AI Enablement Training Modules

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# Module 1: AI Literacy For Everyone

## Purpose

Help employees understand what AI models do, where they are useful, where they are risky, and how to use them as safe daily work assistants.

## Learning Outcomes

- Understand what AI models do.
- Identify safe everyday uses.
- Recognize sensitive-data boundaries.
- Review AI output responsibly.

## Core Artifact

- Employee AI Use Guidelines.

## Teaching Frame

Treat AI output like a draft from a fast assistant. Employees can use it to clarify, draft, summarize, organize, and improve non-sensitive work, but the person using it still owns the judgment.

This module should be practical and confidence-building. The audience may include people who are curious, skeptical, excited, nervous, or unsure what AI means for their work. The teaching goal is to give them a simple mental model: AI can help with language and structure, but people remain responsible for judgment, accuracy, privacy, and action.

AI literacy also includes a new workplace habit: making work clear enough that both people and AI systems can understand it. Organized source material, clear ownership, current job aids, and structured requests are part of responsible AI use.

## Why This Module Matters

AI literacy is the foundation for every other enablement activity. If employees do not understand the basic boundaries, they may either avoid AI entirely or use it in ways that create risk. This module creates a shared baseline for safe experimentation.

It also helps employees see AI as part of daily work improvement. The first useful wins are often low-risk tasks such as drafting, reformatting, summarizing non-sensitive content, creating checklists, or clarifying a confusing request.

## What AI Is Good At

- Clarifying a messy request.
- Drafting a first version.
- Comparing options.
- Summarizing non-sensitive material.
- Generating checklists.
- Creating training examples.
- Identifying missing questions.
- Converting notes into structured action items.
- Explaining a technical concept in plain language.
- Identifying where a process or folder is too messy for AI to use reliably.

## What AI Should Not Be Used For Without Approval

- Entering PHI or sensitive employee data into unapproved tools.
- Making clinical, legal, HR, billing, or financial decisions.
- Sending AI-generated content externally without review.
- Treating AI output as a source of truth.
- Automating actions without a fallback or owner.

## Practical Sessions

- "AI Basics For Work: What You Can Safely Use It For"
- "Turn A Messy Email Or Note Into A Clear Draft"
- "How To Ask Better Questions"
- "Finding AI And Automation Opportunities In Your Day"
- "What Not To Put Into AI"

## Suggested Teaching Flow

1. Start with plain-language examples of what AI can do.
2. Explain that AI output is a draft, not a verified source of truth.
3. Show safe and unsafe examples side by side.
4. Demonstrate how a vague request can become a clearer AI request.
5. Ask participants to identify low-risk work where AI could help.
6. Close by reinforcing data boundaries and human review.

## Discussion Prompts

- What kind of writing, organizing, or summarizing work slows you down?
- What information would you never put into an unapproved AI tool?
- When would AI output need another person to review it?
- What is one repeated task in your role that might be worth discussing with an AI enablement or automation partner?
- What folder, document, or job aid would need cleanup before AI could help someone use it?

## Exercises

- Rewrite a vague request into a clear prompt.
- Turn meeting notes into action items.
- Convert a confusing process into a checklist.
- Identify three repeated lookups in an employee role.
- Classify example use cases as low, medium, or high risk.
- Identify one source-of-truth document or folder that would make AI support more reliable if it were organized.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A list of safe everyday AI use cases.
- A list of information that should not be entered into unapproved AI tools.
- One improved prompt for a low-risk work task.
- One AI or automation opportunity from their own work.
- A basic risk classification for simple examples.

## Common Misunderstandings To Address

- AI is not always correct.
- AI does not replace policy, professional judgment, or approval paths.
- A confident answer still needs verification.
- Safe AI use depends on the tool, the data, the workflow, and the review process.
- The best starting point is low-risk, high-friction work.
- AI will not automatically fix disorganized source material.

## Key Message

AI becomes useful when the work, data, source material, guardrails, owner, and outcome are clear.

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# Employee AI Use Guidelines

## Purpose

Provide practical guidance for safe and useful AI adoption by employees.

## Draft Principles

- Use AI to support work, not replace professional judgment.
- Do not enter sensitive, patient, employee, financial, or confidential information unless the tool and use case are approved.
- Review AI-generated output before using it.
- Be clear about the task, context, and expected format when prompting.
- Treat AI output as a draft, not a final authority.
- Escalate uncertain or higher-risk use cases for review.

## Good Candidate Tasks

- Drafting non-sensitive communication
- Summarizing non-sensitive information
- Brainstorming process improvements
- Creating checklists
- Reformatting text
- Explaining general concepts
- Generating training examples

## Higher-Risk Tasks

- Patient-specific clinical decisions
- Protected health information
- Employee or HR-sensitive information
- Financial or legal decisions
- Automated actions without human review
- Content sent externally without review

## Questions To Clarify

- Which AI tools are approved for which data types?
- What review process is required for new AI use cases?
- What logging or documentation is expected?
- Who approves higher-risk use cases?

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# NotebookLM Prompt: Module 1 AI Literacy For Everyone

## Role
You are a senior AI enablement facilitator creating a practical healthcare workplace training deck.

## Task
Turn Module 1 into a concise slide deck outline about AI literacy, safe use, human review, and sensitive-data boundaries.

## Target Audience
employees with mixed AI familiarity across clinical, operational, administrative, leadership, IT, training, and support roles.

## Goal
Help employees understand what AI is useful for, what it should not be used for without approval, how to review AI output, how organized source material affects AI usefulness, and how to identify low-risk AI or automation opportunities.

---

## The Creative Directive
Style Guide: Practical, calm, plain-language, workplace-oriented, and healthcare-aware. Avoid hype, fear, and abstract AI theory.

Narrative Arc: Why AI literacy matters; AI as thinking partner; AI-ready work and organized knowledge; useful low-risk tasks; boundaries; review habits; opportunity spotting; human accountability.

---

## Slide Outline Requirements

## Slide 1: AI Literacy For Everyone
**Focus:** Introduce the module as practical AI enablement for daily work.
**Prompt:** Title slide with clean workplace visual; show people, process, data, and review as connected elements.

## Slide 2: Why This Matters Now
**Focus:** AI literacy prevents both risky use and total avoidance.
**Prompt:** Contrast two simple paths: unsafe experimentation vs. informed, responsible use.

## Slide 3: A Simple Mental Model
**Focus:** AI helps with language, structure, and drafts, but people keep judgment.
**Prompt:** Show AI as a drafting partner beside a human reviewer, not replacing the person.

## Slide 4: What AI Is Good At
**Focus:** Clarifying requests, drafting, comparing options, summarizing non-sensitive material, checklists, and training examples.
**Prompt:** Use a grid of practical work tasks with simple icons and short labels.

