As AI adoption accelerates across industries, enterprises are confronting a critical question: where should AI run?
While the public cloud has been foundational to the early growth of artificial intelligence, we’re entering a new phase—one where enterprise needs are increasingly defined by constraints the cloud alone can’t solve. That’s where edge AI comes in.
New research from STL Partners puts numbers—and strategic clarity—behind this shift. And it echoes what we’re seeing firsthand across industries: When it comes to AI, the edge is no longer optional. It’s essential infrastructure.
Beyond the Cloud: Meeting Edge Demands
Cloud-based AI has undeniable advantages: scalability, availability, and access to powerful compute infrastructure, to name a few. So, why not run all AI in the cloud? According to STL Partners, there are two main reasons why enterprises are bringing AI capabilities to the edge:
- Cost and bandwidth constraints, especially when processing large volumes of data like video streams. Moving that data to the cloud and back introduces significant expense and latency—and often disrupts other business-critical operations.
- Data sensitivity and sovereignty When proprietary or regulated data is involved, organizations often want to keep it local—either due to regulatory requirements, competitive concerns, or a need for tighter control.
STL’s findings align with what we’ve seen in the field. Industries like manufacturing, transportation, energy, and retail are actively adopting AI—but increasingly hitting limits with cloud-based architectures. These “top-right quadrant” sectors (as STL dubs them) are where cloud struggles to meet performance and data handling requirements, yet where AI adoption is happening aggressively.
It’s not just about technical constraints. These are operational realities—where machines operate in harsh environments, connectivity is unreliable, and milliseconds matter.
Real-World Edge AI: From Concept to Impact
At ZEDEDA, we’re helping customers process data directly at the wellsite—dramatically reducing the need to transport large volumes of telemetry or video back to the cloud. This approach slashes bandwidth costs, improves responsiveness, and keeps sensitive data close to the source. In remote environments where it may take hours by helicopter to reach the site, this kind of localized intelligence isn’t just efficient—it’s game changing.
STL highlights a similar shift across sectors. Edge AI shines in use cases like:
- Computer vision and video inferencing
- Real-time quality control on production lines
- Security and access monitoring at distributed retail sites
- Fine-tuning models based on hyperlocal data (like water temperature and chemical mix in industrial settings)
According to STL’s forecast, these computer vision-based workloads will account for nearly 50% of AI revenues by 2030, with manufacturing, transport, and retail driving the bulk of that growth. These use cases are not just “edge-friendly”—they’re edge-dependent.
<< Discover how ZEDEDA is partnering with SLB to bring AI and IoT to the world’s most remote oil and gas environments. >>
The Future of AI is at the Edge
Across industries, we’re seeing a growing recognition that edge environments are where AI delivers its greatest value—especially in places where connectivity is limited, real-time responsiveness is essential, and data sovereignty matters.
Edge AI enables smarter decisions, faster responses, and safer operations in places where cloud-based tools simply can’t function reliably. That’s especially critical as AI and robotics become more integrated into physical AI, where immediate processing is essential to enable autonomous machines.
The Hybrid Future: Cloud and Edge in Tandem
As enterprises expand their use of AI, they’re learning that no single infrastructure model fits every use case. And the cloud isn’t going away—far from it. It remains critical for centralized model training, long-term data storage, and large-scale analytics. But increasingly, the edge is where inference and real-time decision-making need to happen.
We’re seeing the rise of a hybrid model—with training, inference, and orchestration distributed across cloud and edge environments based on workload requirements. According to our own CIO survey, 54% of enterprises see edge as a complement—not a replacement—for the cloud.
But getting this balance right isn’t easy. Talent gaps, integration complexity, and a lack of purpose-built tools remain major hurdles. That’s why platforms that simplify orchestration and management across distributed environments—like ZEDEDA’s integration with NVIDIA’s edge AI stack—are becoming vital.
A Strategic Imperative
Edge AI is no longer theoretical—it’s being deployed and scaled, and it’s delivering real value in the field. And it’s evolving. From STL’s market projections to ZEDEDA’s own customer deployments, the evidence is clear: enterprises need edge AI to unlock use cases the cloud alone can’t support. As infrastructure matures and adoption grows, edge computing is becoming an essential layer in modern enterprise architecture.
For industries operating at the intersection of physical and digital—like energy, manufacturing, transportation, and retail— edge AI is already solving problems the cloud can’t reach. And as these environments grow more connected and compute-intensive, architectures that are distributed, intelligent, and hybrid won’t just be ideal—they’ll be required.
To learn more about the enterprise drivers and use cases behind edge AI adoption, watch STL Partners’ presentation: Edge AI: The $100 Billion+ Opportunity with Tilly Gilbert.
