On a balmy Monday afternoon, imagine walking into the plant manager’s cabin and saying that they don’t need a boiler operator anymore because AI has become good at doing that job.
If I were the plant manager, I’d ask, “So, if the damned thing blows up, should I call you or chat with your bot?”
I am an insider in AI. I am not saying AI should not go to some sacred avenues inside a plant. It will. But the control of the control systems should rest with the plant manager. The intelligence should not be packed away and stashed into interfaces whose keys you will not possess.
A boiler room needs an operator. What about organization-wide intelligence that does, ratify, decide, and execute everything? Who is that operator? Where do they sit and whose payroll funds them? Are they a human whose throat is accessible or are they an email and a chat box away when you really want them three feet away so that you can imagine choking their visible neck?
Healthcare practices still debate what a data lake is; CIOs in manufacturing firms still only provision cloud budgets. They don’t know an MCP from an API. The typical enterprise is embarrassingly inadequate to handle AI.
I am not talking about change management. That is being dealt with by the brute-force called “Forward Deployed Engineering.” The self-serving framing accelerates deployment, picks the parts that are valuable, and leaves. (Side note: We like diffusion better than deployment and we keep the data and IP with our clients so that they get the upside of AI valuation too.). What happens after deployment? Next plant outage? New practice location with a new payer? Are we waiting for the glorious return of the AI maintenance team, like a lovelorn wife waiting for the returning veteran, so the fights can now be face to face?
The world is making a case for open-weights & local models that you can deploy within your premises. I will make a case for a team that goes with it. IT departments have to be replaced wholesale, by AI departments. They are too comfortable provisioning, when they have to be managing intelligence and maintaining it. And they have to be sitting in the shopfloor and not in the IT rooms.
The FDE model is the failure of the IT department. Who knows better than a company’s workflow that the one who does it and the colleague who enables it? And yet, the constraints of the talent market have prevented the industrial campus in Cincinnati from having its own data scientists.
The market has changed. The software engineer is not in as much demand as the forward deployed engineer is. See LinkedIn. You’d find this mentioned everywhere, next only to pronouns.
Forward Deployed Engineering is tech conceding that your data, and knowhow matters a lot. If it matters a lot for them, it matters more for you.
Its time not just to bring back manufacturing onshore. Bring AI on-premises. Bring the team that brings AI on-premises, on-premises. Without it, you will be paying in tokens for running the factory and yet not have the keys to the machine that was built by the tokens you paid for.