## Slide 5: What AI Is Not
**Focus:** AI output needs verification against trusted sources, policy, approval paths, and human decision owners.
**Prompt:** Use a clear "not this" layout with human accountability emphasized.

## Slide 6: Sensitive Data Boundaries
**Focus:** Do not enter PHI, sensitive employee data, financial, legal, or confidential information into unapproved tools.
**Prompt:** Show protected data categories behind a locked boundary.

## Slide 7: Safe Everyday Examples
**Focus:** Low-risk uses include non-sensitive drafting, reformatting, checklists, explanations, and organizing notes.
**Prompt:** Show before-and-after examples of messy notes becoming structured output.

## Slide 8: Review Before Use
**Focus:** AI output must be checked for accuracy, completeness, tone, source fit, and risk.
**Prompt:** Create a review checklist visual with human signoff.

## Slide 9: Better Questions Get Better Drafts
**Focus:** A good request includes task, context, constraints, output format, and review criteria.
**Prompt:** Show vague prompt transformed into a clearer work request.

## Slide 10: AI-Ready Work
**Focus:** AI works better when documents, job aids, owners, and outcomes are clear.
**Prompt:** Show a messy folder becoming a source-of-truth structure with owner, date, and review loop.

## Slide 11: Spotting AI Opportunities
**Focus:** Look for repeated questions, repeated lookups, copy/paste work, reformatting, and confusing processes.
**Prompt:** Show a workflow with friction points highlighted.

## Slide 12: Practice Activity
**Focus:** Participants rewrite a vague request, identify unsafe data, and name one low-risk task.
**Prompt:** Use an interactive worksheet-style slide with three activity boxes.

## Slide 13: Common Misunderstandings
**Focus:** AI can be wrong, confidence is not verification, and safe use depends on tool, data, workflow, and review.
**Prompt:** Use myth vs. reality cards with concise corrections.

## Slide 14: Key Takeaways
**Focus:** AI is useful when work, data, source material, guardrails, owner, and outcome are clear.
**Prompt:** Summarize the module with five connected pillars.

## Slide 15: Next Step
**Focus:** Ask employees to identify one low-risk, high-friction task for discussion or intake.
**Prompt:** End with a simple call-to-action and a path from idea to review.

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# Facilitator Guide: Module 1 — AI Literacy For Everyone

## Module Info

| Field | Detail |
|---|---|
| Audience | All staff — clinical, operational, administrative, leadership, IT, training, and support |
| Duration | 60–90 minutes |
| Format | Presentation + group discussion + individual exercise |
| Prerequisites | None — this is the entry point for all participants |
| Builds on | N/A |
| Feeds into | Module 2: Prompting As Structured Communication |

---

## Facilitator Overview

This is the foundational module. Your job is not to sell AI — it is to give people a shared, honest mental model so they can engage with it safely. Expect a wide range of reactions: skepticism, anxiety, excitement, and indifference. All are valid. Your goal is to move everyone to a calm, informed starting point.

Avoid demo-mode. This module should feel like a workplace orientation, not a product pitch. Keep examples grounded in real organizational work.

**Prep checklist:**
- [ ] Review the Employee AI Use Guidelines before the session
- [ ] Prepare two or three real organizational examples of low-risk AI use (see examples below)
- [ ] Prepare one clear example of what NOT to put into AI (non-PHI but realistic)
- [ ] Prepare one example of a messy folder, outdated job aid, or unclear source-of-truth problem
- [ ] Have the prompt exercise ready on paper or as a slide activity
- [ ] Know your escalation contact if participants raise compliance questions you cannot answer

---

## Section-by-Section Facilitator Notes

### Opening (5–10 min)
Start by asking: *"Who has already used an AI tool for something — at work or at home?"* Let a few people share. This normalizes the conversation and lets you gauge the room. Do not correct or critique what they share yet.

Then frame the session: *"Today we are building a shared understanding of what AI can and cannot do, and where the organization draws its boundaries."*

### Why This Matters (5 min)
Acknowledge both failure modes: avoidance and overuse. Some staff will be reluctant; others may already be using unapproved tools without realizing the risk. Neither response is ideal. This module aims for informed, responsible engagement.

**Anticipated question:** *"Are we being forced to use AI?"*
**Response:** No. This training is about awareness and safe use when you choose to use it. There is no mandate to use AI for your work.

### The Mental Model: AI as Thinking Partner (10 min)
The key message is: AI helps with language and structure; humans keep judgment. Reinforce this clearly. The most common early mistake is treating a confident-sounding AI answer as verified fact.

Use this framing: *"Think of AI like a very fast, very articulate drafting assistant who has read a lot of material but does not know your patient, your department, your history, or the organization's current policies. You still have to check the work."*

**Anticipated question:** *"How is this different from Googling something?"*
**Response:** AI generates a response rather than returning existing pages. That means it can produce plausible-sounding but incorrect content with no source link. You cannot verify it the same way.

### What AI Is Good At (10 min)
Walk through the list from the module. Pause on at least two organization-specific examples (see below). Ask participants to think of one task in their own role that fits.

**Organization-specific examples to use:**
- Drafting a meeting agenda from bullet-point notes
- Turning a confusing policy question into a plain-language summary (non-PHI)
- Generating a checklist for an onboarding process
- Rewriting a long email to be shorter and clearer
- Organizing action items from a department meeting
- Drafting a job aid from a verbal walkthrough of a process

### AI-Ready Work And Organized Knowledge (5 min)
Make the broader point explicit: AI asks teams to make work clearer. If policies, job aids, workflow changes, and team instructions are scattered or outdated, AI will struggle in the same places people struggle.

Use a simple example: a department folder with three versions of the same job aid and no owner. Ask: *"If a new employee could not tell which file to trust, why would we expect AI to do better?"*

Reinforce that employees do not need to become technical experts. The practical habit is making normal work more explicit: clear source of truth, clear owner, clear date, clear review cycle.

### Sensitive Data Boundaries (10–15 min)
This is the highest-stakes section. Be specific. Do not leave participants guessing about what counts as sensitive.

**Categories to name clearly:**
- PHI (Protected Health Information): any information that could identify a patient and relates to their health, treatment, or payment
- Employee data: performance records, compensation, HR investigations, medical accommodations
- Financial data: budgets, contracts, billing detail
- Legal: anything under attorney review or related to litigation
- Credentials or system access information

**Organization-specific scenarios to walk through:**
- *"I want to paste a patient case into ChatGPT to help me write a care summary."* → Not allowed. This is PHI.
- *"I want to paste an anonymized scenario to help me draft a training example."* → May be acceptable depending on the tool and approval status. Check the guidelines.
- *"I want to summarize meeting notes that include employee names and performance issues."* → Not appropriate for unapproved tools. HR-sensitive.

