KPMG’s Global Tech Report signals a shift from experimental AI pilots to intentional scaling across healthcare. The core finding: organizations must pursue controlled acceleration that balances risk with speed to capture value while meeting rising demand for better access, equity, and outcomes.
Healthcare’s Strategic Stance on Tech Adoption
Healthcare leaders are moving from “early adopter” postures to fast follower strategies that favor longer-term investment over chasing the newest tool. That posture emphasizes predictable returns, compliance, and interoperability as selection criteria. It also means committed multi-year funding plans rather than one-off pilots that never scale.
Investment Shifts & ROI
KPMG highlights a necessary reframe: higher ROI comes from reshaping data foundations and operating models, not from accumulating small POCs. Investments should prioritize consolidated data platforms, common APIs, and governance that let clinical, operational, and patient data flow. Measured KPIs tied to throughput, cost to serve, and clinical impact turn pilots into business cases for enterprise rollouts.
AI Adoption: Rapid Growth & Opportunities
AI deployment is accelerating: 66% of healthcare executives report active AI use, a 32% year-on-year increase. Use cases span predictive care, revenue cycle automation, clinical decision support, remote monitoring, and personalized engagement. The most tangible near-term returns come from process automation that reduces cycle times and predictive models that guide resource allocation.
Overcoming Legacy Tech & Optimizing Workforce
Legacy systems and fragmented data remain primary barriers. AI-enabled automation can compress onboarding and administrative tasks so clinicians spend more time with patients. Practical steps include modular integrations, identity and role mapping to automate credentialing, and low-code automation to standardize repetitive workflows.
Charting the Strategic Path Forward
Controlled acceleration requires enterprise-level action. Start with a clear target state for data and APIs, tie pilots to measurable outcomes, and create cross-functional governance to unblock procurement and security reviews. Priority actions:
- Consolidate data into an interoperable platform with shared taxonomies
- Define pilot-to-scale criteria and ROI gates
- Adopt API-first and cloud migration for core systems
- Invest in workforce reskilling and automation for onboarding tasks
- Form vendor and academic partnerships to speed validated models
When leaders treat AI as a systems transformation rather than a series of experiments, they unlock measurable efficiency, faster patient access, and better outcomes across the enterprise.
