# AI Transformation Maturity Consulting Program: Story, Table of Contents, and Chapter Plan

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Last updated: 2026-05-13

## 1. Program Positioning

This program is not simply about selling AI courses, nor is it just about selling tool deployments.

The true positioning is:

> Help enterprises convert fragmented AI usage into organizational capability that is reproducible, governable, measurable, and scalable.

Most enterprises have already started using ChatGPT, Claude, Gemini, or other AI tools, but the majority remain stuck at individual experimentation. Employees find it useful, yet management sees no organizational benefit; some people produce results that cannot be replicated across the team; processes still rely on manual data transfer, copy-paste, sending emails, filling out forms, and updating systems.

Our strategy is to lift the enterprise up along a clear consulting pathway:

1. Use a simple questionnaire to diagnose the enterprise's current AI maturity.
2. Configure the L1-L5 course ratio based on the diagnostic result.
3. After training, use an eight-stage consulting methodology to produce an AI Transformation Diagnostic Report.
4. The report proposes an executable Skill, Workflow, Agent, and Agent Team Roadmap.

## 2. A Scenario Story the Customer Can Understand

### 2.1 The Story's Protagonist

Suppose the customer is a 300-person manufacturing or services enterprise.

The company has Sales, Customer Service, Operations, Finance, IT, HR, and Management departments. Every department has started hearing about AI, but the actual usage is highly inconsistent:

- Sales staff use ChatGPT to draft visit emails and presentations.
- The customer service manager wants to organize an FAQ, but the data is scattered across Gmail, CRM, Notion, and Excel.
- The operations manager wants to know the daily exception orders, but data exported from the ERP needs manual cleanup.
- The CEO wants to push AI, but only hears about isolated cases and cannot judge where the company truly stands.
- IT worries about security, permissions, data leakage, and the maintenance cost of system integration.

At this point, simply selling tools makes it hard for the customer to understand the value; and if you only deliver training, after the course people easily fall back into the original processes.

So what we want to tell is a transformation story:

> First understand where the company is today, then strengthen employee and departmental capabilities, and finally turn the truly valuable scenarios into a landable AI transformation roadmap.

### 2.2 Before: The Typical State Before Enterprise AI Adoption

In the beginning, enterprises usually do not have "no AI at all"; instead, "AI is already scattered everywhere."

Common situations:

- Some people are very skilled with AI, but the methods stay on their personal computers or in chat histories.
- Departments have no shared Prompts, SOPs, templates, or knowledge bases.
- Customer data sits in the CRM, tasks in Notion, spreadsheets in Google Sheets, emails in Gmail, orders in the ERP — none of them connected.
- When management asks about AI's ROI, the field can only answer "things seem to be faster."
- IT cannot judge which scenarios are ready for automation and which scenarios should be governed first.

This means enterprises are not lacking an AI tool — they are lacking a "maturity map" and a "transformation methodology."

### 2.3 Future: The Future Clients Should See Before Class

Clients usually do not buy a course because they want to learn tools. They buy because they want to see what their company can become. The L1-L5 opening should not start with OpenWebUI, Antigravity, n8n, Hermes Agent, and ClawTeam as a tool list; it should start with a future workday after the course.

Imagine that three months later, the company's Monday morning has changed:

1. A sales rep opens OpenWebUI, logs in with their own account, and enters their personal chat workspace. AI usage is bounded by company policy: which data is allowed, which data is prohibited, and which output format the department expects. Sales no longer starts from a blank page; they use approved Prompts for customer visit emails, meeting summaries, and proposal drafts.
2. A marketing manager converts a product-copywriting method that used to live only in their head into a Skill. A new employee can enter product data, target audience, tone, and channel, then generate a draft aligned with the department's style. The manager reviews quality instead of reteaching the workflow every time.
3. When a customer complaint arrives, n8n reads the Gmail message, checks customer tier and history in CRM, updates Google Sheets, creates a Notion follow-up task, and produces a manager summary. Humans still make judgment calls, but they no longer move data by hand.
4. Every morning, the operations manager receives a Hermes Agent briefing. The Agent has read ERP exception orders, service records, yesterday's meeting notes, and Wiki knowledge. It identifies three issues to follow up and marks which steps require HITL (Human-in-the-Loop) review before continuing.
5. When management plans a new product launch, ClawTeam organizes market, product, service, finance, and operations Agents. Each Agent has a role, task, and deliverable, and the final report is traceable to sources and responsibilities.