**Anticipated question:** *"What about Microsoft Copilot? Is that approved?"*
**Response:** Refer to the Employee AI Use Guidelines and your IT contacts for the current approved tool list. Approved tools and approved uses are not always the same — the tool being licensed does not mean every use case is permitted.

### Safe Everyday Examples (5 min)
Keep this practical. Show the before/after of turning messy notes into structured output. Participants respond well to simple, tangible transformations.

### Review Before Use (5 min)
Reinforce: AI output is a draft. Before using anything AI produced, check it for accuracy, completeness, tone, and data appropriateness. The person who sends the email or signs the document is still accountable.

### Practice Activity (10–15 min)
Choose one or two of these:
1. Rewrite a vague request into a clearer prompt (example: *"Make this better"* → *"Rewrite this paragraph to be shorter and more direct for a non-clinical audience"*)
2. Classify three example use cases as low, medium, or high risk
3. Identify one repeated low-risk task in your own role

Debrief as a group. Ask two or three people to share.

### Close (5 min)
Return to the key message: *"AI becomes useful when the work, data, source material, guardrails, owner, and outcome are clear."*

Invite participants to share one opportunity they identified with their manager or an AI enablement contact.

---

## Organization-Specific Examples Bank

| Scenario | Safe? | Notes |
|---|---|---|
| Draft a team meeting agenda from notes | Yes | Low risk, no sensitive data |
| Summarize a non-clinical policy document | Yes | Confirm no PHI in the document |
| Rewrite a patient-facing letter (no PHI) | Yes with review | Remove any patient-identifiable details first |
| Paste a patient case summary | No | PHI — not appropriate for unapproved tools |
| Generate interview questions for a role | Yes | Low risk, common HR use |
| Summarize a department budget discussion | No | Financial data — sensitive |
| Create a checklist for a clinical procedure | With caution | Clinical content needs SME review before use |
| Draft onboarding materials for a new employee | Yes | Low risk if no sensitive employee data included |

---

## Where Sessions Tend to Stall

- **"But I already use ChatGPT at work."** Acknowledge without shaming. Redirect to the guidelines and explain the difference between personal use and sanctioned use on organizational systems or with organizational data.
- **PHI boundary questions.** If you are not certain, do not guess. Refer to the guidelines and offer to follow up. Do not improvise policy.
- **"AI is going to replace my job."** This anxiety is real. Acknowledge it directly: *"That concern is worth taking seriously. Today's focus is on how to use AI as a tool that helps you, not replaces you. The organization's enablement program is designed around human accountability."*
- **Tool-specific questions you can't answer.** Know your escalation path. Offer to connect participants with the right contact rather than improvising.

---

## Knowledge Check Questions

Use these at the end of the session or as a brief written self-assessment:

1. Name two types of information that should not be entered into an unapproved AI tool.
2. Why does a confident AI answer still need verification?
3. Give one example of a low-risk, high-friction task in your role where AI could help.
4. What should you do before using AI-generated content in your work?
5. Who remains accountable for AI output in a workflow?
6. Why does messy or outdated source material make AI less useful?

---

## Tips for Different Audience Types

**Clinical staff:** Focus on the PHI boundary and the "drafting partner, not clinical authority" framing. They are often more cautious and respond well to clear rules.

**Administrative and operational staff:** Focus on everyday low-risk uses. They often have the most copy-paste and reformatting friction and may find the most immediate value.

**Leadership:** Emphasize the governance angle — approved tools, human accountability, and the risk of staff using unapproved tools without guidance.

**IT staff:** They may already have technical knowledge. Redirect toward the organizational boundary-setting aspects and what "approved" means in the organization.

**Skeptical participants:** Do not oversell. Acknowledge limitations openly. Skeptics often become the most careful and useful AI reviewers.

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# Module 2: Prompting As Structured Communication

## Purpose

Teach employees, analysts, builders, and leaders to treat prompting as structured communication. Good prompting usually looks like a clear work request: task, context, boundaries, and the kind of answer needed.

## Learning Outcomes

- Write clear prompts.
- Specify context and output format.
- Ask for alternatives and tradeoffs.
- Improve prompts through iteration.

## Core Artifact

- Prompt Record template.

## Teaching Frame

The best prompt is a clear work request. A useful prompt usually includes the role, task, context, inputs, constraints, output format, and review criteria.

This module should demystify prompting. Participants do not need clever phrases. They need to communicate clearly enough that the model can help with the actual work. Prompting should feel like writing a clear request to a capable assistant who still needs context, boundaries, and review.

Prompting is also a signal that work itself is changing. If a task cannot be described clearly, sourced clearly, or reviewed clearly, AI will struggle with it. The habit of writing better prompts builds the broader habit of making work explicit.

## Why This Module Matters

Prompting is the entry point for many employees, analysts, managers, and builders. If people learn prompting as a reusable work skill, they can create better drafts, better summaries, better requirements, and better planning artifacts.

Prompting also teaches good thinking habits. A clear prompt forces the user to define the task, identify the audience, name constraints, and describe what a good answer looks like.

## Prompt Elements

- Role: What perspective should the model take?
- Task: What should it do?
- Context: What does it need to know?
- Inputs: What material should it use?
- Constraints: What should it avoid?
- Output: What format should it return?
- Review criteria: What makes the answer good?

## Example Prompt Pattern

Use this structure when teaching:

```text
Act as [role].
Your task is to [task].
Use this context: [context].
Use these inputs: [inputs].
Avoid [constraints].
Return the answer as [format].
A good answer should [review criteria].
```

This pattern is intentionally simple. Participants should learn to adapt it rather than treat it as a rigid formula.

## Good Prompting Use Cases

- Drafting a first version.
- Improving wording.
- Summarizing non-sensitive material.
- Organizing meeting notes.
- Creating checklists.
- Comparing options.
- Finding missing questions.
- Turning informal ideas into structured requests.
- Turning informal team knowledge into clearer source material, instructions, or checklists.

## Prompt Record Practice

A reusable prompt should capture:

- Prompt name.
- Purpose.
- Tool or model.
- Prompt text.
- Expected output.
- Test cases.
- Results.
- Improvements.
- Version notes.

## Suggested Teaching Flow

1. Start with a weak prompt and show the weak output it produces.
2. Add role, context, constraints, and output format.
3. Compare the improved output to the original output.
4. Ask participants to identify what changed and why it helped.
5. Introduce the prompt record as the way to preserve useful prompts.
6. Have participants create a small prompt record with test cases.

## Discussion Prompts

- What context do you usually forget to include when asking for help?
- What output format would make AI answers easier to review?
- What constraints would make an AI answer safer or more useful?
- When does a one-time prompt become worth saving as a reusable prompt record?
- What information lives in people's heads today that would need to be written down for AI to help reliably?

