Artificial-I
Enterprise LLMs & Models

Beyond Generic AI: A Practical Framework for Enterprise Model Selection

Artificial-I Research DeskAI Strategy Team
October 12, 20261 min read

Most organisations meet AI through a single general-purpose tool. It is a useful on-ramp, but it quickly runs into limits: inconsistent answers, no audit trail, unclear data handling and costs that are hard to predict.

Start with the task, not the model

List the tasks you want to automate and describe each one in terms of inputs, outputs and what "correct" means. A contract summary, an invoice extraction and a customer reply all need different strengths from a model.

Score candidates on five dimensions

  1. Accuracy on your own examples, not public leaderboards.
  2. Latency at the response times your users expect.
  3. Cost per completed task, including retries and review.
  4. Data handling such as residency, retention and training policies.
  5. Operability including monitoring, versioning and vendor stability.
Illustrative model scorecard — Replace with your own evaluation results

The best model is the one that passes your evaluation at the lowest total cost of ownership.

Route work across models

Once you have scored candidates, a simple router can send easy requests to compact models and escalate hard ones. A minimal policy might look like this:

def route(task):
    if task.kind in {"classify", "extract"}:
        return "small-model"
    if task.risk == "high":
        return "frontier-model-with-review"
    return "mid-size-model"

Build the evaluation set first

A few hundred representative examples, labelled by your own experts, will outlast any individual model choice. Re-run it whenever a new model appears.

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