This future-state imagination belongs before the course starts because it turns L1-L5 from a tool list into a capability progression the client can understand:

| Level | Axis | Capability After Training | Scenario the Client Can Imagine |
| --- | --- | --- | --- |
| L1 | Scale axis · individual | Every employee safely uses AI through a controlled gateway | Employees have their own AI workspace instead of shared accounts or uncontrolled data input |
| L2 | Scale axis · department | Expert methods become reusable Skills, organized by department responsibility | New employees can use departmental standard methods to produce stable outputs |
| L3 | Scale axis · cross-department / company-wide | AI chains cross-department Skills and integrates systems to execute workflows | email, Sheets, Notes, CRM, API, and ERP become company-wide connected workflows |
| L4 | AI-autonomy axis · super-assistant | A fully autonomous AI Agent uses memory, tools, workflows, and auto-scheduling | Daily briefings, follow-up lists, evidence, and items awaiting HITL review are generated automatically |
| L5 | AI-autonomy axis · AI team | Multiple specialist Agents form a functional-division-of-labor team | Product, quality, healthcare service, or customer engagement work can be prepared by an AI team |

Therefore, sales and course openings should tell the future scenario first, then explain L1-L5; describe the work capability first, then the tools; and show verifiable deliverables before technical details.

### 2.4 Transformation: The Three-Stage Journey We Take Customers Through

#### Stage 1: Questionnaire Diagnostic

First, use a simple questionnaire to quickly identify which level the enterprise is currently at:

- L1: Employees individually use Chat AI.
- L2: Departments begin to consolidate Skills, SOPs, Prompts, and templates.
- L3: Begin using n8n to integrate systems and workflows.
- L4: Begin using Hermes Agent equipped with Wiki memory, tool calling, scheduling, evidence, and HITL (Human-in-the-Loop) review.
- L5: Multiple Agents form a team that collaboratively completes enterprise-class processes.

The purpose of the questionnaire is not academic measurement, but to give the CEO, managers, and IT a shared language about the current state.

#### Stage 2: Course-Based Capability Building

Courses are not a fixed menu; the ratio is configured based on the diagnostic result.

For example:

- If the customer is largely at L1, the curriculum emphasizes OpenWebUI, AI usage guidelines, Prompts, data security, and departmental Skill-ification.
- If the customer already has departmental templates and SOPs, the curriculum moves toward n8n Workflow and system integration.
- If the customer already has an automation foundation, you can add the Hermes Agent knowledge-work closed loop, and further evaluate the ClawTeam Agentic AI Team course.

The goal of the course is not just to teach tool operation, but to enable customers to produce their own work assets during the course:

- Prompt Library
- Skill list
- SOPs and templates
- Workflow PoC
- Hermes Agent task examples
- AI Team collaboration scenarios

Course design must also reference each level's requirement list and the company's profile. In other words, L1-L5 is not a fixed lesson plan but a maturity architecture; what is actually taught, what cases are used, and how deep we go depends on the customer's industry, data sensitivity, IT capability, existing systems, and deployment mode.

For example, the same L3 n8n:

- A marketing/services company may prioritize integrating Gmail, Sheets, Notion, and CRM, to rapidly produce client proposals and campaign reports.
- An R&D/manufacturing company may prioritize integrating ERP, internal APIs, databases, quality documents, and exception order processes.
- An enterprise with highly sensitive data may first go Hybrid or fully on-premises, plus add human review, permission control, and audit logs.

#### Stage 3: Consulting Diagnostic Report

After the course is not the end — it is the entry point into consulting diagnostic work.

We use an eight-stage methodology to organize:

1. Enterprise current state.
2. AI maturity level.
3. Process and data gaps.
4. High-value scenarios.
5. Priority deployment order.
6. Three-phase Roadmap.
7. ROI and governance recommendations.
8. Subsequent deployment plan.

The final deliverable is an AI Transformation Diagnostic Report that management can understand, department heads can endorse, IT can evaluate, and the team can execute.

### 2.5 After: The Change the Customer Should Feel

After completing the first round of services, the customer should be able to answer the following questions:

- What is our company's current AI maturity level?
- Which departments already have an AI-ready foundation?
- Which processes are best suited to be automated first?
- Which data and systems need to be integrated?
- For next quarter, should we focus on L2 Skill, L3 Workflow, L4 Agent, or L5 Agent Team?
- After pushing AI, which KPIs should we use to track effectiveness?

