Hospitals' AI Success Depends on Training Staff, Not Just Technology
Hospital artificial intelligence investments will fail without workforce preparation, according to Dr. Guido Giunti, chief data officer at St. James's Hospital Dublin, who spoke at the 2026 HIMSS Global Health Conference in Las Vegas last week.
The real bottleneck isn't capability-it's people. Hospitals need digital literacy programs, governance frameworks, and leadership alignment to turn AI tools into measurable operational gains, Giunti said.
The Scale of the Challenge
St. James's Hospital, Ireland's largest academic teaching hospital, operates 1,000 beds with 5,400 staff. Its technology infrastructure includes 12,000 devices, 4,000 PCs, 300+ servers, and 220 IT services-supported by just 54 IT staff members.
Most hospital leaders face a stark choice: hire another nurse or another engineer. "If senior management has to decide between one more nurse or one more engineer, they will pick the nurse," Giunti said.
That's before factoring in regulatory complexity, interoperability failures, inconsistent data quality, and aging systems that many European healthcare organizations navigate simultaneously.
Building Digital Literacy From the Ground Up
St. James's Hospital launched an AI literacy program with awareness campaigns, hands-on "AI clinics," structured training pathways, and designated AI champions in each department. The goal is baseline digital literacy across clinical and non-clinical staff.
Educating staff has a secondary benefit: clinicians and administrators can ask better questions when evaluating new technologies. They understand what vendors should support-IoT integration, clinical-grade accuracy in tools like ambient documentation systems.
"Let's create a shared understanding between all of our stakeholders," Giunti said.
Frameworks for Measuring Progress
Giunti highlighted the Adoption Model for Analytics Maturity (AMAM), a HIMSS framework that evaluates how effectively health systems use data to guide decisions. The seven-level model maps a path from basic analytics to mature, evidence-driven operations.
The SUSA Consortium project takes a different approach, closing digital skills gaps through interdisciplinary training. Its 20 learning objectives cover data science, analytics, AI, cybersecurity, healthcare IT architecture, regulation, and global health.
Why Trust Matters
Structured training programs build confidence among users. Without that trust, even solid technologies fail in real environments.
"To create an empowered workforce, you need to make it simple," Giunti said. "And we need to prepare for the long run - it's not a sprint, it's a marathon."
These conversations take time. But they're essential for moving beyond pilot projects to sustained operational change.
Learn more about AI for Healthcare and AI Data Analysis to understand how these frameworks apply across clinical settings.
Your membership also unlocks:
