a mistake to read that 64% figure as evidence that AI-driven automation is already delivering for health systems. Deloitte's own data offers a useful reality check: only around 30% of surveyed health systems report operating gen AI at scale in even select areas of their organisation, and just 2% have deployed it enterprise-wide.
More tellingly, 51% of respondents said they either had not measured returns on their AI investments or felt it was too soon to tell, with only 3% reporting significant financial returns so far. Regulatory uncertainty compounds the caution. The EU AI Act now requires most AI-enabled medical devices and decision-support tools to undergo mandatory risk management review, while countries such as Canada and Australia are still working out their own frameworks. Deloitte explicitly warns health systems against the “pilot trap”: launching proofs of concept that never scale because the underlying infrastructure and governance were never built for enterprise use.
That is a fair challenge to the optimism behind the 64% figure. Wanting to standardise workflows through automation is not the same as having the compliant, auditable architecture in place to do it safely at scale, particularly in a sector handling some of the most sensitive personal data.
The direction of travel for 2026 is not really in dispute. Health leaders have told Deloitte, clearly and consistently, that standardising and automating workflows is their single biggest expected source of savings next year, and that ambition is entirely reasonable given the financial and workforce pressures the sector is under.
The harder question, and the one that will separate the organisations that actually capture that value from those still counting pilot projects in twelve months, is whether automation is being built as durable, governed infrastructure from day one, or bolted on as another experiment. Deloitte's own advice points the same way: develop an enterprise-wide AI strategy with clear ownership, align it with governance from the outset, and avoid mistaking a successful pilot for a scalable system.
For health leaders in regulated markets, that means treating compliance, auditability and core system integration as part of the automation design itself, not an afterthought layered on once something already works in a demo. Get that right, and the 64% who are betting on automation this year will have something real to show for it in 2027.
What percentage of health leaders expect AI to reduce costs in 2026? According to Deloitte's 2026 Global Health Care Outlook, 64% of surveyed health system executives expect AI to reduce costs by standardising and automating workflows, the single largest expected source of savings for the year ahead.
Are health systems actually using AI at scale yet? Not widely. Deloitte found that only about 30% of surveyed health systems operate generative AI at scale in select areas, and just 2% have deployed it across their entire enterprise, reflecting how early adoption still is.
What is the biggest risk in health care automation for 2026? Deloitte highlights the “pilot trap”: organisations launch automation and AI proofs of concept that never scale because they never built the infrastructure, governance, and compliance architecture needed for enterprise-wide use from the outset.