This is the transition from "everyone trying AI individually" to "the company has an AI transformation roadmap."

## 3. L1-L5 Maturity and Course Modules

### 3.0 The Two Axes of L1-L5: From One Person's Tool to the Company's Own Digital Workforce

> An enterprise's AI maturity is not "learning a few tools" — it is walking **two axes**: first letting AI permeate the whole organization, then growing the organization's own AI workforce.

#### Act One: The Scale Axis — AI Grows from "One Person's Tool" to "the Whole Company's Nervous System" (L1 → L2 → L3)

Meet Andy, a salesperson.

- **L1 · Individual**: Andy learns to use AI to write customer visit emails himself. Useful — but only he can do it, and the method is locked in his head. AI's reach = one person.
- **L2 · Department**: The sales department turns "how to write a good visit email" into a Skill that a new hire can use on day one. AI's reach spreads from Andy to the whole department.
- **L3 · Cross-department / company-wide**: The Skills of Sales, Customer Service, and Finance are chained together by n8n and connected to CRM, ERP, and email. A complaint email arrives, and AI automatically looks up data, updates, and notifies across departments. AI's reach becomes the whole company.

Throughout this act, **the steering wheel is in human hands**. Andy, managers, and IT decide what AI does and how far it goes — however capable AI gets, it is "a car driven by a human." The progress of this axis is **reach**: one person → one department → the whole company.

#### Act Two: The AI-Autonomy Axis — The Company Grows an "Off-Headcount Employee" (L4 → L5)

Once the whole company's processes are connected (L3 lays the foundation), the company can do something it could not do before: **let AI go to work on its own.**

- **L4 · AI super-assistant**: The company gains an AI entity that goes to work on its own. It has its own capability map, its own tools, its own schedule. When the manager arrives in the morning, the briefing and the three issues to follow up are already on the desk. The manager's role shifts from "using AI" to "managing an AI subordinate."
- **L5 · AI team**: Not just one. A market AI, a product AI, a finance AI, a customer-service AI… multiple specialist AIs form a team that divides labor and collaborates on its own, completing cross-department tasks like "new product launch analysis." The manager's role rises another level — from "managing one AI" to "governing an AI workforce."

In this act, **the steering wheel is in AI's hands**. But humans have not disappeared — **humans move up to a higher position**: defining responsibilities, setting rules, accepting results, keeping HITL (Human-in-the-Loop) review and acceptance gates. **The more autonomous AI becomes, the more the human looks like a "governor" rather than a replaced "operator."**

#### The Most Important Line: L3 → L4

The whole model has only **one true boundary**, between L3 and L4:

| | L1 → L3 (scale axis) | L4 → L5 (AI-autonomy axis) |
| --- | --- | --- |
| The question you ask | "How **widely** can I **use** AI?" | "How much can my AI do **on its own**?" |
| Who holds the steering wheel | Humans | AI (humans step back to governor) |
| AI's identity | The company's **tool** | The company's **digital workforce** |
| How it grows | Individual → department → company-wide | One assistant → one team |

And there is **no shortcut** across this line: L4 must stand on the foundation L3 built — without company-wide connected processes, AI has nothing to "run on its own."

> In L1-L3, you teach AI and spread it; in L4-L5, you raise AI and hand off to it. Walk both axes, and the company doesn't just "know how to use AI" — it "owns an AI workforce," and you become a governor instead of an operator.

### 3.0.1 Procure AI, Don't Buy a System: The L4-L5 Mindset Shift

At L4-L5, the biggest shift an enterprise must make is not technical — it is a **mindset shift**.

Bosses are used to seeing AI the way they "buy a system": evaluate the feature list, compare license prices, calculate software ROI. But L4-L5 AI is not software — it has three things: **reasoning, autonomy, and tool use** — functionally, it does what a knowledge worker does. So the correct mindset is **"procuring AI"**: like recruiting, onboarding, managing, and governing a talent, not like buying a piece of software.

| The "buy a system" mindset | The "procure AI" mindset |
| --- | --- |
| Evaluate feature lists, seat counts, annual license fees | Evaluate how much workload it takes off, how much capacity it amplifies |
| Calculate "software ROI" | Calculate "workforce ROI" — ROI doesn't disappear, the measuring stick changes |
| Install, go live, done | Define responsibilities, onboard, review performance, govern continuously |
| People are replaced by a tool | People are upgraded to governors, with an extra digital workforce to govern |

> **Scope**: this mindset applies only to **L4-L5 (the AI-autonomy axis)**. At L1-L3 you are legitimately buying tools, equipment, and systems — that is reasonable and verifiable. "Procuring AI" is about the boss's mindset shifting once AI crosses L3→L4 and becomes an autonomous digital workforce.
> **It is a management-posture analogy, not a claim that "AI is human"** — precisely because AI ≠ human, it needs the governance framework and HITL that humans build, and that is the core of the consulting service.

### 3.1 L1 Controlled AI Access: OpenWebUI

#### Positioning

OpenWebUI is the enterprise's internal AI conversation portal.

The focus at L1 is to enable employees to begin using AI safely, stably, and effectively to handle daily knowledge work.

#### Typical Scenario

A sales rep needs to write customer visit emails, meeting summaries, and proposal drafts. Where it used to take 1 hour to organize, OpenWebUI can now produce a first draft within 15 minutes, which the person then refines.

#### Value the Customer Will Understand

- Employees begin saving personal work time.
- The company can establish a unified AI portal.
- Models, permissions, and usage guidelines become easier to manage.
- Establishes the common foundation for subsequent Skill-ification and Workflow-ification.

#### Course Content

- OpenWebUI basic operation.
- AI conversation and Prompt fundamentals.
- Summarization, rewriting, translation, classification, brainstorming.
- Enterprise AI usage guidelines.
- Data and tasks that are not appropriate to hand to AI.

#### Course Deliverables

- Personal high-frequency work Prompts.
- Departmental AI usage notes.
- Common functional scenario examples.

### 3.2 L2 Knowledge Codification: Antigravity / Claude Code / Codex

#### Positioning

The focus at L2 is to turn individual experience into departmental capability.

Antigravity, Claude Code, and Codex are not just for writing code — they serve as "Skill construction tools": consolidating a top performer's working method into reusable specifications, prompts, documents, templates, and operating procedures.

#### Typical Scenario

A marketing manager has always been great at writing product copy, but every time a new hire joins they have to teach again. Through L2 Knowledge Codification, the copy generation process can be organized into a "Copy Generation Skill":

- Target audience.
- Tone and style.
- Prohibited expressions.
- Product selling points.
- Sample copy.
- Output format.

A new hire only needs to invoke the Skill and can produce content close to the department's standard.

#### Value the Customer Will Understand

- No longer dependent on individual heroes.
- Good methods can be replicated.
- New-hire onboarding time is shortened.
- Departmental quality becomes more consistent.
- Knowledge begins to accumulate as a company asset.

#### Course Content

- Definition and examples of a Skill.
- How to write personal experience into a Skill.
- How to consolidate Prompts, SOPs, Checklists, and Templates.
- How to use Antigravity / Claude Code / Codex to help produce documents and specifications.
- How to maintain a departmental Skill Library.

#### Course Deliverables

- Departmental Skill list.
- 3-5 high-frequency Skill examples.
- Skill standard template.
- Skill maintenance and version control recommendations.

### 3.3 L3 Workflow Automation: n8n

#### Positioning

The focus at L3 is to make AI not just answer questions, but enter workflows, integrate systems, and complete tasks.

n8n is the core of Workflow AI, used to connect Gmail, Google Sheets, Notion, CRM, API, ERP, and other systems.

#### Typical Scenario

The customer service manager has to read Gmail complaint emails every day, look up customer data in the CRM, update the status in Google Sheets, and write a weekly report in Notion. This work is time-consuming and prone to missing data.

With n8n, you can design a Workflow:

1. Gmail receives a complaint email.
2. AI determines the complaint type and urgency.
3. Query the CRM for the customer's tier and history.
4. Query the ERP for order or shipment status.
5. Write the consolidated result into Google Sheets.
6. Automatically create a handling task in Notion.
7. Send an Email or Slack notification to the owner.
8. Automatically compile the customer service weekly report on Friday.

