It's Time to Scale AI According To KPMG
KPMG's 2026 Global Tech Report confirms what healthcare CIOs already know: the window for cautious pilots is closing fast.
I’ve sat in enough boardrooms to recognize the pattern. We celebrate a successful AI pilot. Clinical leadership nods. And then — nothing scales. The pilot lives on a slide deck, and we move on to the next proof of concept.
KPMG’s 2026 Global Tech Report on Healthcare, drawn from 128 senior executives globally, is a mirror held up to that pattern. The reflection isn’t entirely flattering — but it does point clearly to what comes next.
Investment Is Serious. ROI Is Not.
Forty percent of healthcare organizations are spending $50–100 million annually on digital technologies, and that figure is climbing at roughly 25 percent per year. AI adoption has more than doubled in a single year — 66 percent of executives report actively deploying use cases, up from 32 percent. And 76 percent expect enterprise-scale AI deployment within 12 months, the highest of any sector surveyed.
So why are 57 percent of organizations still reporting ROI below breakeven? Because the technology isn’t the constraint. The algorithms are ready. The platforms are maturing. The problem is that we keep deploying technology as an extension of existing structures rather than as a catalyst for fundamental change. We’ve modernized the tools without modernizing the system.
Fix the Data Foundation First
Healthcare accounts for roughly one-seventh of all global data, yet only a fraction is actively used for insight. Most of our data teams are built around static dashboards that tell us what happened — not what’s about to happen. The KPMG report ranks data-powered forecasting as the top organizational priority, and for good reason: AI cannot succeed without mature data practices. That’s not a vendor talking point. It’s the lived experience of every health system that has tried to scale a model trained on siloed, inconsistent data.
Data governance is a leadership issue, not an IT issue. Clear ownership and accountability for how data is managed across the enterprise must sit at the CIO level — and it must be resolved before the AI strategy is announced, not after it underperforms.
Cybersecurity Leads, Not Follows
Forty-one percent of healthcare leaders plan to increase cybersecurity spending by more than 10 percent this year — placing it ahead of both AI and data analytics in growth priority. That sequencing is right. In a digital-first healthcare environment, resilience is clinical as much as technical. When systems fail — and they will — staff must still access minimum patient information to deliver safe care. Business continuity is a patient safety issue. Every CIO should be treating it accordingly.
Stop Measuring AI With ERP Metrics
Sixty-nine percent of executives agree that traditional KPIs are insufficient for tracking AI performance. Binary, linear metrics don’t capture how AI value actually shows up — distributed across downstream workflows, patient experience, staff retention, and throughput. We need governance frameworks built for AI’s non-linear impact. Building those frameworks proactively, before a high-profile failure forces the conversation, is one of the highest-leverage moves available to health system CIOs right now.
The Mandate: Stabilize, Synchronize, Scale — In That Order
The most useful insight from the KPMG report is the progression it describes: stabilize your core platforms, synchronize your data and governance, then scale AI into that ready foundation. The temptation is to skip ahead — to launch a patient-facing AI product while EHR integration remains fragmented. That path produces exactly the ROI disappointment the data describes.
The organizations that will look back in three years with confidence are those building deliberately today: aligning governance, defining a target operating model fit for the AI-enabled organization, and scaling into that model — rather than retrofitting a strategy onto a collection of pilots.