What Infrastructure Does AI Actually Need? A Readiness Checklist

Most AI rollouts stall for infrastructure reasons, not model reasons: the network cannot move data fast enough, the security model was not built for a tool with broad data access, or nobody planned for the extra bandwidth and latency the tool actually needs. Run this checklist before you buy.

Network capacity and latency

AI tools, especially anything processing video, voice, or large documents in real time, are more bandwidth- and latency-sensitive than the SaaS tools most teams are used to. Confirm your circuits have headroom, not just enough for today's usage, and that redundancy actually covers an AI workload if the primary link drops.

Data access and where data actually lives

AI tools are only as good as what they can see, which means mapping exactly what data sources they will touch, where that data lives, and whether it is structured well enough to be useful. This is also the point where compliance requirements (HIPAA, PCI, industry-specific rules) need to be checked against the tool's data-handling practices, before rollout, not after.

Identity and access, tightened

An AI tool with broad read access to internal systems is a new, high-value target. Zero-trust access controls and tight identity management are not optional extras here, they are the baseline for giving any AI tool that much reach safely.

Buying the tool is the easy part

The infrastructure work, network capacity, data readiness, and security, usually takes longer than picking the AI vendor, and it is the part most demos never show you. Thomson advises on the AI and CX tools themselves and sources the connectivity, data-center, and security foundation underneath them, so the infrastructure is ready before the tool goes live, not scrambled together after.