#### Value the Customer Will Understand

- Reduce repetitive manual data shuffling.
- Reduce missed orders, missed emails, and missed updates.
- Connect data flow, task flow, and decision flow.
- AI begins doing work for the company, not just chatting with people.

#### Scope of System Integration

- Gmail: email retrieval, classification, summarization, reply drafts.
- Google Sheets: data recording, list maintenance, report updates.
- Notion: task management, knowledge base, meeting notes.
- CRM: customer data, opportunities, interaction logs, service records.
- API: data exchange with internal and external systems.
- ERP: orders, inventory, shipments, procurement, financial data.
- Database: PostgreSQL, MySQL, vector databases, etc.
- Communication tools: Slack, Teams, LINE, Telegram.

#### Course Content

- n8n basic concepts.
- Trigger, Node, Credential, Webhook.
- Gmail / Sheets / Notion integration.
- API integration and data transformation.
- ERP / CRM integration planning.
- AI Node and RAG node applications.
- Error handling and human-review nodes.

#### Course Deliverables

- 1-2 departmental Workflow PoCs.
- Workflow scenario inventory table.
- System integration requirements list.
- Automation risk and permission checklist.

### 3.4 L4 Autonomous Agent: Hermes Agent

#### Positioning

The focus at L4 is to upgrade AI from "a one-time answer" to "a continuously running knowledge-work agent." It not only understands tasks, decomposes steps, calls tools, and reports back results, but also retains memory, indexes, evidence, schedules, and human review records.

Hermes Agent is the standard platform for L4. It integrates L1 Chat, L2 Skill, and L3 Workflow into a knowledge-grade Agent operating system:

1. Markdown Wiki serves as the source of knowledge truth.
2. SQLite / FTS serves as the query index and derived cache.
3. Five core skills handle management, source analysis, keyword extraction, autonomous discovery, and briefing.
4. Deterministic tools handle writing, indexing, validation, extraction, caching, and logging.
5. Schedules drive nightly ingest, morning briefing, discovery ping, evening preview, and weekly graph synthesis.
6. HITL (Human-in-the-Loop) review handles high-risk updates, keyword approval, schema changes, deletions, and go-live judgments.

#### Typical Scenario

The operations manager tells the AI assistant:

> Help me organize this week's exception orders, identify the top three root causes, produce a management meeting report, and list customers that need follow-up.

Hermes Agent will:

1. First read the department's `purpose.md`, `SCHEMA.md`, `INBOX.md`, `queue.md`, `watchlist.md`, and recent logs.
2. Call the ERP to query exception orders.
3. Call the CRM to query customer data.
4. Call Google Sheets to organize the analysis spreadsheet.
5. Call the L2 "Exception Analysis Skill."
6. Call the L3 report-generation Workflow.
7. Write the analysis process, cited sources, follow-up tasks, and executive summary back into the Wiki.
8. In the briefing, remind managers of which items require human confirmation.

#### Value the Customer Will Understand

- Staff don't need to know how each system operates.
- AI can help decompose and execute cross-system tasks.
- Managers can obtain decision data more quickly.
- AI transforms from a tool into a work agent that accumulates knowledge, traces evidence, and operates on a schedule.
- The Agent is not a one-shot worker — it organizes, reminds, updates, and produces briefings continuously, every day.

#### Course Content

- Agentic AI fundamentals and the L4 / L5 boundary.
- Hermes Agent architecture: Wiki, SQLite, skills, tools, runtime schema, policy.
- Orient-first SOP: Before working, the Agent first reads schema, purpose, INBOX, index, and log.
- Ingest / query / update / lint / graph-synthesis.
- Source analysis, keyword extraction, autonomous discovery, briefing-generator.
- How to design Agent-callable Skills and n8n Workflows.
- HITL review, permissions, Logs, fallback, and acceptance gates (Stage Gates).

#### Course Deliverables

- 1 departmental Hermes Agent task example.
- L4 IPOE table.
- Agent task specification.
- Initial Wiki: `purpose.md`, `SCHEMA.md`, INBOX, queue, watchlist, tasks.
- Agent-available tools list.
- Ingest / query / update test records.
- Briefing template.
- Acceptance gate 4A-4E table.
- Agent risk, permissions, and operations Runbook.

### 3.5 L5 Multi-Agent Organization: ClawTeam

#### Positioning

The focus at L5 is to have multiple specialist Agents collaborate, forming an AI team that completes enterprise-class tasks that a single Agent cannot.

