# AI Maturity Questionnaire

> 🌐 中文版本 / Chinese version: [AI_MATURITY_QUESTIONNAIRE.md](AI_MATURITY_QUESTIONNAIRE.md)

Last updated: 2026-05-15

## 1. How to Use

This questionnaire is provided in three versions:

- 10-item quick diagnostic: for business development, first meetings, and pre-proposal use.
- 25-item pre-course diagnostic: for planning course ratios and case selection.
- 50-item consulting deep-dive diagnostic: for use after the course, when entering the eight-stage consulting diagnostic.

Each item is scored 0-4:

| Score | Description |
| --- | --- |
| 0 | None at all |
| 1 | Sporadic individual use |
| 2 | Department has an initial approach |
| 3 | Standard practice exists and is repeatable |
| 4 | Institutionalized, governable, and measurable |

### 1.1 A Note to the Person Filling This Out

This questionnaire is designed for **every department manager** to fill out — not just IT. **You do not need to understand the technical terms.** Each item has a "plain-language explanation," and the 10-item version also includes "scenario anchors" for scores of 0 / 2 / 4. Just read the examples, think about "which one is my company most like," and pick a score.

Filling-out principles:

- **When unsure, score lower.** The point of a diagnostic is to find the true level. Inflated scores only make the downstream course plan and rollout plan miss the mark.
- **When you see an unfamiliar English term or acronym** (OpenWebUI, n8n, CRM, ERP, API…), check the §1.2 Glossary below first.
- **There are no right or wrong answers.** This is a health check, not an exam. L1 or L2 is not "bad" — it just means "this is where you are now, and here is the next step."

### 1.2 Glossary

| Term | Plain-language explanation |
| --- | --- |
| OpenWebUI | A web interface that looks like ChatGPT, but the company can host and manage it itself. Employees log in to use it, and IT can see who is using it and for what. |
| Cloud AI / public-cloud AI | Services like ChatGPT, Gemini, and Claude — provided by overseas companies, where data is sent to their servers for processing. |
| Prompt | The instruction or way of asking you give the AI. A good prompt is like a "task brief for a new hire" — write it clearly and you get good results consistently. |
| SOP | Standard operating procedure — writing down "how a task should be done" as fixed steps. |
| Skill | Packaging the instructions, examples, and steps for a frequently done task (e.g., "writing a complaint reply") so it can be reused directly without re-thinking each time. |
| n8n | A workflow-automation tool that connects different software with drag-and-drop, so work runs automatically — like a "digital assembly line." |
| Workflow (workflow automation) | A series of steps that originally required a person to click through manually, set up so the system completes them automatically. |
| Auto-trigger | No one presses "start" manually; the workflow kicks off automatically when an event happens (an email arrives, a form is submitted, a scheduled time is reached). |
| AI Agent | A step beyond chat AI: you give it a goal, and it breaks the task into steps itself, looks up information, uses tools, and reports back the result. Like a "digital assistant that figures out how to get the task done on its own." |
| Agent Team / multi-Agent collaboration | Several specialist AI Agents working together like a small team (e.g., one researches the market, one writes copy, one handles finance). |
| API / Webhook | The "connector" through which software passes data to each other. Having an API means the system can be integrated for automation; ask IT if unsure. |
| Database | Where the company stores structured data (customers, orders, inventory). |
| CRM | Customer relationship management system — stores customers, opportunities, and sales records (e.g., Salesforce, HubSpot). |
| ERP | Enterprise resource planning system — manages finance, inventory/sales, and production (mainstream ERP / custom-built / none). |
| MES | Manufacturing execution system — manages shop-floor production. |
| PLM | Product lifecycle management — manages product design and engineering data. |
| QMS | Quality management system — manages inspection, defects, and audits. |
| Data classification | Categorizing company data by confidentiality (public / internal / confidential / highly confidential) to decide what can and cannot be given to AI. |
| Cloud AI / Hybrid / Fully on-premises (deployment modes) | Cloud AI = use overseas services directly, fastest but data leaves the company; fully on-premises = AI runs on the company's own machines, data stays in-house but IT must maintain it; Hybrid = general data on cloud, confidential data on-premises. |
| Stage Gate | An acceptance checkpoint at each rollout phase — you must pass it before moving on, to avoid losing control halfway. |
| PoC (proof of concept) | A small-scale example built first to show visible results, proving "this really works" before scaling up. |
| Log (execution log) | The record the system keeps automatically of "what was done, when, and whether it succeeded or failed." |
| Human review node | A checkpoint in the workflow where "a person confirms," so AI does not make high-risk decisions directly. |
| KPI | A metric for measuring outcomes (e.g., how much time saved, how much the error rate dropped). |

