Approach

Teach the runtime.Sell the workflow outcome.

Scale Lean is built around a durable operating model: start with real business context, turn useful prompt patterns into reusable skills, and build workflows the team can keep using after the meeting ends.

The goal is not to attach the business to one tool. The goal is to make the workflow portable, reviewed, and worth repeating.
Context first
AI gets more useful when it understands the business, the files, and the rules of the work.
One runtime per room
Workshops should teach one operating pattern well instead of comparing tools live.
Human review built in
Sensitive outputs need approval rules, not hope.
Core Rule

Workflows and skill specs should outlast the current tool cycle.

That is why the methodology is framed around the work rather than a single vendor.

Workflow First

Start with the repeated work

The team should begin with admin, document, inbox, and reporting patterns that already matter.

Guardrails

Add checks before automation expands

Legal, financial, and client-facing outputs need explicit review rules before the workflow scales.

Reuse

Capture what works as reusable skills

A one-off prompt becomes a team asset when it is documented, repeatable, and portable.

Framework first, runtime second, workflow outcome always.

Hands

AI takes action on your behalf.

Drafting, organizing, summarizing, and transforming work should happen inside clear workflows instead of one-off chat sessions.

Eyes

AI sees what is happening in the business.

Bank data, contracts, inboxes, SOPs, and reporting files become useful signals when the work is structured and reviewed.

Brain

AI knows the business well enough to repeat the work.

Reusable skills, context files, and workflow rules turn a good prompt into a system the team can use again.

Supported runtimes, one at a time

Hands / Eyes / Brain is the durable frame. Claude Code, Codex, and Gemini are delivery layers. The work stays useful when the workflow assets stay portable.

Claude Code

Default workshop runtime and founder-led pilot environment.

Codex

Best fit for OpenAI-native teams that want the same operating pattern.

Gemini

Supported for Google-native environments when the client stack calls for it.