AI is probabilistic, so behavior has to be discovered per customer

The first time you deploy an AI product into a real customer environment, you'll unlearn a software-era assumption fast: that if it worked in testing, it works. On real data, in the customer's actual workflow, the model does something you never saw in any demo. The job isn't to install a known thing - it's to discover how this thing behaves, here, for these people. That discovery work is the heart of being a Forward Deployed Engineer in the AI era.

Heart of being a Forward Deployed Engineer

The old reflex you have to drop

In deterministic software, behavior is a fixed property of the product - you learn it once and it holds everywhere, forever.
That let you build confidence centrally: QA at headquarters, write the docs, and trust that what you saw is what the customer gets.
With a probabilistic system, behavior isn't fixed - it's an emergent result of the model plus this customer's data, edge cases, and definition of a good answer.
Change any of those and the behavior changes, so what you validated in your test environment tells you little about theirs.
The mental shift: you're not deploying known behavior, you're running an investigation.

Why the same product behaves differently for every customer

Their data is shaped differently. The vocabulary, formats, legacy conventions, and missing fields are unique to them, and the model responds to that distribution - so two customers on the "same" product are running it against two different realities.
Their edge cases are their own. Every business has a long tail of unusual situations that matter enormously and appear nowhere in your test set - and that long tail is often what they care about most.
Their definition of "good" is specific. One wants terse, another thorough; one sees a compliance risk where another sees a perfect answer. The model is right or wrong only relative to a standard that lives inside this organization.
The takeaway: behavior has to be discovered per customer because the inputs, the edges, and the standard all change per customer. There's no central "correct" waiting to be shipped.

What the discovery actually looks like on the ground

Run on real data early. Get the model against actual messy customer data as soon as possible - not synthetic, not a sanitized sample. Every day on clean test data is a day you learn nothing about the real deployment.
Review outputs with the customer. You need their judgment in the loop because they define what "good" means here. Watch for the surprises - confidently wrong outputs, unexpected wins, failures on cases that turn out to be central.
Turn observations into an eval set. A living collection of real examples, labeled by what good looks like for this customer, is your steering wheel - it tells you whether a change made things better or worse for them.
Iterate and tighten the loop. Adjust prompts, add retrieval, tune the workflow, add a human checkpoint where trust isn't earned yet, then re-check against the eval set. What felt unpredictable in week one becomes characterized and manageable by week six.

The traps that catch people who skip the discovery

Generalizing from your own demo. It worked beautifully on your data, so you assume it'll work on theirs - and the more impressive the demo, the more dangerous the assumption, because it makes you skip the investigation.
Treating surprises as bugs to escalate. Early on, unexpected behavior isn't a defect - it's information about the gap between the general model and this specific reality. File it and wait for a fix, and you miss the point of being embedded.
Defining success in the abstract. "Make it accurate" isn't a target you can steer toward. Until you've turned the customer's fuzzy sense of quality into concrete labeled examples, you don't have a definition of done - you have a wish.

Why this is the FDE's job specifically

The work requires real technical ability - to wire the model against messy data, build the eval loop, diagnose bad outputs, and change the system in response.
It also requires being embedded in the customer's context - long enough to understand their data, their edges, and their standard from the inside.
Neither half works alone: an engineer who never leaves headquarters can't discover behavior they never observe; a customer-facing person with no technical depth can see the surprises but can't act on them.
The FDE fuses both - technical enough to change the system, embedded enough to know what to change it toward - which is exactly what a probabilistic product demands.

Final Thoughts

The model ships the same to everyone. What it does once it lands is a discovery, not a delivery. How much of your deployment process is built to discover, rather than to install?