“When we transitioned to real-time predictive AI, the cost and latency of shipping raw time-series data to the cloud became a bottleneck,” Kale says. “By shifting feature extraction and anomaly detection to the customer’s local collector and sending only high-level risk signals to the cloud, we reduced egress dramatically while improving model fidelity.”
In all instances, “AI made the architectural trade-offs clear: Specific workloads benefit from public-cloud elasticity, but the most sensitive, data-intensive, and latency-critical AI functions need to run closer to the data,” Kale says. “For us, cloud-smart has become less about repatriation and more about aligning data gravity, privacy boundaries, and inference economics with the right control plane.”
A less expensive execution path
Like P&G, World Insurance Associates believes cloud-smart translates to implementing a FinOps framework. CIO Michael Corrigan says that means having an optimized, consistent build for virtual machines based on the business use case, and understanding how much storage and compute is required.
