Most IT leaders have discovered that production AI is significantly harder than early experimentation suggested. The real work begins not when a model performs well in isolation, but when it must operate inside environments that are secure, observable, and operationally durable.
Recent research my company conducted with enterprise cloud architects and IT decision-makers confirms what many engineering teams already know instinctively: experimentation is easy. Operationalizing AI reliably, repeatedly, and at scale is the hard part.
Once AI begins influencing real workflows, recommending decisions or triggering actions, the model quickly becomes the least interesting part of the system. The pressure shifts to everything around it.
Agentic AI is scaling faster than the environment around it
The data leaves little room for debate: AI has already moved into operational territory. Nearly three-quarters of respondents report actively training machine learning models, and 76% are running GPU workloads in production. More than 70% are investing in AI reasoning, decision optimization and AI assistants designed to execute tasks.
