Michael Gerstenhaber, Vice President of Product at Google Cloud, outlined how enterprises are approaching artificial intelligence through the company’s Vertex AI platform, emphasizing that real-world adoption depends on balancing capability, latency, and deployment cost. Vertex AI serves primarily as a developer platform, enabling organizations to build domain-specific applications using advanced models rather than delivering turnkey solutions.
Gerstenhaber noted that companies deploying AI at scale must optimize across three constraints: raw intelligence for complex tasks, rapid response times for user-facing applications, and affordability for high-volume operations. He also highlighted infrastructure gaps slowing adoption of agentic systems, including governance, auditing, and data authorization frameworks. Google’s vertically integrated stack — from chips and data centers to models and interfaces — positions it to address these challenges as enterprises move from experimentation to production AI systems.