ClawTeam is the landing platform for Agentic Team AI.

#### Typical Scenario

The CEO says:

> We want to develop a new product. Help me organize market opportunities, competitors, product positioning, marketing strategy, customer-service preparation, and financial projections.

ClawTeam can launch multiple Agents:

- Market Analysis Agent: analyzes market trends, customer segments, and competitors.
- Product Planning Agent: organizes product requirements and feature priorities.
- Marketing Strategy Agent: proposes channels, messaging, and campaign plans.
- Customer Service Agent: prepares FAQs, service workflows, and knowledge base.
- Financial Analysis Agent: estimates costs, revenue, budget, and ROI.
- Project Integration Agent: integrates all outputs into a project plan.

#### Value the Customer Will Understand

- Not one person plus one AI, but one department plus one AI team.
- Cross-departmental complex tasks can be drafted faster.
- Management can use the AI Team to prepare decision work upstream.
- The enterprise begins to acquire automated operations and innovation capabilities.

#### Course Content

- Multi-Agent collaboration concepts.
- Agent role design.
- Task assignment and integration.
- Role conflicts and quality checks.
- Agent Team output formats.
- Governance and supervision of enterprise-class tasks.

#### Course Deliverables

- An Agent Team scenario design.
- Agent role cards.
- Multi-Agent collaboration flow diagram.
- Agent Team output samples.

## 4. Simple Diagnostic Questionnaire Design

### 4.1 Questionnaire Purpose

Within 10-15 minutes, the questionnaire must let the customer understand:

- Where the company's current AI maturity stands.
- Which capabilities already have a foundation.
- Which gaps obstruct the next stage.
- How the course ratio should be configured.
- Which topics the subsequent consulting diagnostic should focus on.

### 4.2 Questionnaire Dimensions

We recommend dividing the questionnaire into six dimensions:

| Dimension | What it judges |
| --- | --- |
| Tool Usage | Whether employees stably use AI tools |
| Knowledge Consolidation | Whether there are Prompts, SOPs, templates, Skill Library |
| Process Automation | Whether there is system integration and automated workflows |
| System Integration | Whether Gmail, Sheets, Notion, CRM, API, ERP can be invoked by workflows |
| Agent Application | Whether there is an AI Agent that can autonomously execute tasks |
| Implementation & Change | Whether there are permissions, security, performance metrics, and management systems |

### 4.3 Diagnostic Output

The questionnaire outputs after completion:

- Current maturity level: L1-L5.
- Main gaps: e.g., knowledge not consolidated, workflows not integrated, Agent lacking governance.
- Recommended course ratio: e.g., L1 20%, L2 30%, L3 30%, L4 15%, L5 5%.
- Recommended priority scenarios: e.g., automated classification of customer service emails, sales proposal generation, ERP exception-order analysis.
- Subsequent consulting diagnostic focus: e.g., process inventory, data permissions, ROI metrics.

## 5. Course-Ratio Configuration Logic

### 5.1 Configuration Principles

The course ratio is neither randomly chosen by the customer nor fixed by us.

Recommended logic:

- First judge current maturity based on the questionnaire.
- Then look at management's objectives: efficiency, cost, quality, revenue, governance.
- Then look at system conditions: whether Gmail, Sheets, Notion, CRM, ERP, and API are already in place.
- Then look at the company profile: industry, data sensitivity, IT capability, budget, cloud policy.
- Then look at the deployment mode: cloud AI, Hybrid, fully on-premises.
- Finally, decide the L1-L5 course ratio.

### 5.2 Company Profile and Deployment Mode

Before course design, you must first investigate which AI architecture suits the customer.

#### Cloud AI

Cheapest and fastest to launch. Suitable for enterprises with low data sensitivity that are mainly focused on copywriting, research, customer service, marketing, and general knowledge work. The course can emphasize L1 and L2; L3 first integrates with SaaS tools such as Gmail, Sheets, Notion, and CRM.

#### Hybrid: Cloud + On-Premises

The most pragmatic path for most enterprises. Non-sensitive tasks can use cloud models, while sensitive data, internal systems, and core processes remain on-premises. The course should emphasize data classification, n8n routing, Credentials, API, ERP integration, and human review.

