After visits, patients receive structured follow-ups, medication reminders, and secure communication for ongoing support. AI continuously monitors health data, alerting clinical teams about potential risks and helping prevent readmissions. The result is a more personalized, efficient, and human-centered healthcare experience.

AI transformation starts with data

Realizing this vision requires the healthcare ecosystem to address significant data challenges. The first challenge is the sheer volume of data: healthcare produces approximately 30% of global data, driven by sources such as electronic health records, advanced imaging technologies, and wearable devices. In addition to volume, a substantial amount of this data is isolated within legacy systems. Lastly, compliance oversight with rigorous regulatory requirements, such as HIPAA and GDPR, adds complexity to the path of integrating data for artificial intelligence.

Learning from the pacesetters

There is an emerging group of healthcare companies that are effectively navigating data challenges to transform with AI. According to the Enterprise AI Maturity Index, developed by ServiceNow and Oxford Economics, the average AI maturity score within the healthcare sector declined from 45 to 34 in 2025. Nonetheless, approximately 16% of organizations—designated as Pacesetters—are making significant progress. These Pacesetters view AI as a transformative resource; over one-quarter have already implemented agentic AI, and nearly half plan to do so in the coming year.

Demonstrating leadership in data utilization for AI

These healthcare Pacesetters are unlocking AI value by demonstrating data leadership:

Adopting a platform-based strategy. Pacesetters connect modern and legacy platforms to create efficient, AI-managed workflows for scheduling, coding, charting, billing, and reimbursement, while maintaining data security and integrity.Implementing robust governance protocols is essential. AI technologies used in healthcare are required to meet certification standards, complete audit and compliance processes, and follow privacy regulations. Pacesetters implement governance structures to assess risks, improve security practices, and maintain patient trust.Promoting transparency across the enterprise. The adoption of leading practices in AI governance involves creating centralized AI model registries that systematically catalogue transparent details concerning intent, ownership, operational costs, and lifecycle management. Evaluating business impact during pilot phases allows leadership to substantiate improvements in patient outcomes and organizational performance resulting from AI implementation.

The future is bright

Successful AI transformation necessitates sophisticated infrastructure, strong governance protocols, and organizational transparency. By adopting a comprehensive approach to AI integration, healthcare systems can improve operational efficiency and deliver care that is increasingly personalized, proactive, and attentive to individual patient needs.

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