## Exercises

- Rewrite a vague prompt into a structured prompt.
- Ask for three different output formats and compare usefulness.
- Add constraints to make an answer safer or more specific.
- Create a reusable prompt record with at least two test cases.
- Improve a prompt after reviewing weak output.
- Rewrite an informal request so it names the source material, owner, and review criteria.

## Expected Outputs

By the end of the module, participants should be able to produce:

- One structured prompt for a real low-risk task.
- One before-and-after prompt comparison.
- One reusable prompt record.
- At least two test cases for a saved prompt.
- A short explanation of why the improved prompt works better.

## Common Misunderstandings To Address

- Prompting works better when the request is clear and honest.
- More words do not automatically make a better prompt.
- The model cannot infer missing business context reliably.
- Output format matters because it affects review and reuse.
- Prompt quality should be judged by whether the output helps the workflow.
- Prompting cannot compensate for missing, stale, or conflicting source material.

## Key Message

Prompting maps directly to how good analysts and engineers already communicate: clear task, clear context, clear sources, clear constraints, clear output, and clear review.

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# Prompt Record

## Prompt Name


## Purpose


## Tool Or Model


## Prompt

```text

```

## Expected Output


## Test Cases

- 

## Results


## Improvements

- 

## Version Notes

-

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 2 Prompting As Structured Communication

## Role
You are a senior AI enablement facilitator creating a practical workplace training deck on prompting.

## Task
Turn Module 2 into a concise slide deck outline that teaches prompting as structured communication and source-aware work requests.

## Target Audience
employees, analysts, managers, automation builders, and leaders who need to write clearer AI requests for low-risk work.

## Goal
Help participants write clearer prompts, specify sources, context, and output format, add useful constraints, review outputs, and preserve reusable prompts as prompt records.

---

## The Creative Directive
Style Guide: Practical, plain-language, example-driven, and facilitator-friendly. Avoid jargon and prompt-hacking language.

Narrative Arc: Start by demystifying prompting; show why vague requests fail; explain that AI pushes work toward clearer tasks and reusable knowledge; introduce the prompt elements; transform a weak prompt; practice output formats and constraints; close with reusable prompt records.

---

## Slide Outline Requirements

## Slide 1: Prompting As Structured Communication
**Focus:** Introduce prompting as clear work communication.
**Prompt:** Title slide with a simple request moving from messy notes to organized output.

## Slide 2: Write A Clear Work Request
**Focus:** Prompting works best with a clear task, context, constraints, and output.
**Prompt:** Show "magic phrase" crossed out and "clear work request" highlighted.

## Slide 3: Why Prompting Matters
**Focus:** Better prompts produce better drafts, summaries, requirements, plans, and checklists.
**Prompt:** Use a practical workplace grid of common prompt-supported tasks.

## Slide 4: A Useful Prompt Has Parts
**Focus:** Introduce role, task, sources, context, inputs, constraints, output, and review criteria.
**Prompt:** Show the prompt elements as labeled building blocks.

## Slide 5: Role And Task
**Focus:** The model needs to know what perspective to use and what job to perform.
**Prompt:** Compare "help me" with a clearer role-and-task example.

## Slide 6: Context And Inputs
**Focus:** The model cannot reliably infer missing business context.
**Prompt:** Show missing context as blank puzzle pieces being filled in.

## Slide 7: Constraints And Boundaries
**Focus:** Constraints make outputs safer, more specific, and easier to review.
**Prompt:** Use guardrail visuals around a sample prompt.

## Slide 8: Output Format
**Focus:** Format matters because it affects usability and review.
**Prompt:** Show the same content returned as paragraph, checklist, table, and action list.

## Slide 9: Review Criteria
**Focus:** Tell the model what a good answer should include or avoid.
**Prompt:** Use a quality checklist beside a generated draft.

## Slide 10: Before And After Prompt
**Focus:** Demonstrate a vague prompt transformed into a structured prompt.
**Prompt:** Split screen: weak prompt on left, improved prompt on right, with annotations.

## Slide 11: Practice: Improve The Prompt
**Focus:** Participants rewrite a vague prompt using the seven prompt elements.
**Prompt:** Worksheet-style slide with fill-in sections for each prompt element.

## Slide 12: Prompt Records
**Focus:** Useful repeated prompts should be saved, tested, improved, and versioned.
**Prompt:** Show a prompt record template with purpose, prompt, expected output, tests, results, and version notes.

## Slide 13: Work Is Becoming More Explicit
**Focus:** AI rewards clear tasks, clear sources, clear owners, and clear review criteria.
**Prompt:** Show informal team knowledge becoming instructions, source links, and a reviewable output.

## Slide 14: Common Misunderstandings
**Focus:** More words are not always better; prompting is not tricking the model; output must be reviewed.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 15: Key Takeaways
**Focus:** Prompting works best when the work request is clear, sourced, bounded, formatted, reviewed, and reusable when repeated.
**Prompt:** End with a clean summary: task, sources, context, constraints, output, review.

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 2 — Prompting As Structured Communication

## Module Info

| Field | Detail |
|---|---|
| Audience | All staff; especially valuable for analysts, coordinators, project managers, and department leads |
| Duration | 60–90 minutes |
| Format | Hands-on workshop — participants write and improve prompts in session |
| Prerequisites | Module 1: AI Literacy For Everyone |
| Builds on | M1 mental model (AI as drafting partner, human keeps judgment) |
| Feeds into | Module 3: Workflow Discovery |

---

## Facilitator Overview

This module is best run as a workshop, not a lecture. Participants learn prompting by doing it — writing a weak prompt, getting a weak result, improving it, and seeing the difference. The goal is to demystify prompting and make it feel like a professional communication skill rather than a technical trick.

Your job is to coach, not demonstrate. Show one example, then put participants to work. The more they write, the more they retain.

**Prep checklist:**
- [ ] Choose a real AI tool participants can access during the session (or prepare printed example outputs)
- [ ] Prepare one weak prompt and its output as an opening example
- [ ] Prepare one strong prompt covering all seven elements and its output for comparison
- [ ] Print or share the Prompt Record template
- [ ] Choose two or three organization-specific tasks as exercise starting points (see bank below)
- [ ] Prepare one example where the prompt fails because the source material or owner is unclear
- [ ] Be ready to demo iteration — improving a prompt mid-session in response to weak output

---

## Section-by-Section Facilitator Notes

### Opening (5–10 min)
Start with a live or pre-prepared example of a weak prompt:

> *"Summarize this for me."*

Show the output — probably vague, too long, or missing the point. Then ask: *"What was missing from that request?"* Let the group identify what context, format, or constraints were absent. This sets up the rest of the module organically.

