Most organizations meet AI as a chat window. People use it on their own, leadership isn't sure what's allowed, and nobody can say whether it's making a real difference. I help leaders sort that out: where a simple automation would pay off, where agent-style work is worth building, and how to set sensible rules for what people are already doing.
Mike Harvey -- BSEE · MBA · PMP. Founder, former division director at Alfa Laval, and an engineer who builds and runs AI in his own businesses.
Take the free readiness read →You've tried ChatGPT or Copilot, and it's impressive in a chat window. Some of your people use it every day, some never, and a few paste in things you'd rather they didn't. Vendors are pitching automations and AI agents. What nobody has told you is whether any of it makes a real difference here, or how you would know.
Employees use AI tools whether or not there's a policy. The real questions are what's safe to put in, where it genuinely helps, and who decides.
Most real wins are plain automation: a repetitive step done the same way every time. Agentic work, where AI plans and acts across several steps on its own, can pay off too, but it needs clearer goals, cleaner information and more oversight. Knowing which one you need saves real money.
A good demo is not a benefit. Before you invest, you need a simple way to measure time saved, mistakes avoided, or work that wasn't possible before, and a way to check it after launch.
Email me what you're working on. If AI can help, I'll tell you where; if it can't, I'll say so. When there's a fit, the first paid step is the State of AI Session: one focused hour on your operation, run the same way every time, and a written snapshot within 48 hours of where AI pays off and where it doesn't. I quote it after we've talked, and the fee is credited toward anything that follows.
Want a preview? Run the free self-serve version of this scorecard first -- no sign-up, two minutes.
Whether AI helps an organization depends on decisions made at three levels: the owner deciding what to spend, the director deciding what to allow and how to measure it, and the employee deciding whether to use it at all. I have made those decisions from all three chairs.
I built Verbatim so every answer traces back to its source because the domain demanded it. Version, hash, provenance, on every answer. That is not a feature. That is the minimum the work required.
I run AI systems in my own businesses today. The best evidence that a method works is that it is running.
Six systems I've designed and built.
Grounded in deep technical operations experience where documentation and data accuracy are not optional.
From the first paid hour to a commissioned system, the sequence is structured, reviewed, and managed -- the way a disciplined build should run. You always know what comes next.
The paid hour. We map and evaluate your operation together; you leave with a written snapshot.
Opportunities evaluated against your constraints -- data quality, regulatory needs, review-gate requirements. A prioritized scope with acceptance criteria.
Design and build with traceability and evaluation in the architecture from the start, not added later.
Commission with documented baselines and a defined evaluation cadence. Someone is watching, systematically.
The scorecard and the systems get sharper with every run. The process compounds.
I am Mike Harvey -- BSEE, MBA, PMP, and founder of Harvey Consultancy LLC. I spent 20+ years engineering desiccant and hybrid desiccant-refrigeration systems for food processing, pharmaceutical manufacturing, hospital ORs, lithium-battery dry rooms, and cold storage -- operations where a wrong number has real consequences. That work taught me that a system which cannot prove its answer is not a working system. I brought that standard to AI.
I built Verbatim, a retrieval system for Medicare and Medicaid billing questions where every answer cites its source. I have also built a multi-agent AI content pipeline, an engineering knowledge system, an AI phone agent, and scheduled monitoring agents. I advise on AI the same way I built those systems: scoped, reviewed, traceable, and managed to a finish line.
Harvey Consultancy LLC -- Grand Island, NY. Career projects in 14 countries across North America, Europe, and Asia.
Email me a few lines about your business and what has you looking at AI. I'll reply personally, and if there's a fit we'll talk about a structured first session, priced once we've talked and credited toward anything that follows.
Get in touchmike.harvey@harveyconsultancy.com · harveyconsultancy.com
