AI Process Diagnostic: map the real process before you build the agent
Most AI efforts stall because no one mapped the real work first. The Process Diagnostic (also called an AI Opportunity Sprint) is a short engagement on one high-value workflow that produces the map, the redesign and the baseline.
What you get
- An as-is process map showing the steps as they actually run, including exceptions, loops and workarounds, and how often each happens.
- A four-bucket redesign that sorts every step into delete, plain code, agentic, or human-in-the-loop.
- Baseline KPIs: cycle time, straight-through rate, cost per transaction and exception rate.
- An estimate of impact, agreed with you once the map exists and never promised in advance.
How it works
- Discover. Structured interviews with the people who do the work, from heads of function to the person who has handled exceptions for twenty years.
- Map. Systems observation and documentation review fill in what interviews miss. Across teams, systems and channels, I document the process as it runs.
- Redesign. High-risk steps stay with people. Agents are designed to work inside your existing systems of record rather than adding a new surface.
- Measure. Baseline before anything is built, so improvement can be shown rather than asserted.
Who it is for
Operations, finance, sales-ops and transformation leaders who have been told to "apply AI" to a workflow that spans several teams and systems. It is also useful to engineering teams and forward-deployed engineers who need an accurate process map before they build.
What it is not
I lead the discovery, process re-engineering and human-in-the-loop design. I do not build production agents. If you need the agents built, I hand over an engineer-ready specification for your team or partners.
Questions
What is an AI process diagnostic?
A short engagement on one high-value workflow. I interview the people who do the work, observe the systems, and review the documentation. You get an as-is process map showing the exceptions, loops and workarounds, a redesign that sorts each step into delete, plain code, agentic or human-in-the-loop, baseline KPIs, and an estimate of impact.
Why do AI agent projects fail?
Most stall because no one mapped the real work first. The official process says six steps; the reality is often closer to fourteen, with loops, regional variations and knowledge that lives in people's heads. An agent built on the clean version automates the wrong thing, faster.
What is human-in-the-loop design?
Deciding which steps an agent should never take alone, such as approvals, payments, negotiation and sign-off, and designing the handoff so a person can review, challenge and override. It is a service design problem as much as an engineering one.