Dublin, July 20, 2026 (GLOBE NEWSWIRE) -- The "Edge AI Software Market - Global Forecast 2026-2032" report has been added to ResearchAndMarkets.com's offering. The Edge AI Software Market is projected to reach USD 3.12 Billion in 2026. It is expected to continue growing at a CAGR of 24.63%, reaching USD 11.86 Billion by 2032. Edge AI software is becoming a critical layer in digital infrastructure as organizations move artificial intelligence workloads closer to where data is generated. Instead of sending every sensor reading, image, voice command, or machine signal to centralized cloud environments, edge AI enables real-time inference, local decision-making, and intelligent automation on devices, gateways, industrial controllers, vehicles, cameras, and embedded systems. This shift is especially important for use cases requiring low latency, privacy preservation, bandwidth efficiency, operational resilience, and continuous availability in environments where network connectivity is limited or intermittent. The adoption of edge AI software is supported by verified technology and policy trends, including the expansion of 5G and private wireless networks, the growth of Internet of Things deployments, advances in compact AI accelerators, stronger data protection regulations, and increasing enterprise demand for automation across manufacturing, healthcare, energy, retail, transportation, smart cities, and defense. Edge AI software now includes model optimization, inference runtime, device orchestration, federated learning, computer vision analytics, predictive maintenance, anomaly detection, and secure lifecycle management. As AI becomes embedded into physical operations, the ability to deploy, monitor, update, and govern models at the edge is emerging as a decisive capability for digital transformation. Transformative Shifts in the Edge AI Software Landscape The edge AI software landscape is undergoing a structural shift from centralized analytics toward distributed intelligence. Historically, organizations relied on cloud-based AI pipelines to collect, process, and analyze data after transmission. Today, a growing portion of AI inference is moving to edge environments