## 2. 10-Item Quick Diagnostic

> Each item is scored 0-4 (see the §1 scoring table). Each item includes a "plain-language explanation" and "scenario anchors" for scores of 0 / 2 / 4 — pick the description closest to your company's current state; if you fall in between, score 1 or 3.

### Q1 | Does the company have an "officially approved, used-by-everyone" AI portal?

- **Dimension**: Tool Usage
- **Plain-language explanation**: This asks whether, when employees use AI, they each open free ChatGPT on their own phones (completely outside the company's control), or whether the company has one official, unified, controllable place for everyone to log in and use.
- **Scenario anchors**:
  - **0**: Nothing at all — everyone uses their own thing; the company has no idea who is using it or for what.
  - **2**: A few people share one paid ChatGPT account, but there is no announcement and no guidelines.
  - **4**: The company has set up a unified portal (e.g., a self-hosted ChatGPT interface like OpenWebUI), everyone has an account, IT can see usage, and there are guidelines on "what data must not be pasted in."

### Q2 | Do employees use AI every week to do their work (documents, email, summaries, analysis, reports)?

- **Dimension**: Tool Usage
- **Plain-language explanation**: This asks whether "using AI" is a few people occasionally playing around, or whether it has already become a weekly work habit for most colleagues.
- **Scenario anchors**:
  - **0**: Almost no one uses it, or only the boss occasionally asks something.
  - **2**: A few people in marketing and sales use AI to write copy or revise emails, but other departments show no movement.
  - **4**: Most colleagues across departments use AI weekly for summaries, drafts, organizing data, and analyzing numbers — it is already routine.

### Q3 | Has the department turned its "good ways of asking and doing things" into shareable assets?

- **Dimension**: Knowledge Consolidation
- **Plain-language explanation**: This asks whether colleagues' know-how has been written down and turned into a company asset, or whether everyone's good prompts are locked in their own heads. A Skill = the instructions and examples for a frequently done task, packaged for reuse.
- **Scenario anchors**:
  - **0**: Nothing is organized; everyone re-explains everything to the AI from scratch every time.
  - **2**: Some colleagues privately save a few useful prompts, but nothing is shared or organized.
  - **4**: The department has a shared prompt library / SOPs / templates / Skills, and checks for an existing reusable one before tackling a new task.

### Q4 | Can new hires quickly complete standard work using the company's ready-made toolkits?

- **Dimension**: Knowledge Consolidation
- **Plain-language explanation**: This asks whether new hires (or people unfamiliar with a task) can get up to speed on standard work via ready-made toolkits, rather than always needing a veteran to coach them hands-on.
- **Scenario anchors**:
  - **0**: New hires rely entirely on senior employees teaching them verbally — no documents, no tools.
  - **2**: There are some SOP documents, but new hires still have to figure out the AI tools themselves.
  - **4**: A new hire can open the shared Skills / documents and complete most high-frequency standard work, getting up to speed quickly.

### Q5 | Has the company "connected" AI to its other systems to read and write data automatically?

- **Dimension**: System Integration
- **Plain-language explanation**: This asks whether AI is integrated with the company's other systems and can read/write data automatically, rather than being used only standalone in a chat box. (Gmail = email; Sheets/Excel = spreadsheets; Notion/CRM/ERP = various systems the company uses — see the glossary if unsure.)
- **Scenario anchors**:
  - **0**: AI is completely standalone; getting it to process data means manual copy-paste.
  - **2**: You have tried using a tool to connect AI to Google Sheets or email, with one or two small experiments.
  - **4**: AI / n8n already stably integrates with email, spreadsheets, CRM, ERP, and other systems, reading and writing data automatically.

