Enterprise AI projects tend to start at the top of the stack — a chatbot, an assistant, an agent — and run into trouble a few layers down: data that can’t be found, access rules nobody wrote down, and a model endpoint nobody is on call for.
The five layers
Business workflows: the enterprise use cases the system exists to serve. Applications: assistants, agents and search. Models: public, private or fine-tuned. AI platform: compute, serving and access. Enterprise data: sources, pipelines and retrieval.
Where the work actually is
The model is the easiest layer to swap. The hard, durable work sits beneath it: connecting enterprise data through reliable pipelines and retrieval; running a platform — private, public or hybrid — with the right access controls; and operating it like any other production system, with monitoring, cost visibility and incident response.
Start with one workflow
Pick one business workflow with a clear owner and measurable outcome. Build the data and platform foundations it needs, with governance from day one. The second use case then reuses those foundations instead of starting over.