Give the unfamiliar detail somewhere useful to belong.

A mental model organizes a subject. It helps a learner connect a new detail with a relationship, recognize a pattern, and ask what follows when a condition changes.

That makes a complex explanation easier to use beyond the example that introduced it.

A model should be clear enough to work with and open enough to question. The learner needs to know what it helps explain and where a different view may be necessary.

Howard Pearlmutter's teaching connects foundational knowledge, mental models, and deep practical insight. His experience spans architecture, development, systems, business, research, and communication.

At Goldman Sachs, his Goldmine & Greenfield model helped executives examine difficult legacy-migration questions. It gave accumulated assets and new possibilities a place in the same conversation.

At Telos Alliance, the learning connected AI-assisted development with the roles, concerns, and practical work around adoption. Read Telos and Valtech.

One useful test is whether you can apply the model to a different example. What becomes clearer? What does it leave out? Which question would you now ask before acting?

The answers can guide the next explanation or experiment.

Explore applied examples, feedback, or a workshop that works through a subject the group already cares about.

A worked illustration: the prototype everyone calls ready.

Imagine a group examining a prototype. The developer means it is ready for another technical experiment. A product colleague understands that users can now be shown the intended capability. A manager hears that the main implementation uncertainty has been removed.

The prototype has not changed. The models around it differ.

A presentation could tell the group to communicate more clearly. A learning model can go further by giving them categories through which to inspect the disagreement: observed behavior, inferred capability, and intended commitment.

Observed behavior is what the demonstration actually showed under its conditions. Inferred capability is what someone believes that observation implies. Intended commitment is what the organization may now promise or plan to do.

Separating the three makes the missing connection visible. A participant can explain how an observation supports an inference. Another can identify the condition that would have to hold before a commitment follows.

The group now has a way to discuss the work. It can use the same distinction on a different prototype, code change, model, or tool claim.

The distinction turns a familiar difficulty into something the group can examine and use on another piece of work.

Connect the model with an actual case.

Goldmine & Greenfield supplies another example. A modernization discussion can become trapped between attachment to an old system and enthusiasm for a replacement.

The model makes room for two things at once: accumulated value and a new possibility. That distinction lets people examine which operating knowledge should survive, which constraint belongs to the current implementation, and what boundary could make change practical.

At Goldman Sachs, this frame helped executives untangle difficult legacy-migration questions. The model mattered because it made a useful relationship available in the audience's own domain.

Let the learner use the model before adding more detail.

Once a distinction is introduced, another example can reveal whether it has become usable. A learner might identify an observation that is being treated as a promise, or explain which part of an old system carries value independent of its platform.

The response gives the instructor something to work with. It may call for a clearer example, a different representation, or a technical explanation of the mechanism underneath the claim.

More detail then has somewhere to belong. The learner can connect it with a purpose instead of adding another isolated term to remember.

For remote learning, this is especially valuable: the model can travel into the task the learner performs after the session. It supports another question, another review, and another conversation with a colleague.

The model earns its place when the learner can use it to understand more of the next situation.