### Q6 | Does the company have automated workflows that "just run once set up"?

- **Dimension**: Process Automation
- **Plain-language explanation**: This asks whether there are workflows that "run themselves once set up" — after something happens, the system carries on automatically, notifies people when done, and leaves a record, without a person watching every step.
- **Scenario anchors**:
  - **0**: All processes must be run manually by a person; AI does not help with automation.
  - **2**: One or two small automated workflows have been built, but they are unstable and often need human intervention.
  - **4**: Workflows already auto-trigger, auto-execute, auto-notify the relevant people, and leave an execution log.

### Q7 | Does the company use AI Agents that "execute tasks on their own"?

- **Dimension**: Agent Application
- **Plain-language explanation**: Chat AI is "you ask, it answers"; an AI Agent goes further — you give it a goal, and it breaks the task into steps itself, uses tools to look things up, and reports the result back to you. This asks whether the company uses this kind of thing.
- **Scenario anchors**:
  - **0**: Only chat Q&A; no Agent that executes tasks on its own.
  - **2**: A simple Agent has been prototyped (e.g., auto-compiling a weekly report), still at the experimental stage.
  - **4**: An Agent already runs stably — able to decompose tasks, call tools, and report results — with a human-handover mechanism.

### Q8 | Has the company tried having multiple AI Agents work together?

- **Dimension**: Agent Application
- **Plain-language explanation**: A further step — not one Agent, but several specialist AI Agents working together like a small team. This asks whether the company has tried this kind of "AI team."
- **Scenario anchors**:
  - **0**: Nothing at all — not even a single Agent yet.
  - **2**: Planning, or experimenting on a small scale with multiple Agents dividing the work.
  - **4**: Multiple Agents (e.g., market, product, marketing, customer service, finance) already collaborate to complete cross-departmental tasks.

### Q9 | Does using AI have "braking mechanisms" (data classification, permissions, human review, usage logs)?

- **Dimension**: Implementation & Change
- **Plain-language explanation**: This asks whether using AI has "braking mechanisms" — which data can or cannot be given to AI, who can use it, whether high-risk decisions get a human double-check, and whether what the AI did is logged.
- **Scenario anchors**:
  - **0**: Nothing at all — employees can paste anything into AI, no one manages it, and there are no records.
  - **2**: It has been said verbally that "confidential data should not be pasted," but there is no system and no records.
  - **4**: There is data classification, permission control, human review for high-risk actions, and auditable AI usage logs.

### Q10 | Has management defined whether AI adoption is successful using "numbers"?

- **Dimension**: Implementation & Change
- **Plain-language explanation**: This asks whether the boss/managers define success of AI adoption with "numbers," or whether it is just "feels useful." A KPI = a metric for measuring outcomes.
- **Scenario anchors**:
  - **0**: No metrics at all; AI adoption runs entirely on gut feel.
  - **2**: Managers have expectations in mind (hoping it is faster, cheaper), but nothing is written as a concrete number.
  - **4**: Management has defined clear KPIs — e.g., "save X hours per month," "cut error rate by Y%," "responses Z% faster" — and tracks them regularly.

### Quick Interpretation

- 0-10 points: L0-L1, must first establish an AI usage portal and foundational training.
- 11-20 points: L1, individual usage exists; should start Skill-ification.
- 21-28 points: L2, departmental capabilities are accumulating; suitable for pushing into Workflow.
- 29-34 points: L3, foundational workflows exist; suitable for deploying Agents.
- 35-40 points: L4-L5, can evaluate Agent Team and governance expansion.

## 3. 25-Item Pre-Course Diagnostic

> Each item is scored 0-4, total 100 points. Each item has a one-line "plain-language + example" note; check the §1.2 Glossary for unfamiliar terms.

### A. Tool Usage

**A1 | Does the company have a unified or approved AI tool portal?**
→ **Plain language**: Whether employees each use their own thing, or the company has an official, controllable, unified portal. **Example**: a deployed OpenWebUI or unified enterprise accounts = high score; everyone using free versions outside the company's control = low score.

**A2 | Do employees know which data can and cannot be input into AI?**
→ **Plain language**: Whether colleagues have a mental "red line," knowing that confidential things like customer PII, financials, and contracts must not be casually pasted into AI. **Example**: a written guideline that has been trained on = high score; never discussed, left to individual judgment = low score.

**A3 | Can employees use AI to do summarization, rewriting, translation, classification, and report drafts?**
→ **Plain language**: This asks about actual hands-on ability, not "knowing AI can do it." **Example**: most colleagues can use AI to turn long documents into summaries, smooth out drafts, and produce report drafts = high score.

