AI is strategically embedded in core IT and infrastructure spending across industries. In fact, 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy, according to ZEDEDA’s 2026 Edge AI Survey, conducted by Censuswide.
“Edge AI has officially crossed the threshold from experimentation to essential infrastructure,” says Said Ouissal, ZEDEDA’s CEO and founder. “What we’re seeing is a clear signal that enterprises understand that AI must operate where data is generated. The next phase isn’t about proving value, it’s about scaling it across distributed environments and bringing agentic-powered intelligence where it matters most for these enterprises, at the edge.”
Key takeaways:
· Half of respondents (50%) are actively researching how edge AI agents can manage goals rather than simply process inputs, 21% are piloting edge agents that autonomously execute multi-step tasks, and 15% have deployed autonomous edge agents in production with minimal human intervention. In total, 86% of enterprises with active edge AI deployments are pursuing agentic edge capabilities.
· Half of respondents measure or plan to measure edge AI initiatives through operational efficiency gains, followed by cost reduction (45%) and safety and risk reduction (42%). That demonstrated impact is reshaping how organizations fund edge AI. And, 30% now allocate edge AI spending through IT and infrastructure budgets, compared with 18% from innovation or pilot programs.
· Enterprises are increasingly distributing AI workloads across cloud and edge environments, with 47% reporting a hybrid cloud-edge architecture. While training remains largely centralized, inference is shifting to the edge as organizations seek faster decision-making closer to the point of operation. Only 24% of respondents rely primarily on centralized cloud or data center infrastructure, a sign that the gravity of AI execution is shifting to the edge.
· Customer experience optimization (45%) and computer vision (45%) lead enterprise edge AI deployments currently in production, followed closely by real-time monitoring and anomaly detection (41%), energy optimization (40%) and predictive maintenance (38%).
· Integration with existing systems leads the list of barriers at 34%, followed by security and governance concerns (32%) and lack of internal expertise (31%). Security worries are particularly acute in distributed environments, where organizations must manage data sovereignty across endpoints, ensure model integrity outside the data center, and maintain consistent access controls across heterogeneous hardware. Overall, 41% of organizations with active deployments describe managing AI workloads across distributed environments as challenging, with U.S. enterprises reporting greater difficulty than their German counterparts.
