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Distributed futures: the Spectro Cloud State of Edge AI
AI isn’t just living in the cloud or data center. It’s moving to where data is created — factory floors, hospitals, stores, and vehicles. But how far have organizations really come in bringing AI to the edge? Our new research reveals the edge AI trends you need to know about.
Discover edge AI trends
Our new State of Edge AI report cuts through the hype with data from 320 enterprise professionals already building edge AI projects.
Inside, you’ll discover the key trends in edge AI: what’s driving adoption, what’s holding it back, and what separates the pioneers from the pack.
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Key findings
75% of organizations have been working on edge AI for two years or less.
Most run just 2–3 initiatives, and only 11% have reached full-scale production.
Top use cases: predictive maintenance, real-time personalization, and edge cybersecurity.
6 in 10 organizations increased their edge AI budget this year.
72% say they can demonstrate business value to leadership
43% feel pressured to “do edge AI,” and 42% have killed projects that never made it beyond pilot.
31% have suffered core service disruptions due to edge AI.
The most desired capability for success: consistent, standardized deployments across devices.
52% already use edge Kubernetes to orchestrate AI.
Kubernetes adopters are 2× more likely to have reached full scaled edge AI, and are 20 points more confident in showing business value.
Who is this report for?
If you’re leading or supporting AI projects at the edge — in manufacturing, healthcare, financial services, or technology — this report gives you the benchmarks, pitfalls, and proven strategies your peers are using to operationalize AI in real-world environments. We cover:
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FAQs
It’s an independent research study based on a survey of 320 enterprise professionals running real edge AI projects. The report explores adoption trends, challenges, use cases, and technologies powering AI at the edge.
Anyone involved in developing, deploying, or managing AI workloads outside the cloud — especially platform engineers, IT leaders, and data science teams working in industries like manufacturing, healthcare, and financial services.
You’ll discover how fast edge AI is growing, where organizations are investing, the biggest barriers to scaling, and why Kubernetes users are pulling ahead. It’s packed with data, charts, and insights to help benchmark your progress.
Yes. The full State of Edge AI report is available to download for free. No paywall, no obligation, just insights.
The two studies complement each other. The State of Edge AI focuses on AI at the edge, while The State of Production Kubernetes explores how organizations manage cloud-native infrastructure at scale — including the Kubernetes foundation that makes edge AI possible.
Put simply, it’s running AI workloads outside of the data center or cloud. ‘Edge’ means many things, from a small form-factor device like a NUC or RPi in a remote location, to branch office and micro data centers in the telco or enterprise edge. But the challenges are the same: large distributed scale, limited access. Edge is a popular choice for AI workloads because AI often demands low latency for real-time inference and autonomy.
Still have questions?
Reach out to our team or book a meeting to discuss your edge AI use case. We’d be happy to help, wherever you are in your journey.