**A4 | Are there departmental or company-level AI usage guidelines?**
→ **Plain language**: Whether there are written rules (what can be used, what must not be done, who to contact if something goes wrong). **Example**: a one-page usage code announced to all staff = high score; only verbal reminders = low score.

**A5 | Can specific cases of personal AI productivity improvement be cited?**
→ **Plain language**: Whether you can tell a real story, rather than vaguely saying "AI is useful." **Example**: "Procurement uses AI for price comparison and quote organizing, saving 3 hours a week" = high score.

### B. Knowledge Consolidation

**B1 | Does the department have shared Prompts or task templates?**
→ **Plain language**: Whether useful "ways of asking" have become a shared asset. **Example**: the department has a shared prompt list that new hires use directly = high score; know-how locked in individual heads = low score.

**B2 | Has senior staff experience been turned into SOPs, Checklists, or Skills?**
→ **Plain language**: Whether veterans' experience has been "written down and packaged" so others can use it. **Example**: a senior agent's reply know-how organized into a Skill = high score.

**B3 | Is there a fixed, centralized location to manage Skills?**
→ **Plain language**: Whether these organized toolkits have a fixed home (shared folder, Notion, Git), or are scattered and impossible to find. **Example**: a unified knowledge base everyone knows where to find = high score.

**B4 | Can new hires apply Skills directly to complete high-frequency work?**
→ **Plain language**: Whether new hires can quickly complete frequent work using ready-made toolkits. **Example**: a new hire produces standard documents with shared Skills in their first week = high score.

**B5 | Is there an Owner, version, and quality-check mechanism for Skills?**
→ **Plain language**: Whether these toolkits are maintained, versioned, and quality-checked, to avoid going stale or producing errors. **Example**: each Skill has an owner and an update log = high score.

### C. Process Automation

**C1 | Has there been an inventory of "which high-frequency processes can be automated"?**
→ **Plain language**: Whether anyone has sat down to inventory "which work is done daily and is most suitable to hand to the system to run automatically." **Example**: an automation-candidate list has been compiled = high score.

**C2 | Is n8n or a similar tool already used to integrate workflows?**
→ **Plain language**: Whether an actual workflow-automation tool has been used to build something (n8n = connecting software with drag-and-drop to run automatically). **Example**: an n8n workflow that is live and runs daily = high score.

**C3 | Are there processes that can auto-trigger, execute, notify, and log?**
→ **Plain language**: Whether processes "run themselves" — kicking off automatically when an event happens, doing the work, notifying, and recording. **Example**: complaint email arrives → auto-classify → notify owner → log = high score.

**C4 | Are there "human review nodes" preventing AI from making high-risk decisions directly?**
→ **Plain language**: Whether high-risk things (refunds, outbound emails, financial decisions) keep a "person presses confirm" checkpoint. **Example**: AI drafts it, a person approves before it is sent = high score.

**C5 | Are there error notifications, automatic retries, and execution logs?**
→ **Plain language**: Whether, when a process fails, the system notifies a person, retries automatically, and leaves a record of "where it went wrong." **Example**: failure auto-sends a notification plus auto-retry = high score.

### D. System Integration

> This group inventories "which systems AI will need to connect to in the future." Answer based on your company's actual situation; ask IT or your information contact if unsure.

**D1 | Is integration with Gmail or enterprise email required?**
→ **Plain language**: Whether the company has a lot of email-based work to hand to AI. **Example**: customer service and sales process large volumes of mail daily = needed.

**D2 | Is integration with Google Sheets, Excel, or reporting systems required?**
→ **Plain language**: A lot of company data actually lives in spreadsheets, and AI needs to connect to use it. **Example**: quotes, tracking sheets, and KPI sheets are all in Excel/Sheets = needed.

**D3 | Is integration with Notion, SharePoint, Confluence, or a knowledge base required?**
→ **Plain language**: This asks which platforms hold the company's documents and knowledge — AI must connect to query them. If you do not recognize these names, just think "what is our company's document platform."

**D4 | Is integration with CRM, ERP, MES, PLM, QMS, or internal systems required?**
→ **Plain language**: This asks about the company's core operational systems (customers, finance/inventory/sales, shop-floor production, product design, quality — see the glossary if unsure). **Example**: AI helping with the sales process means connecting the CRM; helping with manufacturing scheduling means connecting ERP/MES.

