Google Cloud's second annual report
Google Cloud surveyed 1,402 IT leaders worldwide and found that 83% must upgrade their infrastructure to run agentic AI in production. Here's what the data means for your stack.
The Infrastructure for Agentic AI
Agentic AI doesn't just think, it acts, and that shift is breaking infrastructure built for a slower, human-paced web. Google Cloud's second annual report surveyed 1,402 senior IT leaders across 12 countries to map what a production-ready foundation for autonomous agents really looks like.
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Where Legacy Infrastructure Breaks Down
The readiness gap
83% of organizations say their infrastructure needs upgrades to run agentic AI in production, and 12% require a rebuild.
The governance bottleneck
79% of tech leaders name security, governance, and MLOps as the top barrier to scaling inference safely.
The hidden cost
62% of leaders pay a costly inference tax, driven by data egress fees, storage bloat, and idle specialized hardware.
The Numbers Behind the Shift
1
83%
of organizations require infrastructure upgrades for production-grade agentic AI
2
52%
now run hybrid multicloud architecture, up from 41% a year ago
3
91%
of leaders now factor power consumption into hardware decisions
Agentic AI needs a new infrastructure standard
Inference now accounts for 47% of all AI workloads, officially overtaking training. A single agentic prompt can trigger hundreds of downstream actions across systems that were never built to talk to each other, which is why 83% of organizations say their current stack falls short. Closing that gap means unifying data lakes, compute, and AI tooling on a single, AI-ready cloud platform instead of patching legacy systems together.
Governance is the price of admission for scaling
79% of tech leaders cite security, governance, and MLOps as their biggest obstacle to scaling inference, and 35% point specifically to insufficient security for multi-system access. Agents need broad access to be useful, which also makes them a new attack surface for prompt injection and tool poisoning. Leading organizations are responding by centralizing control and observability instead of treating security as an afterthought.
Hybrid multicloud is now the default
Hybrid multicloud adoption jumped from 41% to 52% of organizations in a single year, as enterprises balance the raw power of the cloud for training with the speed of edge and on-premises environments for inference. Cost efficiency is a parallel driver: 96% of leaders now rate it as important to their AI infrastructure decisions, with operational complexity, egress fees, and idle hardware named as the top hidden costs.
What This Means for Your Data Stack
The organizations pulling ahead in the agentic era aren't the ones with the biggest models, they're the ones with the most resilient, governed, and cost-efficient infrastructure underneath them. That foundation starts with the same fundamentals Datakimia builds every day: a clean, centralized data warehouse, clear governance, and pipelines ready to feed the agents your business will run tomorrow.
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