#### Fully On-Premises

Suitable for R&D/manufacturing, finance, healthcare, government, or highly sensitive data scenarios. The course should emphasize the OpenWebUI private portal, on-premises models, internal RAG, ERP/API/DB integration, Logs, permissions, auditing, and change management.

### 5.3 Industry Fit

#### R&D / Manufacturing

Common systems are ERP, MES, PLM, QMS, internal databases, and document systems. AI scenarios should focus on SOP lookup, quality anomalies, complaint analysis, R&D document summaries, ERP exception orders, and process knowledge consolidation. Typically recommend Hybrid or fully on-premises.

#### Marketing / Services

Common systems are Gmail, Sheets, Notion, CRM, ad platforms, and social tools. AI scenarios should focus on proposals, copywriting, competitor analysis, customer meeting summaries, CRM updates, and campaign performance reports. Typically can launch quickly from Cloud AI or Hybrid.

#### Highly Sensitive Industries

For example, finance, healthcare, government, and critical departments of listed companies. The course should prioritize data classification, permissions, auditing, human review, and on-premises deployment. L4/L5 should first be positioned as decision support rather than fully autonomous decision-making.

### 5.4 Example Configurations

#### Scenario A: AI Beginner Enterprise

Current state: Most employees have not yet stably used AI.

Recommended ratio:

- L1 OpenWebUI: 40%
- L2 Knowledge Codification: 35%
- L3 n8n: 20%
- L4 Hermes Agent: 5%
- L5 ClawTeam: 0%

Goal: First build a shared language and departmental Skills.

#### Scenario B: AI in Use, but Workflow Adoption Lagging

Current state: Employees can use AI, but results are scattered and workflows remain manual.

Recommended ratio:

- L1 OpenWebUI: 15%
- L2 Knowledge Codification: 30%
- L3 n8n: 40%
- L4 Hermes Agent: 10%
- L5 ClawTeam: 5%

Goal: Chain Skills into Workflows, prioritize completing 1-2 PoCs.

#### Scenario C: Automation Foundation in Place, Wants to Deploy Agents

Current state: Already has n8n or system integration experience.

Recommended ratio:

- L1 OpenWebUI: 5%
- L2 Knowledge Codification: 20%
- L3 n8n: 25%
- L4 Hermes Agent: 30%
- L5 ClawTeam: 20%

Goal: Upgrade from process automation to Agent and Agent Team.

## 6. The Eight-Stage Consulting Diagnostic Method

> ⚠️ **All client examples referenced in this chapter (e.g., "Client M" / "MingChang Packaging" in §6.1) are AI-generated fictional cases**, NOT real clients. All numbers are illustrative. See [`../04_Scenarios/README_EN.md`](../04_Scenarios/README_EN.md) for full disclaimer.

### Stage 1: As-Is Snapshot — Current State Discovery

Inventory the enterprise's existing AI usage, systems, processes, data, and decision-making methods.

Outputs:

- Current state assessment report.
- As-Is Process Maps.
- AI usage inventory table.

### Stage 2: Reference Model Alignment — Standard Model Introduction

Introduce the L1-L5 maturity model so management and departments share a common language.

Outputs:

- Maturity assessment matrix.
- L1-L5 capability gap list.

### Stage 3: Best Practice Integration — Benchmarking Against Best Practices

Compare against common AI application patterns and benchmark cases in the industry.

Outputs:

- Industry benchmark scenario list.
- High-value AI application scenarios.

### Stage 4: Gap Analysis

Conduct a structured comparison between current state and target state, classifying as Missing, Broken, Redundant.

Outputs:

- Gap matrix.
- Impact x Effort priority ranking.

### Stage 5: Problem Definition — Core Problem Definition

Use the 80/20 principle to identify the problems most worth solving first.

Outputs:

- Executive Problem Statement.
- Key problems and root causes.

### Stage 6: Benchmarking and Phased Goals

Set short-, medium-, and long-term goals and acceptance gates (Stage Gates).

Outputs:

- Three-phase goals.
- Acceptance criteria.
- KPI recommendations.

### Stage 7: To-Be Design — Future Blueprint Design

Design the future AI Operating Model.

Outputs:

- To-Be Operating Model.
- Skill Map.
- Workflow Map.
- Agent Map.
- System integration blueprint.

### Stage 8: Implementation and Change — Execution Deployment and Change Governance

Plan the deployment sequence, governance system, training plan, and ROI tracking.

