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cloudzero

Blind Tokenmaxxing Is The New Cloud Waste. Focus on Outcome-Maxxing Instead

Apr 22, 2026 By David Aponovich In CloudZero
Meta's internal token leaderboard sparked a frenzy — and a reckoning. Tokenmaxxing without attribution is just cloud waste 2.0. Companies like Hudl and Duolingo use cost intelligence to connect every AI dollar to a business outcome.
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digitate

Why Enterprise AI Demands More Than Just Automation

Apr 22, 2026 By Digitate In Digitate
Based on insights from The Intelligent Enterprise podcast, “The Evolution from Automation to Autonomy” Every couple of weeks, The Intelligent Enterprise podcast steps away from the day-to-day noise of enterprise life to explore big ideas from a fresh perspective. In one recent episode, the focus turned to a question many organizations are still grappling with: What does it really take to build an AI-powered enterprise that works with people, not against them?
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Episode 10 - How I Learned to Stop Worrying and Love AI

Apr 22, 2026 By Digitate In Digitate
Are we still in the first chapter of AI, and mistaking it for the whole story? In this episode of The Intelligent Enterprise, host Tom Stoneman zooms out from the headlines to explore where we really are in the AI journey. He’s joined by journalist and independent analyst Joe McKendrick, who has spent decades documenting how emerging technologies reshape business and society. As co-chair of the AI Summit in New York and a senior contributor to Forbes and ZDNet, Joe brings the perspective of someone who understands how these stories unfold over time.
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ScienceLogic

The New Economics of Enterprise AI: Why Small Models Win Where It Matters

Apr 22, 2026 By ScienceLogic In ScienceLogic
For years, progress in AI was equated with scale. Larger models, broader parameter counts, and increasingly complex cloud architectures were treated as signals of advancement. In enterprise operations, however, scale alone does not determine success. Economics does. As AI becomes embedded in operational workflows, organizations are discovering that model size is less important than cost stability under continuous load. AI-driven operations do not run in bursts. They run constantly.
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The Regional Data Centre Revolution Powered by AI Demand

The Regional Data Centre Revolution Powered by AI Demand

Apr 21, 2026 By Pulsant In Pulsant
London still hosts the biggest concentration of UK data centre capacity, but the centre of gravity is starting to move. AI workloads are changing the infrastructure maths, pushing power, space and planning considerations up the decision list. That is exactly where regional locations start to look like the sensible option. Government data shows how concentrated the market remains: as of autumn 2024, London is estimated at 1,048MW of colocation IT load. Compare that with 44MW in the East of England, 17MW in the North East and 30MW in Scotland. The gap is huge, yet it is not a permanent advantage.
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grafana

Grafana Assistant everywhere: Customize and connect to the AI agent to fit your specific needs

Apr 21, 2026 By Maurice Rochau In Grafana
The ways you and your teams build and observe your systems are changing. It’s no longer just engineers looking at dashboards, or writing queries or config files. More often, it’s an agent interacting with the data, too, helping write code, run applications, investigate incidents, rightsize deployments, and more.
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grafana

AI Observability in Grafana Cloud: A complete solution for monitoring your agentic workloads

Apr 21, 2026 By Maurice Rochau In Grafana
The observability industry has developed great tools for using metrics, logs, traces, and profiles to monitor the cloud native applications that have dominated the last decade of software development. But when it comes to understanding what an AI system is actually doing, we’re often left reading raw conversations, guessing at quality, and reacting too late. And that’s a problem.
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grafana

Introducing o11y-bench: an open benchmark for AI agents running observability workflows

Apr 21, 2026 By Yasir Ekinci In Grafana
Evaluating agents is hard. Verifying observability tasks is harder. Yes, AI agents have gotten dramatically and quantifiably better at coding and tool use, but observability presents a different kind of challenge. In a real incident, the hard part is rarely just writing a query. It's deciding which signal matters, figuring out whether a spike is noise or symptom, correlating metrics with logs and traces, and sometimes making a change in Grafana without breaking the dashboard another engineer depends on.
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