Adapting LLMs to the enterprise: a field guide
Most enterprise AI initiatives stall between the demo and production. Here is the adaptation playbook we use to get language models doing real work inside real systems — safely, measurably, and lean.
Every enterprise has now seen the demo. A language model summarizes a contract, drafts a response, answers a policy question — and the room nods. Six months later, surprisingly few of those demos are in production. The gap is rarely the model. It is everything around the model.
We call the discipline of closing that gap AI adaptation: reshaping a general-purpose model into a dependable component of your specific system. Here is the playbook we run.
Start from the workflow, not the model
The question "where can we use AI?" produces feature lists. The question "which workflow costs us the most manual effort per week?" produces candidates with measurable baselines. Pick a workflow where:
- the input already arrives digitally,
- a human currently applies judgment you can describe in writing,
- and an error is detectable before it becomes expensive.
That last property matters more than accuracy benchmarks. A model that is 92% accurate with cheap error detection beats one that is 97% accurate with silent failures.
Constrain before you fine-tune
Teams reach for fine-tuning early because it feels like engineering. In practice, most enterprise workloads are solved by constraining the problem: retrieval over your own corpus, structured outputs validated against schemas, tool calls into systems of record, and prompts that encode your policies explicitly.
Fine-tuning has its place — tone, domain vocabulary, latency-sensitive classification — but it is a late optimization, not a starting point. Constraints are auditable; weights are not.
Build the evaluation harness first
Before the first prompt is written, we build the harness that will judge it: a curated set of real cases with known-good outcomes, run on every change. This is ordinary software discipline applied to an unfamiliar component. Without it, every prompt edit is a leap of faith. With it, AI behaves like any other dependency — versioned, tested, and observable.
Design for the failure path
Production AI is a supervision problem. Every integration we ship answers four questions:
- What does the system do when the model is uncertain?
- Who reviews outputs, and how little of their time does that take?
- How does a bad output get corrected and fed back?
- What is the kill switch?
If those answers exist, adoption follows, because operators trust what they can override.
Lean applies to AI too
The waste in most AI programs is not compute — it is work-in-progress: pilots that never conclude, committees that never decide, platforms bought before problems are chosen. Run AI adaptation the way lean runs a line: one workflow at a time, pulled by demand, measured end to end, improved continuously.
That is how a demo becomes infrastructure.