For companies running engineering in more than one location, there's a risk to AI adoption that single site teams never face. This risk is not technical - it is organizational, and like any such risk it is easy to overlook until the price has been paid.
The risk is easily articulated - you end up paying multiple times for the same learning curve.
Parallel, uncoordinated adoption trap
Imagine an organization with three engineering sites. AI arrives. The measure is implemented at each site, composed of highly skilled personnel.
You are trained on a limited set of prompts and conventions over a site. One develops its own, distinctly conflicting process. One option is to wait and see what works with about one-third.
But each site hits the same dip, that trough right when you introduce an AI. The parties are processed through their own respective queues. They all learn the same lessons - the hard way - what should be delegated and what you must verify.
Six months later you look around and see three dialects of use of AI, three sets of standards half-built, and three teams who each paid the full price of the same mountainside. Nothing accumulated. You can't throw upward, so the effort was compounded sideways.
The technology was never the issue. The coordination was the problem.
Central control is not the fix
The obvious (and the incorrect) solution is to centralize. Select one approach, demand it everywhere, enforce consistency.
This fails for a very simple reason. Adoption does not happen like waste; it is rather the local experimentation that each site is doing. People learn about AI by testing it against their stack and under constraints specific to their organization. Impose one-size-fits-all working practices cross-border and you destroy the very thing that works about adoption.
Hence, the aim is not sameness of practice. That is, shared learning with local freedom.
What actually travels well
Three things make the difference between three teams climbing in isolation and one organization growing together.
Shared vocabulary. Everyone describes maturity and practice with the same vocabulary. One site can say a module is at the AI level and exact some other kaleidoscope knows with no translation. Then a conversation across sites is not a negotiation over terms, it is about the same thing.
Shared artifacts. All the configuration files, the prompt libraries, eval scenarios and security checklists live in one place and can move freely. One useful pattern discovered in one place is automatically available to all of them, rather than someone mentioning something during a call.
Local autonomy on top. Every site retains its own racers who scout on the ground, customizing the common foundation to their reality and broadcast outward to share their learnings on almost a monthly basis. They go local first, then report back across the borders.
It is not a top down architecture with central home base hierarchical dogs barking orders below (the shape), but an upper level light weight part of shared identity plus place-based exploration [above]. When it crystallizes, the organization moves once up the curve instead of every time for every site: the second site builds on first-site learning; and the third is built on a bigger base than any one started.
The sacrifice of a leader
There is a price to pay and it doesn't come so much with the teams as with the leader.
It means, of course, not giving in to the common inclination: letting every strong site operate at its own pace (because they can and probably would be okay without coordination). Instead you work at the connective tissue, the common index and cross-site sync and shared language.
This work has no demo. The only trace it leaves is that there was no pain, because the coordination worked so there is no more duplicated pain. And that is a tough thing to invest in, precisely because success looks like nothing happening. However, it makes a difference between AI making your entire organization better by using the tool across multiple sites vs AI tiring each of your sites separately.
If one of your sites learns something AI useful this month, how is that information shared with the others and what is the lag time?
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