**D5 | Are there APIs, databases, or Webhooks available for AI workflows to use?**
→ **Plain language**: In plain terms, "can these systems be opened up — can other software read/write them automatically." Ask IT if unsure — having an API = can be integrated for automation = high score.

### E. Agent Application

**E1 | Are there well-defined Agent tasks?**
→ **Plain language**: Whether you have a clear, specific job for the AI Agent to do, rather than vaguely saying "we want to do Agents." **Example**: "automatically compile last week's sales report every Monday" is a well-defined task.

**E2 | Can Agents call Skills or n8n Workflows?**
→ **Plain language**: Whether the Agent actually "has tools in hand" to use, or can only think abstractly. **Example**: the Agent can run an n8n workflow itself to look up data = high score.

**E3 | Are Agent permissions, data sources, and executable actions clearly defined?**
→ **Plain language**: Whether there is a clear boundary on what the Agent "can touch, can do, and cannot do." **Example**: clearly stating "the Agent can only read, not delete or modify" = high score.

**E4 | Do Agents have a reporting format, exception handling, and human-handover mechanism?**
→ **Plain language**: How the Agent reports when done, what happens when something goes wrong, and whether a person can take over when it gets stuck. **Example**: fixed-format reporting plus auto-handover to a human on exception = high score.

**E5 | Is there demand or experimentation around multi-Agent collaboration?**
→ **Plain language**: Whether you have considered or tried having several AI Agents divide the work. **Example**: having planned a "market Agent + copy Agent + finance Agent" working on a proposal together = high score.

### 25-Item Diagnostic Output

Each item is scored 0-4, total 100 points.

Output fields:

- Total score.
- Six-dimension scores.
- Initial maturity assessment.
- Recommended course ratio.
- Recommended deployment mode.
- Recommended priority PoC.

## 4. 50-Item Consulting Deep-Dive Diagnostic

The 50-item version adds company profile, deployment mode, and ROI on top of the 25-item version. This version is usually filled out with help from IT or the AI contact; each group below includes a short note.

### F. Company Profile

> This group is about understanding the company's basic background. Answer honestly; there are no right or wrong answers.

| No. | Question |
| --- | --- |
| F1 | Is the company's main industry R&D/manufacturing, marketing/services, finance, healthcare, government, education, retail, logistics, software, or other? |
| F2 | Which departments need AI the most? |
| F3 | Which work is the most repetitive, time-consuming, and error-prone? |
| F4 | Which work relies most heavily on the personal experience of senior employees? |
| F5 | Does management most value efficiency, cost, quality, revenue, innovation, governance, or security? |

### G. Deployment Mode

> This group determines where the AI should run (cloud / the company's own machines / hybrid). See §1.2 for unfamiliar terms.

| No. | Question | Plain language and example |
| --- | --- | --- |
| G1 | Does the company allow public-cloud AI to process general working data? | Public-cloud AI = services like ChatGPT and Gemini where data is sent to overseas companies. This asks whether company policy allows using it for general (non-confidential) work. |
| G2 | Is there core data that must not leave the corporate intranet? | Whether some data is required to "stay inside the company only — not on any cloud." |
| G3 | Is there PII, R&D, financial, contractual, customer confidential, or regulated data? | Inventory whether the company has this sensitive data — if so, the deployment mode must be more cautious. |
| G4 | Is there an existing data classification system? | Whether data is categorized by confidentiality (public / internal / confidential). Example: all documents are labeled with a classification = high score. |
| G5 | Is there an internal IT team able to maintain servers, Docker, APIs, databases, and permissions? | In plain terms, "does the company have its own technical staff who can sustain a system installed on its own machines." If not, lean toward cloud services. |

### H. System Inventory

> This group inventories the company's existing systems. Please fill in the actual system names; ask IT or your information contact if unsure.

| No. | Question | Plain language and example |
| --- | --- | --- |
| H1 | Is the main collaboration tool Google Workspace, Microsoft 365, Notion, SharePoint, or other? | The suite the company normally uses for email, meetings, and shared documents. |
| H2 | Is a CRM used? What is the system name? | CRM = the system for managing customers and sales (e.g., Salesforce, HubSpot). |
| H3 | Is an ERP / MES / PLM / QMS used? What are the system names? | Respectively the systems for finance/inventory/sales, shop-floor production, product design, and quality — see the glossary if unsure. |
| H4 | Are there internal APIs or databases that can be read? | Plain language: ask IT "can our systems let other programs read data automatically." |
| H5 | Are there mechanisms for permissions, Credentials, accounts, and audit management? | Credential = a system's account password / key. This asks whether these are centrally managed and have audit records. |