**Do not start with the framework.** Start with the failure, then introduce the framework as the solution.

### Why Prompting Is a Work Skill (5 min)
The practical shift: good prompting usually looks like good workplace communication. A clear prompt has the same qualities as a useful project brief or meeting agenda: task, context, constraints, and expected output.

This framing helps participants who feel intimidated. They already know how to communicate clearly — prompting is just applying that skill to AI.

Add the larger workforce shift: prompting exposes whether the work itself is clear. If someone cannot name the task, audience, source material, owner, constraints, or review criteria, the AI problem is really a work-definition problem.

### The Seven Prompt Elements (15 min)
Walk through each element with a concrete organization-related example. Do not rush this section. Participants need to internalize each element before they can write good prompts independently.

**Annotated example for organizational context:**

```
Act as a clear, plain-language writer familiar with healthcare operations.

Your task is to rewrite the attached onboarding checklist so it is easier 
for a new employee in a non-clinical administrative role to follow on their 
first day.

Use this context: The audience has no clinical background and may be 
unfamiliar with Epic, organizational systems, or healthcare terminology.

Use these inputs: [paste the existing checklist]

Avoid jargon, acronyms without explanation, and steps that assume prior 
system access.

Return the answer as a numbered checklist with a one-sentence explanation 
for each step.

A good answer is clear enough that a new employee could follow it without 
asking for help.
```

Ask participants to identify each element in the example. This reinforces the framework without requiring memorization.

**Anticipated question:** *"Do I really need all seven elements every time?"*
**Response:** Not always. For simple tasks, four or five elements are enough. For anything you plan to reuse or share, capturing all seven makes the prompt easier to test, improve, and hand off.

**Source reminder:** For prompts based on internal content, ask participants to name the source explicitly. "Use the current onboarding guide dated May 2026" is stronger than "use our onboarding information." Clear sources make output easier to review.

### Prompt Iteration Exercise (15–20 min)
Give participants a weak prompt based on a real organizational task. Have them improve it using the seven elements. Then compare outputs (if running live) or discuss what they added and why.

**Good starter prompts for this exercise:**
- *"Fix this email."* (improve to: draft a revised version of this email for a non-clinical department head audience, focusing on clarity and next steps)
- *"Explain this policy."* (improve to: summarize the key points of this policy in plain language for a new employee with no prior healthcare experience)
- *"Make a checklist."* (improve to: create a step-by-step checklist for processing an incoming referral in the scheduling department, formatted so each step can be checked off)

### Output Format Matters (5 min)
Spend time on this because participants often skip it. The same content returned as a paragraph vs. a table vs. a bulleted list has very different review and reuse value.

Ask: *"If you were going to share this output in a team meeting, what format would make it easiest to read?"* Then show how adding a format instruction changes the output.

### Prompt Records (10 min)
Introduce the Prompt Record as the way to preserve useful prompts. The key selling point: a good prompt you forget is wasted work. A prompt record is how individuals and teams build reusable knowledge.

Emphasize: the record should include test cases. A prompt is not ready to share or reuse until you know it works consistently.

**Anticipated pushback:** *"This feels like extra paperwork."*
**Response:** It is — the first time. But a saved prompt record means the second person on the team does not have to figure this out from scratch. It also makes it easier to improve the prompt when the output is not quite right.

### Exercise: Build a Prompt Record (10–15 min)
Have participants choose a real low-risk task from their own work and fill in a prompt record with at least two test cases. This is the module's primary deliverable.

Debrief: ask one or two participants to share their prompt and explain why the format or constraints they chose matter.

### Close (5 min)
Key message: *"Prompting maps directly to how good analysts and engineers already communicate: clear task, clear context, clear sources, clear constraints, clear output, and clear review."*

Ask participants to commit to using their prompt record on a real task before the next session.

---

## Organization-Specific Prompt Examples Bank

| Task | Weak Prompt | Improved Prompt (abbreviated) |
|---|---|---|
| Onboarding doc | "Clean this up" | "Rewrite this onboarding guide for a new administrative employee with no healthcare background. Use numbered steps and plain language. Avoid acronyms." |
| Meeting notes | "Summarize the meeting" | "Convert these meeting notes into a structured action item list. Format: Owner / Action / Due Date. Flag any items with no assigned owner." |
| Policy question | "Explain this policy" | "Summarize the key rules in this HR policy in three to five plain-language bullet points for a manager who needs to explain it to their team." |
| Job posting | "Write a job description" | "Draft a job posting for a Patient Access Coordinator. Audience: job seekers with no prior healthcare experience. Include responsibilities, required skills, and a plain-language description of how the role supports patients." |
| Department update | "Write an update email" | "Write a brief department update email from a department manager to their team. Tone: clear and direct. Include: what changed, why it matters, and what employees need to do next." |
| Training exercise | "Make a quiz" | "Create five multiple-choice questions to test whether a new scheduler understands the referral intake process. Each question should have one clearly correct answer and two plausible distractors." |

---

## Where Sessions Tend to Stall

- **Participants stare at a blank prompt.** Give them a starter task from the bank above. Starting from something real removes the blank-page friction.
- **"The AI gave me something totally wrong."** Use this as a teaching moment. Ask: what element was missing? Usually it is context, constraints, or output format. Improve the prompt together.
- **Participants focus on the model, not the prompt.** Redirect: the model matters less than the clarity of the request. A clear prompt produces better output on any capable model.
- **Concern about sharing prompts with the team.** Acknowledge that some prompts may contain context that is role-specific. The prompt record can be shared at the template level (without the actual inputs) for reuse.

---

## Knowledge Check Questions

1. What are the seven elements of a well-structured prompt?
2. Why does output format matter when writing a prompt?
3. What is a Prompt Record and why is it useful?
4. Give an example of a constraint you would add to make an AI answer safer or more specific.
5. How do you know when a prompt is ready to save and share?
6. Why should a reusable prompt name the source material and review criteria?

---

## Tips for Different Audience Types

**Analysts and coordinators:** These participants often take to prompting quickly because they already write structured requests. Push them toward the Prompt Record and test cases — they are the most likely to build something reusable.

**Clinical staff:** Focus on non-clinical prompt examples first. Reinforce that prompts involving clinical content need SME review of the output before any use.

**Managers and leaders:** Frame prompting as a way to get better first drafts for communications, planning documents, and summaries. Emphasize the time-saving value of structured requests over back-and-forth iteration.

**Less confident participants:** Pair them for the exercise. The conversation between two people working on a prompt together often produces better results than working alone, and reduces anxiety.

Copy the actual module source notes to use as context in NotebookLM, PowerPoint Copilot, or another content-generation tool.

# Module 3: Workflow Discovery

## Purpose

Teach teams to start with the real work before choosing a tool. Workflow discovery turns pain points, complaints, and repeated tasks into structured AI, automation, app, reporting, or process-change opportunities.

