Public MVP - Updated June 2026 - Governed AI worker planning, Advisor paths, and portal previews are informational until production access is separately approved.

Human-Led, AI-First guidance

Implementation Paths

Choose the right path before you build: role enablement, workflow redesign, or operating model lab.

Run readiness assessment Open library

Context

Select the unit of change before selecting tools.

The implementation path should clarify what is changing, who owns it, what evidence is needed, and whether the next step is self-guided library work, Create a Scaled Agent, or Human Advisor support.

  • Use the role path when one role, persona, or team type needs practical AI Worker enablement.
  • Use the workflow path when value depends on handoffs, decisions, systems, evidence, or review gates across a process.
  • Use the operating model lab when leadership wants to redesign how a team or function works from a clean starting point.
  • All paths preserve human ownership, risk review, evidence needs, and readiness boundaries before scaling.

Implementation choices

Three paths, one governed planning model

Each path routes teams to a different planning depth while keeping the same Scaled Agents controls: owner, outcome, AI Worker fit, governance boundary, evidence, and measurement.

Path 1

Role-to-AI Worker Enablement

Use when the unit of change is a role, persona, or team type.

  • Map current role work, friction, knowledge, and repeatable outputs.
  • Identify where AI Workers draft, classify, summarize, route, or prepare evidence.
  • Prepare role enablement, usage guidance, and adoption notes.

Best next step: library artifacts or Create a Scaled Agent.

Path 2

Human-Agent Workflow Design

Use when the unit of change is an end-to-end workflow with decisions, handoffs, systems, or controls.

  • Map the current workflow, owners, decision points, data, systems, and rework.
  • Define human review, Toll Gates, evidence, and escalation before automation.
  • Shape the AI Worker Blueprint and governance handoff path.

Best next step: readiness assessment or Human Advisor.

Path 3

AI-First Operating Model Lab

Use when the organization wants to redesign a team, pod, or function from a clean starting point.

  • Define the business outcome, operating model, roles, review rituals, and measurement loop.
  • Use sandbox governance before scaling patterns or platform licensing decisions.
  • Capture reusable patterns, lifecycle decisions, and adoption evidence.

Best next step: Human Advisor and platform planning.

Planning artifacts support education and readiness decisions. They are not legal, compliance, security, or production approvals.

Decision sequence

Move from path selection to implementation planning

Use the sequence below to keep the work practical and reviewable before licensing, build, or advisory decisions.

1. Name the unit of change

Decide whether the starting point is a role, a workflow, or an operating model lab.

2. Prepare the evidence

Use library artifacts to document outcome, owner, current work, AI Worker fit, risk, and review boundaries.

3. Route the next step

Continue with self-guided library work, Create a Scaled Agent, Portal Sign In, or Human Advisor support based on readiness and risk.

Readiness

Choose the lightest path that still covers ownership, risk, and evidence.

Do not over-design low-risk role enablement. Do not under-govern workflows, sensitive data, external exposure, or autonomous action. Escalate when ownership, evidence, or approval boundaries are unclear.

Ready to choose the right path?

Start with readiness if the path is unclear, or route to a Human Advisor when workflow risk, governance, or platform planning needs specialized review.

Start readiness assessment Human Advisor
Public MVP - Scaled Agents™ Client Portal preview remains informational until production access is separately approved.