Edge AI software market to hit $3.12 billion in 2026

The decentralization of intelligence marks a critical shift for streaming infrastructure, moving processing from the core to the network edge. For video-heavy applications, this enables real-time computer vision and privacy-compliant surveillance without saturating backhaul bandwidth. As enterprises look to minimize latency for mission-critical automation, the software layer—including lightweight inference runtimes and federated learning—becomes the competitive differentiator. Watch for the convergence of 5G and dedicated AI silicon to further lower the barrier for high-throughput video analytics at the endpoint.

The transition toward distributed intelligence is gaining momentum as hardware providers optimize for local execution. In late 2025, Intel released OpenVINO 2025.4, an edge-centric update designed to broaden model support for local AI deployments. This was followed by the 2026 introduction of the Intel Core Ultra Series 3, a platform built on the advanced 18A semiconductor process. This hardware specifically targets high-throughput performance for AI and video analytics, delivering nearly 180 TOPS in a single SoC to enable visual language model (VLM) workloads directly on edge devices, according to Intel reporting from early 2026.

Regulatory pressures are also funneling investment toward edge-based processing to ensure data sovereignty. The European Union’s AI Act, which enters full enforcement in 2026 per European Commission guidelines, classifies real-time biometric identification in public spaces as high-risk or prohibited, depending on the application. This legal framework forces surveillance and video platform providers to adopt privacy-aware architectures. By processing video feeds locally and only transmitting metadata or anonymized insights, companies can align with stringent GDPR and AI Act mandates while reducing the risk of large-scale data breaches associated with centralized cloud storage.

Market analysis from Gartner in July 2026 supports this trajectory, projecting that worldwide end-user spending on AI models and platforms will reach $64 billion in 2026, with specialized generative AI models growing by 210%. As enterprise AI budgets face more scrutiny, spending is shifting toward tools that offer cost transparency and usage efficiency. Companies like Bosch and Schneider Electric are already integrating these edge capabilities to manage industrial robotics and energy optimization, using localized intelligence to ensure operational continuity even when network connectivity is intermittent.