## Learning Outcomes

- Map a real process.
- Identify friction.
- Find automation and AI candidates.
- Translate complaints into use cases.

## Core Artifact

- Solution Intake Form.

## Teaching Frame

Start with the work. Most AI conversations begin with Copilot, ChatGPT, Copilot Studio, Power Automate, Power Apps, Azure AI, or agents. The better starting point is the workflow itself.

This module teaches people to slow down before solutioning. A vague idea like "Can AI fix this?" should become a clear description of the current process, pain points, triggers, data sources, decisions, exceptions, risks, and desired outcome.

In the AI era, discovery should also ask whether the work is AI-ready. That means checking whether the knowledge is organized, current, findable, owned, and structured enough for a person or AI system to use without relying on tribal knowledge.

## Why This Module Matters

Most failed AI or automation ideas are not caused by a weak model. They fail because the team did not understand the work well enough. Workflow discovery prevents overbuilding, reduces risk, and improves the quality of requests that reach technical teams.

Discovery also helps employees feel heard. Instead of jumping to a tool, the facilitator helps the team describe what is actually difficult, repetitive, unclear, or risky.

## Discovery Questions

- What work is actually happening?
- Who does it?
- What triggers it?
- What information is needed?
- Where does that information live?
- What decisions are made?
- What is repetitive?
- What is risky?
- What is frustrating?
- What would a good outcome look like?
- Where do the policies, job aids, workflow changes, and team notes live?
- Are those sources current, organized, and owned?

## Opportunity Signals

- Repeated questions.
- Repeated lookups.
- Manual reformatting.
- Copy/paste work.
- Status updates.
- Routing decisions.
- Policy interpretation.
- Intake triage.
- Document summarization.
- Training or onboarding gaps.
- Spreadsheet processes that have outgrown spreadsheets.

## What To Capture

- Trigger: What starts the work?
- Actor: Who performs each step?
- Input: What information is needed?
- Source: Where does the information come from?
- Decision: What judgment or routing happens?
- Output: What is produced or changed?
- Exception: What happens when the normal path fails?
- Frequency: How often does this occur?
- Friction: What slows the work down?
- Risk: What could go wrong?
- Owner: Who is accountable for the process?
- Knowledge readiness: Are the source documents findable, current, named clearly, and maintained by an owner?

## Practical Sessions

- "From Stakeholder Pain Point To Structured Use Case"
- "Finding AI And Automation Opportunities In Your Day"
- "Turning Work Inventory Into Automation Candidates"
- "From Excel Tracker To SharePoint, Power App, Or Dataverse"

## Suggested Teaching Flow

1. Start with a messy complaint or informal idea.
2. Ask discovery questions until the workflow is clear.
3. Identify the trigger, inputs, decisions, outputs, and exceptions.
4. Highlight friction points and repeated steps.
5. Separate the problem from the proposed solution.
6. Capture the result in a solution intake form.
7. Identify whether the next step is training, process redesign, AI, automation, app, reporting, or further discovery.

## Discussion Prompts

- What workarounds have become normal in this process?
- Where do people copy, paste, retype, or reformat information?
- What questions are asked repeatedly?
- What breaks when the normal process does not work?
- Who owns the process today, and who should own the improved version?

## Exercises

- Convert a workflow complaint into a problem statement.
- Map a manual process into trigger, action, exception, and owner steps.
- Identify three repeated lookups in a role.
- Capture a use case using a solution intake form.
- Ask AI to identify missing discovery questions.
- Assess whether a team folder or knowledge source is ready for AI-assisted use.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A clear problem statement.
- A simple workflow map or step list.
- A list of friction points.
- A list of data sources and owners.
- A completed draft solution intake form.
- A preliminary recommendation for next-step assessment.

## Common Misunderstandings To Address

- A proposed tool is not the same as a defined problem.
- Repetition alone does not mean automation is appropriate.
- Unclear ownership is a process issue that needs to be resolved before AI can help much.
- Exceptions matter because they often determine whether automation is safe.
- The discovery output should be useful even if the final answer is not AI.
- Messy folders and undocumented work are workflow problems as much as content problems.

## Key Message

Only after the work and its knowledge sources are understood should the team decide whether the answer is a prompt, agent, flow, app, dashboard, training artifact, Epic workflow change, custom engineering path, cleanup effort, or no AI at all.

Copy this module artifact into NotebookLM, PowerPoint Copilot, a document, or a team workspace to support the exercise and follow-up work.

# Solution Intake Form

## Requestor


## Audience

- Provider
- Nurse
- MA
- Admin Staff
- Leadership
- IT Or Engineering
- Other:

## Department Or Team


## Problem

What is the current problem or friction?


## Current Workflow

How is this handled today?


## Desired Outcome

What would be better if this problem were solved?


## Proposed Solution

If the requestor already has an idea, capture it here.


## Frequency

How often does this happen?


## Impact

Who is affected and how much time, effort, risk, or frustration does this create?


## Data Involved

What systems, documents, reports, messages, or patient-related information might be involved?


## Possible Tools

- AI prompt
- Copilot Studio agent
- Power Automate
- Power App
- Dataverse
- Azure
- Epic workflow
- Reporting
- Training
- Process change
- Unknown

## Risks Or Constraints


## Next Step


## Status

`Idea`

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 3 Workflow Discovery

## Role
You are a senior AI enablement facilitator creating a practical workplace training deck on workflow discovery.

## Task
Turn Module 3 into a concise slide deck outline that teaches teams to understand real work and knowledge readiness before choosing AI, automation, app, reporting, or process-change solutions.

## Target Audience
employees, analysts, automation builders, operational leaders, and technical partners who need to turn workflow pain points into structured use cases.

## Goal
Help participants map work, identify friction, assess whether source material is findable/current/owned, capture solution intake details, and separate the problem from the proposed tool.

---

## The Creative Directive
Style Guide: Practical, process-oriented, plain-language, and facilitation-ready. Use workflow visuals, simple examples, and clear before-and-after framing.

Narrative Arc: Start with "start with the work"; show why weak discovery causes failed solutions; introduce discovery questions; map workflow components and knowledge sources; identify opportunity signals; capture intake; close with tool selection only after understanding the work.

---

## Slide Outline Requirements

## Slide 1: Workflow Discovery
**Focus:** Introduce workflow discovery as the foundation for useful AI and automation.
**Prompt:** Title slide showing work moving through people, systems, decisions, and outputs.

## Slide 2: Start With The Work
**Focus:** AI conversations should begin with the workflow.
**Prompt:** Show a fork: "tool first" vs. "work first," with work first leading to clearer outcomes.

