Measuring Experience Capital

Experience Capital should be measured primarily by the value, coverage, reuse, and productive yield of released reusable models, not by the volume of stored material.

The Wrong Unit

Document counts, archive size, ticket volume, and number of AI conversations measure information activity. They do not show whether accumulated experience has been converted into reusable models.

A stronger measurement system begins with the model itself.

Model Metrics

Useful measures include declared-scope coverage, source support, structural completeness within that scope, internal consistency, verification status of represented rules, number of unresolved ambiguities, required-fact coverage, reuse count, number of applications, and frequency of successful application.

Yield

Model-production yield measures how effectively relevant source material and reconstruction effort become useful reusable structure. It can be expressed through questions such as:

• How many released reusable models or components are produced from a source corpus?
• What share of reconstructed candidate structure reaches release after the required verification for its declared scope?
• How much of a new model can reuse existing components?
• How often does a released model produce an actionable result within its scope?

Yield is therefore not a separate asset. It is a production and reuse property of the model portfolio.

Economic Effect

The operational effect can be measured through reduced interpretation time, faster onboarding, fewer repeated analyses, lower error rates, fewer inconsistent decisions, reduced rework, faster software implementation, and lower cost of automation.

Return on Model Production

Return on model production can be measured by comparing the cost of model production, verification, and maintenance with the recurring value produced by reuse.

A simple conceptual form is:

Recurring value from reuse - production and maintenance cost
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          production and maintenance cost

The important point is not one universal formula. Producing and releasing a reusable model concentrates substantial cost before first reuse, while maintenance continues over the model lifecycle and benefits can recur across many applications.

Portfolio Metrics

At the portfolio level, an organization can measure how many reusable process models it owns, how many domains and declared scopes they cover, how often components are reused across models, how much new work begins from existing models with known verification status, and how quickly the portfolio grows through composition.

Capital Logic

Experience Capital grows when models achieve stronger coverage and support within their declared scopes, become more reusable, and produce value across more appropriate applications.

The central metric is therefore not how much experience or source material was recorded. It is how much useful structure has been released as durable reusable models that continue to produce value.