Outputs:

- Transformation Roadmap.
- Change management plan.
- ROI tracking matrix.
- Follow-on deployment proposal.

## 7. Final Deliverable: AI Transformation Diagnostic Report Table of Contents

### 7.1 Cover

Enterprise name, diagnostic period, consulting team, version.

### 7.2 Executive Summary

A 1-2 page summary for the CEO:

- Current maturity.
- Largest problem.
- Largest opportunity.
- Recommended priority deployment direction.
- Expected value.

### 7.3 AI Maturity Diagnostic Results

- L1-L5 scoring.
- Six-dimension radar chart.
- Maturity differences across departments.
- Main gaps.

### 7.4 Course Observations and Capability Inventory

- Course participation.
- Prompts, Skills, Workflows, and Agent scenarios produced by each department.
- Employee absorption.
- Management concerns.

### 7.5 As-Is Current State Processes and System Inventory

- Usage status of Gmail, Sheets, Notion, CRM, API, ERP, and other systems.
- Data flow.
- Task flow.
- Decision flow.
- Manual rework points.

### 7.6 High-Value AI Scenario List

Organized by department:

- Sales.
- Customer Service.
- Marketing.
- Operations.
- Finance.
- HR.
- IT.
- Management.

Each scenario must be labeled with:

- Corresponding maturity: L1-L5.
- Systems requiring integration.
- Expected benefits.
- Deployment difficulty.
- Risk and governance requirements.

### 7.7 Gap Analysis and Core Problems

- Missing: missing capabilities, data, processes, roles.
- Broken: currently broken or unstable processes.
- Redundant: repetitive, low-value, automatable work.
- Core problem definition.

### 7.8 To-Be AI Operating Model

- Departmental Skill Library.
- n8n Workflow Center.
- Hermes Agent task center.
- ClawTeam Agent Team collaboration model.
- Permission and review mechanisms.

### 7.9 Three-Phase Roadmap

#### Phase 1: AI Foundation and Skill-ification

Goal: Convert individual usage into departmental capability.

#### Phase 2: Workflow Automation

Goal: Integrate Gmail, Sheets, Notion, CRM, API, ERP, and complete high-value process PoCs.

#### Phase 3: Agentic AI and AI Team

Goal: Enable Hermes Agent and ClawTeam to take on cross-departmental tasks.

### 7.10 ROI and Governance Recommendations

- Time saved.
- Error rate reduced.
- Response speed improved.
- Knowledge consolidated.
- New-hire onboarding shortened.
- Management decision quality improved.
- Data permissions and security control.

## 8. External Sales Story Versions

### 8.1 30-Second Version

Most enterprises today already have people using AI, but most are stuck at the individual-tool stage. We first use a questionnaire to diagnose the enterprise's current AI maturity, then arrange the course ratio according to L1-L5, and finally use an eight-stage consulting methodology to produce an AI Transformation Diagnostic Report — so the enterprise knows where they are now, what to do next, and which processes can be automated first.

### 8.2 3-Minute Version

The most common problem in AI transformation is not that employees can't use AI, but that usage is too scattered. Some people use ChatGPT, some can write Prompts, some want to integrate the CRM, some want to automatically consolidate ERP reports — but the company has no shared architecture.

We classify AI maturity into five levels: L1 is Chat AI on OpenWebUI; L2 is Skill AI built with Antigravity, Claude Code, and Codex; L3 is Workflow AI using n8n to integrate Gmail, Sheets, Notion, CRM, API, and ERP; L4 is Hermes Agent; L5 is the multi-Agent team on ClawTeam.

The service flow is simple: first do the questionnaire diagnostic to find out where the enterprise currently stands; then train based on the diagnostic result, with the course ratio determined by customer maturity and goals; finally, we use an eight-stage management consulting methodology to produce a diagnostic report covering current state, gaps, core problems, future blueprint, Roadmap, and ROI.

So what the customer buys is not a course, but a path from AI learning all the way to enterprise transformation execution.

## 9. Content to Be Completed Subsequently

The following should be completed with priority:

- AI maturity questionnaire items.
- L1-L5 scoring logic.
- Course-ratio recommendation rules.
- Consulting diagnostic report template.
- Sample customer stories.
- High-value scenario library by department.
- Demonstration Workflows for n8n integrating Gmail, Sheets, Notion, CRM, API, ERP.
