What Google Cloud Next Told Me About the State of AI Analytics

I just got back from Google Cloud Next. 40,000 attendees. 900 sessions.

The agent debate is settled.

No one at the conference was debating whether AI agents are the future. That conversation ended sometime last year.

The conversation that replaced it is messier: how do you build agents that work in production, not just in demos? How do you maintain them? How do you know what they're doing?

Prototypes are easy. Every serious buyer in that room already knows this. The gap between a compelling demo and a system that a business team actually relies on is where things get difficult.

Three concerns the market has

These came up across sessions, hallway conversations, and customer meetings. They are not edge cases. They are becoming the default worry for anyone building or buying AI in the enterprise.

• Agent chaos. Too many agents, no clear orchestration, no one owns the outcome.
• Visibility. When AI generates an answer, buyers want to know: on what data, with what logic, approved by whom?
• Maintainability. What happens to your AI workflow when the person who built it moves on?

These are showing up in conversations right now. Teams that have an answer to them have a clear advantage over teams that cannot.

"Context is king," - but it means different things to different people

This phrase was repeated in nearly every session. The consensus is real. Clarity is lagging.

The teams getting it right treat context as something that needs to be owned and governed across structured and unstructured sources and available to multiple AI and human use cases. Getting context right is the difference between an AI answer you can act on and one you have to second-guess.

The analyst profession is changing

Analysts at the conference were saying it themselves. The expectation from business stakeholders has shifted - the analyst in 2026 is a manager of AI agents.

Top-level KPIs are still locked in. Dashboards and slide decks are not going away. But the days are gone when one would agonize over getting a dashboard or slide deck just right. It is about the key takeaways, the actions you take after seeing the insights.

The bottom line

Production reliability matters more than feature count. Governed outputs matter more than clever interfaces. The organizations that build a trusted, governed layer of context across their sources will have an advantage.

That's where the market is heading.

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Orion by Gravity is an enterprise-grade AI analyst that turns governed data into decision-ready outputs. Book a working session to see it in your workflow.