EDGE COMPUTING
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AI workload placement in hybrid infrastructures
AI’ s new frontier – and its new threats No force is accelerating the shift toward edge computing faster than AI. The industry is rapidly pivoting from centralised model training to distributed AI inferencing – running small language models( SLMs) and real-time computer vision algorithms locally on remote hardware.
Yet, as AI reshapes the edge, placing workloads at the wrong architectural layer remains one of the most common – and costly – mistakes enterprise leaders make.
“ The layer you choose sets your risk profile, your cost structure, and your ability to operate at scale,” Séamus warns.
“ Put something too far out and you struggle to secure and maintain it. Keep it too centralised and you lose the latency and resilience benefits that justified the project in the first place.”
This transition amplifies every existing risk. Deploying proprietary AI models to remote edge nodes places valuable intellectual property on hardware exposed to theft or tampering. To navigate this safely, Séamus points to three non-negotiable pillars that must guide executive decision-making.
First is security. Every edge location is a potential entry point into the enterprise network. AI elevates both the value of local data and the
100 October 2026