## Slide 3: Why Discovery Matters
**Focus:** Many failed AI or automation ideas fail because the work was not understood.
**Prompt:** Visualize a weak foundation under a technology build.

## Slide 4: From Complaint To Use Case
**Focus:** A vague idea like "Can AI fix this?" must become a clear description of the current process.
**Prompt:** Show messy complaint notes transforming into a structured use case card.

## Slide 5: Core Discovery Questions
**Focus:** Ask what happens, who does it, what triggers it, what information is needed, and what decisions are made.
**Prompt:** Use a question wheel or checklist around a workflow.

## Slide 6: What To Capture
**Focus:** Capture trigger, actor, input, source, decision, output, exception, frequency, friction, risk, owner, and knowledge readiness.
**Prompt:** Show a workflow intake canvas with labeled fields.

## Slide 7: Is The Work AI-Ready?
**Focus:** Check whether policies, job aids, workflow changes, and team notes are current, findable, and owned.
**Prompt:** Show a folder readiness checklist with source of truth, owner, date, and review cadence.

## Slide 8: Opportunity Signals
**Focus:** Look for repeated questions, lookups, copy/paste, reformatting, routing, triage, and spreadsheet strain.
**Prompt:** Highlight friction points across a process map with icons.

## Slide 9: Exceptions Matter
**Focus:** Exceptions often determine whether automation is safe or whether human review is required.
**Prompt:** Show a normal workflow path splitting into exception paths.

## Slide 10: Separate Problem From Solution
**Focus:** A proposed tool is not the same as a defined problem.
**Prompt:** Show "we need a chatbot" being reframed into problem, users, sources, and outcome.

## Slide 11: Solution Intake Form
**Focus:** Use intake to capture problem, current workflow, desired outcome, data, risks, and next step.
**Prompt:** Display a clean intake form snapshot with the most important fields emphasized.

## Slide 12: Practice Activity
**Focus:** Participants convert a messy workflow complaint into a problem statement and intake draft.
**Prompt:** Worksheet-style slide with complaint, discovery questions, and output boxes.

## Slide 13: What Comes After Discovery
**Focus:** Next step may be training, process redesign, knowledge cleanup, AI prompt, agent, automation, app, report, or more discovery.
**Prompt:** Show discovery feeding into multiple possible solution paths.

## Slide 14: Common Misunderstandings
**Focus:** Repetition alone does not mean automation; unclear ownership is a process issue; discovery is useful even without AI.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 15: Key Takeaways
**Focus:** Understand the workflow, knowledge sources, friction, risks, and owners, then choose the right next step.
**Prompt:** End with a simple workflow discovery checklist and "tool comes later" message.

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 3 — Workflow Discovery

## Module Info

| Field | Detail |
|---|---|
| Audience | Managers, department leads, analysts, process owners, and AI enablement partners |
| Duration | 90–120 minutes |
| Format | Facilitated workshop — best run with a real team around a real process |
| Prerequisites | Modules 1–2 (literacy and prompting) |
| Builds on | M1 safe use boundaries; M2 structured thinking |
| Feeds into | Module 4: Tool-Fit And Solution Judgment |

---

## Facilitator Overview

This module works best with real work. If possible, run it with a real team working on a real process, even a low-stakes one. The discovery questions land differently when people are describing actual work rather than a training scenario.

Your job is to ask questions and listen, not to suggest solutions. Resist the pull toward solutioning. If participants start talking about what tool to use, redirect: *"Let us understand the work first."*

The primary deliverable is a completed Solution Intake Form. Participants should leave with a structured problem description, not a tool recommendation.

**Prep checklist:**
- [ ] Choose a real or realistic organizational process to use as a worked example (see suggestions below)
- [ ] Print or share the Solution Intake Form
- [ ] Prepare the discovery question list as a reference card for participants
- [ ] Know how to facilitate a process mapping conversation (trigger → steps → exception → owner)
- [ ] Prepare one example of a process where the right answer was NOT AI (see below)
- [ ] Prepare one example where source material cleanup is the right next step before AI
- [ ] Be ready to park tool suggestions — have a visible "parking lot" for ideas that come up too early

---

## Section-by-Section Facilitator Notes

### Opening (10 min)
Start with a real complaint or informal idea, not a polished scenario. Something like: *"We keep getting the same questions from new hires about how to submit a referral."* or *"Scheduling changes take forever because everyone is in a different system."*

Ask: *"Before we talk about what to do about this, let us describe exactly what is happening."* This sets the discovery-first norm for the session.

**Frame the session:** Most AI and automation projects fail not because the model is weak, but because the team did not understand the work well enough before building. Today we slow down on purpose.

### Discovery Questions Walk-Through (15 min)
Work through the discovery question list using your chosen example process. Do not read the list mechanically — use it as a guide for a real conversation. The questions should feel like a structured interview, not a checklist.

**Facilitation tip:** Write answers on a whiteboard or shared screen as the group provides them. Visible structure helps participants see the workflow taking shape. It also makes it obvious when something is missing.

**Watch for these gaps:**
- Trigger is unclear (participants describe a step, not what starts the process)
- Multiple actors are doing the same step differently (a process problem, not an AI problem)
- Exception cases are not known or not documented
- No one can name the process owner
- Source documents are scattered, stale, duplicated, or ownerless

**Anticipated question:** *"Does this mean we have to map every single process before we can use AI?"*
**Response:** No — for simple, low-risk uses like drafting an email or summarizing a document, this level of discovery is not needed. Discovery is for anything you want to automate, build into a tool, or deploy at scale. The investment in understanding pays off when the stakes are higher.

### Knowledge Readiness Check (10 min)
Add this check before tool selection. Ask:

- Where do the policies, job aids, workflow changes, and team notes live?
- Which item is the source of truth?
- Who owns it?
- When was it last reviewed?
- Are there duplicate or conflicting versions?

Frame this plainly: messy knowledge is part of the workflow. If a human cannot find the right document, an AI agent may retrieve the wrong one or produce a weak answer. Sometimes the next step after discovery is not a build; it is source cleanup and ownership assignment.

### the organization Process Examples to Use

**Good processes for this exercise:**
- New employee onboarding: many steps, multiple owners, high repetition, known pain points around system access and Epic training
- Referral intake and routing: clear trigger, multiple decision points, exception handling is important, owned by scheduling
- Policy question intake: high question volume, repeated lookups, opportunity for a knowledge agent
- Department status reporting: manual aggregation, regular cadence, consistent format — classic automation candidate
- Incident or complaint intake: routing decision, sensitive data, escalation paths matter
- Credentialing or provider onboarding: long process, many handoffs, compliance requirements

**An example where the answer was NOT AI:**
> A department asked whether AI could automate how they handle scheduling exceptions. After discovery, it became clear that no one had documented what a "scheduling exception" actually was, and three people in the department defined it differently. The right answer was process documentation and ownership clarity — not AI. Once the process was defined, a simple Power Automate notification flow handled 80% of the volume.

