Most businesses know AI matters. Fewer know where it actually belongs. The problem is not a lack of tools. It is a lack of clarity around the work itself: the roles, workflows, decisions, data, handoffs, and repetitive tasks that make the business run.
My advisory work helps leaders separate signal from noise. We look at how work actually happens inside the company, identify where AI can create real leverage, and build a practical roadmap for adoption without turning the business into an experiment. The model is simple: understand the work, find the leverage, and build systems that make the company better.
The goal is not to chase AI. The goal is to redesign work intelligently.
Not every AI initiative is worth pursuing. Not every process should be automated.
Sometimes the right answer is to simplify the workflow. Sometimes it is to improve the data. And sometimes the opportunity is obvious once the work is mapped clearly. My bias is toward honest diagnosis. I would rather tell a business not to spend money than sell an initiative that creates noise, risk, or complexity without a clear operational return.
Start with the work. Then decide where AI fits.
Most AI projects start in the wrong place. They begin with a tool, a demo, or a vendor promise.
The better starting point is the operating reality of the business. What work happens every week? Which tasks repeat? Where do people wait, copy, search, summarize, re-enter, reconcile, approve, or escalate?
Which decisions require human judgment? Which parts of the workflow are rules-based, data-heavy, or slow because the system was never designed for modern automation?
That is where useful AI strategy begins. The advisory process is built around the AI Workflow Audit: a practical method for breaking work into roles, tasks, inputs, friction points, automation opportunities, human judgment, and business outcomes.
