Size matters less than the type of work. Good candidates are tasks that are repetitive, follow a recognizable pattern, and rely on information you already have. Examples include pulling data from documents, answering questions from your financial or operational systems, drafting routine replies for a person to review, and preparing a first pass at a forecast or variance explanation. We start by looking at where your team’s time goes, then decide whether AI, automation, or a process change fits best.

Mostly time from the people who own the process. Early on, we watch how the work is done today and ask questions. As we build, we need those same people to test what we produce and tell us where it falls short. We also need access to the relevant systems, scoped to what the work requires. How much time this takes depends on the process and how many people are involved, and we agree on it with you before we start.

We work in your environment, or in one you approve. Before connecting any AI service, we review its data-use and retention terms with you so you know how your information is handled. Access is limited to what the solution needs. We document the permissions and controls we set up and hand that documentation to you.

It depends on the problem. Options include Microsoft Copilot Studio, Azure AI Foundry, and Anthropic’s Claude models, and some solutions combine more than one. Some problems don’t need AI at all and are better handled with workflow automation or a system integration. We recommend tools after reviewing the process, your existing systems, your security requirements, and cost.