This example is useful because it shows that discovery has value even when the answer is not AI.

### Friction and Opportunity Identification (15 min)
After mapping the process, ask the group to highlight: what is repeated, what is manual, what is slow, what is risky, and what is unclear?

Use colored markers or a simple tagging system (R = repeated, M = manual, F = frustrating, risk = risky, U = unclear ownership) to annotate the process map.

**Anticipated question:** *"Everything feels like a friction point. How do we prioritize?"*
**Response:** Good signal that the process has real problems. Prioritize by: frequency × impact. Something that happens once a month with medium friction is lower priority than something that happens daily with high friction or risk.

### Separating Problem from Solution (10 min)
This is the most important facilitation skill in the module. Every time a participant says *"We should build X"* or *"Could we use Power Automate for this?"*, redirect:

*"That is a good idea to capture. Let us finish describing the problem first, and then we will look at options."*

Use a visible parking lot (sticky note section or slide) for tool and solution ideas. Acknowledge them so participants feel heard, then set them aside until the discovery is complete.

### Solution Intake Form Completion (20–30 min)
Work through the Solution Intake Form as a group for the first example. For subsequent examples, have participants work in small groups.

**Common intake form gaps to watch for:**
- "Expected value" is vague ("it will be faster") — push for specificity ("reduce referral routing time from 2 days to 4 hours")
- "Owner" is a team name, not a person — someone specific needs to be accountable
- "Data source" is listed as "the system" without naming which system or whether access is approved
- "Exception handling" is left blank — this is often where the real risk lives

### Close (10 min)
Ask each group to share their preliminary recommendation for next-step assessment: training, process redesign, AI, automation, app, reporting, or further discovery.

Reinforce the key message: *"Only after the work and its knowledge sources are understood should the team decide whether the answer is a prompt, agent, flow, app, dashboard, training artifact, workflow change, cleanup effort, or no AI at all."*

---

## Organization-Specific Discovery Scenarios Bank

| Process | Key Friction Points | Likely Opportunity Type |
|---|---|---|
| New employee onboarding | System access timing, repeated questions, inconsistent checklists | Knowledge agent, automation |
| Referral intake | Manual routing decisions, status update requests, exception handling | Automation, routing rules |
| Policy question intake | Same questions to HR/compliance, no self-service path | Knowledge agent |
| Scheduling exception handling | No consistent definition, ad hoc decisions | Process redesign first, then automation |
| Department status reporting | Manual spreadsheet aggregation, email-based reporting | Dashboard, automation |
| Credentialing / provider onboarding | Long handoff chain, compliance checkpoints, many documents | Structured tracking app, checklist automation |
| Incident/complaint intake | Routing depends on category, sensitive data, audit trail needed | Structured intake form, routing automation |
| Training completion tracking | Manual follow-up, spreadsheet-based, no visibility for managers | Dashboard, automated reminders |

---

## Where Sessions Tend to Stall

- **Participants jump to solutions immediately.** Use the parking lot. Acknowledge the idea, capture it visibly, and redirect: *"Let us understand what is actually happening first."*
- **No one can name the process owner.** Treat this as a discovery finding. Flag unclear ownership as a required first step before any AI or automation work.
- **The process is more complex than expected.** Good. That complexity is exactly why discovery matters. Help the group scope down to one part of the process that is bounded and well-understood.
- **Participants disagree on how the process works.** Also a useful finding. If the same process works differently across people or departments, that is a process standardization problem, not an AI problem.

---

## Knowledge Check Questions

1. What is the difference between a complaint and a problem statement?
2. Name four elements that should be captured in a workflow map.
3. Why do exception cases matter when deciding whether to automate a process?
4. What does it mean if no one can name the owner of a process?
5. What are the possible next-step recommendations after a discovery session?
6. What does knowledge readiness mean in a workflow discovery session?

---

## Tips for Different Audience Types

**Department managers:** They have the most context about the work but may be eager to jump to solutions. Channel their energy toward the owner and exception questions — they usually know where things break.

**Frontline staff:** They know the friction better than anyone. Ask them to describe their worst day with the process, not their average day. Edge cases and breakdowns reveal the real complexity.

**IT staff:** They may want to talk about systems and integrations early. Redirect to the process first. Their technical knowledge is most useful once the workflow is defined.

**Leaders:** Help them understand that their role in this session is to ask questions and listen, not to decide. Discovery produces better decisions than assumption.

Copy the actual module source notes to use as context in NotebookLM, PowerPoint Copilot, or another content-generation tool.

# Module 4: Tool-Fit And Solution Judgment

## Purpose

Teach teams to choose the right solution path instead of defaulting to the newest or most exciting tool.

## Learning Outcomes

- Choose between prompt, agent, automation, app, dashboard, process change, or custom development.
- Explain tradeoffs in plain language.
- Avoid overbuilding.

## Core Artifact

- AI Use Case Assessment.

## Teaching Frame

Some AI ideas deserve a build, and some need a simpler fix first. The useful question is whether the team is solving the right problem well.

This module teaches solution judgment. Participants should learn to compare options based on the nature of the work, the data involved, the expected value, the risk level, the required ownership, and the maintenance burden.

Tool fit now includes knowledge readiness. Sometimes the right next step is source cleanup, ownership, archiving outdated documents, or documenting the workflow before anyone builds a prompt, agent, flow, app, or dashboard.

## Why This Module Matters

AI enablement can create confusion if every problem is treated as an AI problem. Some problems need a clearer prompt. Some need a better SharePoint list, a Power Automate flow, a Power App, a dashboard, an Epic workflow change, or a custom software path. Some need process ownership before technology is useful.

Good tool-fit decisions help teams avoid novelty projects, reduce rework, and route ideas to the right implementation path.

## Tool-Fit Decision Model

| If the problem is... | Consider... |
|---|---|
| Drafting, summarizing, brainstorming, reformatting | AI prompt or Copilot |
| Answering questions from approved documents | Copilot Studio knowledge agent |
| Repetitive handoff or notification | Power Automate |
| Structured data entry and tracking | Power App |
| Shared operational data with stronger governance | Dataverse |
| Metrics, trends, and visibility | Dashboard or report |
| Broken process with unclear ownership | Process redesign before AI |
| Scattered, stale, or conflicting knowledge | Knowledge cleanup before AI |
| Complex integration or custom behavior | Software engineering path |

## Assessment Criteria

- Audience.
- Problem.
- Current process.
- AI or automation opportunity.
- Expected value.
- Data classification.
- Risk level.
- Human review needs.
- Tool fit.
- Knowledge readiness.
- