What technology leaders need to ensure AI delivers (www.techradar.com)

Enterprises are being warned that vision and flashy pilots aren’t enough: Gartner predicts over 40% of agentic AI projects will be canceled by 2027, largely because organizations fail to treat AI as an enterprise application. The story argues technology leaders must achieve “operational readiness” — the ability to deploy, manage and scale AI beyond labs — or risk wasted investment and widening gaps between AI leaders and laggards as GenAI moves to agentic systems. Operational readiness means a repeatable, full‑stack platform spanning compute, storage, networking and accelerators (GPUs, HBM, other processors), plus integrated data services, security, governance and orchestration for VMs and containers across on‑prem, cloud and edge. Key technical implications include planning elastic capacity (scale up/down), cost predictability, latency-sensitive inference placement, energy/ESG impact, data sovereignty and strict access controls as agents read/generate data and act autonomously. LLM outputs may be non-deterministic, but the underlying infrastructure must be reliable and reproducible. In short, to turn pilots into production value, organizations need turnkey platforms that unify compute, data and governance so AI can be managed like any other mission‑critical enterprise app.