Your team’s expertise can become an AI workflow or evaluation asset

Expertise often lives between the documents

A procedure can tell someone what usually happens. The experienced person knows when it should not happen, which exception matters and what evidence resolves the uncertainty. That judgment may appear in review comments, rejected drafts, diagnostic notes or the explanation behind an approval.

A business exploring commercialization should look for the artifacts that preserve this reasoning. The aim is not to claim that every historical action was correct. It is to identify reviewed examples that explain a specialist task and distinguish an acceptable result from a flawed one.

Anthropic describes Agent Skills as packages of instructions, scripts and resources that an agent can load for relevant work. This is one concrete format for delivering expertise. It is not evidence that a lab will buy a business’s existing manuals.

Three different products can grow from expert work

These products need different preparation. A workflow package must make its dependencies and expected behavior clear. A training example must preserve relevant inputs and the quality of its target. An evaluation set needs an independent test design that does not simply repeat the training material.

A construction estimator, for example, might have reviewed takeoffs, changes and acceptance criteria. A specialist service team might have resolved cases with documented exceptions. Both are hypothetical sources of useful expertise, with rights and quality still to verify.

The strongest examples show the decision boundary

Collect cases where the expert changed course, rejected a plausible answer or asked for more information. Those examples can expose the limits of a simple rule. Preserve the original context, the reviewed decision and enough explanation to understand why the answer was acceptable.

Include difficult and unsuccessful cases where appropriate. A collection made only of polished successes can conceal the conditions that make the task hard. Record uncertainty honestly, especially when experts disagree or the eventual result cannot be observed.

For a first review, a schema and a few approved examples can be more useful than a large export of unorganized notes. Our quality framework helps structure the questions about links, context and evidence.

Performance evidence supports a commercial conversation

An interesting knowledge collection and a demonstrated capability are different stages of readiness. A buyer will need to understand which task the asset improves, against what baseline and under what conditions. Agree on those questions before spending heavily on packaging.

A pilot might compare performance with and without the workflow resources, or test a set of specialist tasks against a reviewed rubric. Keep evaluation examples separate where necessary to avoid measuring memorization. Human review remains important when the task has meaningful consequences.

Commercial structures might include a workflow license, paid evaluation work or a development project. Their economics depend on the actual deliverable and support commitment. There is no universal premium attached to the word “skill.”

OTD’s role begins with identifying the asset

We can help frame what the business contributes, identify a plausible buyer use case and discuss which format deserves a first test. The owner supplies the domain knowledge and confirms permissions. Technical partners may be needed to implement tools, run evaluations or integrate the workflow.

Explore the commercialization options before deciding to build. In some cases, the first useful product is a dataset of reviewed examples. In others, the opportunity depends on continuing expert participation rather than a one-time archive.

Choose expert workflows in the assessment if this describes your business. Tell us which task your team does unusually well and what evidence of that work already exists. That is enough to begin a focused conversation.