Most AI projects that stall do so over data, not models. Before choosing a platform or sizing compute, a business should settle where its data may be processed, who may access it, how long logs are kept and which systems the model will connect to. We set out the questions to answer first, and why answering them early makes every later decision cheaper.
Before investing in AI compute, a small or medium business needs a clear view of its workloads, its data-handling obligations and its budget. We outline a staged approach: assess the workload, plan the resources, deploy, then operate.
Slow systems, frequent outages, unsupported software and a growing backlog of security patches are often the first signs that infrastructure has fallen behind the business. We look at the common warning signs and how to decide what to fix first.