### I. ROI and Governance

> This group looks at "whether someone owns AI adoption and whether outcomes are being measured."

| No. | Question | Plain language and example |
| --- | --- | --- |
| I1 | Can you estimate how much time is saved per AI scenario? | Whether you can state "this used to take X hours, now it takes Y hours." |
| I2 | Can you track error rates, response speed, throughput, or quality improvements? | Whether numbers are being used to see the change AI brings. |
| I3 | Are there an AI Project Owner, IT Owner, and Department Owner? | Whether it is clear "who drives it, who owns the technology, who owns the business side." |
| I4 | Are there Stage Gate acceptance criteria? | Stage Gate = an acceptance checkpoint at each phase; you must pass it before moving on. |
| I5 | Are AI deployment outcomes reviewed regularly and the Roadmap adjusted accordingly? | Whether there are regular reviews and the plan is corrected based on results. |

### J. Consulting Diagnostic Focus

> This group has no standard answers — these are open questions consultants use to find diagnostic priorities. Answer based on your instinct.

| No. | Question |
| --- | --- |
| J1 | Which department or process do you most want to improve first? |
| J2 | What is the most concerning risk in AI deployment? |
| J3 | Which processes, if automated, would managers most appreciate? |
| J4 | Which data or systems are the largest obstacles to AI deployment? |
| J5 | What outcomes do you hope to see within 90 days? |

## 5. Deployment Mode Determination Items

Can be used together with the 25-item or 50-item versions. Check the descriptions matching your company's current state row by row, sum the corresponding scores, and the highest-scoring column is the recommended deployment mode.

| Item | Cloud AI | Hybrid | Fully On-Premises |
| --- | ---: | ---: | ---: |
| Low data sensitivity | +2 | +1 | 0 |
| Partially sensitive data | 0 | +2 | +1 |
| Core data must not go to cloud | 0 | +1 | +2 |
| Limited IT operations capability | +2 | +1 | 0 |
| Has IT and security teams | 0 | +1 | +2 |
| SaaS tools as the main stack | +2 | +1 | 0 |
| ERP / MES / internal systems as the main stack | 0 | +1 | +2 |
| Limited budget, must start quickly | +2 | +1 | 0 |
| High audit or regulatory requirements | 0 | +1 | +2 |

The highest score indicates the recommended deployment mode. If Hybrid and fully on-premises are close, we recommend starting with a Hybrid pilot, keeping core data on-premises.

## 6. Diagnostic Result Text Templates

### L1 Example

Your company's current AI applications mainly sit at the L1 Controlled AI Access stage. Employees have started using AI to boost personal productivity, but knowledge has not yet been consolidated into departmental assets. We recommend first establishing an OpenWebUI unified portal, AI usage guidelines, and departmental high-frequency Prompts, before moving on to L2 Skill-ification.

### L2 Example

Your company already has a preliminary L2 Knowledge Codification foundation. Departments have some SOPs, Prompts, or templates, but a stable and maintainable Skill Library has not yet formed. We recommend that the course focus on Skill design, departmental knowledge consolidation, and inventorying L3 Workflow candidate scenarios.

### L3 Example

Your company has the conditions for L3 Workflow Automation. The next step is to select high-value processes and use n8n to integrate Gmail, Sheets, Notion, CRM, API, ERP, and similar systems, completing 1-2 demonstrable PoCs while establishing permission, logging, and human-review mechanisms.

### L4 Example

Your company has the foundation to deploy Hermes Agent. We recommend starting from a single departmental task by designing Agent role cards, task boundaries, callable Skills, triggerable Workflows, and reporting formats, while avoiding letting the Agent handle high-risk decisions from the outset.

### L5 Example

Your company has the conditions to explore ClawTeam. We recommend selecting cross-departmental tasks — such as new product planning, customer management, or operational anomaly analysis — and designing multi-Agent role assignments, output integration, and quality-check processes, so the AI Team becomes a decision-support capability for management.
