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Among the numerous educational and startlingly insightful panel discussions on AI enterprise integrations featuring industry leaders at VentureBeat’s Transform 2025 conference this week was one led by Google Cloud Platform Vice President and Chief Technology Officer (CTO) Will Grannis and Richard Clarke, Highmark Health’s Senior Vice President and Chief Data and Analytics Officer.
That session, “The New AI Stack in Healthcare: Architecting for Multi-Model, Multi-Modal Environments,” delivered a pragmatic look at how the two organizations are collaborating to deploy AI at scale across more than 14,000 employees at the large U.S. healthcare system Highmark Health (based out of Western Pennsylvania).
In addition, the collaboration has onboarded all these employees and turned them into active users without losing sight of complexity, regulation, or clinician trust.
So, how did Google Cloud and Highmark go about it? Read on to find out.
A Partnership Built on Prepared Foundations
Highmark Health, an integrated payer-provider system serving over 6 million members, is using Google Cloud’s AI models and infrastructure to modernize legacy systems, boost internal efficiency, and improve patient outcomes.
What sets this initiative apart is its focus on platform engineering—treating AI as a foundational shift in how work gets done, not just another tech layer.
Richard Clarke, Highmark’s Chief Data and Analytics Officer, emphasized the importance of building flexible infrastructure early. “There’s nothing more legacy than an employment platform coded in COBOL,” Clarke noted, but Highmark has integrated even those systems with cloud-based AI models. The result: up to 90% workload replication without systemic disruption, enabling smoother transitions and real-time insights into complex administrative processes.
Google Cloud CTO Will Grannis echoed that success begins with groundwork. “This may take three, four, five years,” he said, “but if your data is ready, you can run the experimentation loops and evaluations that make AI useful at scale.”
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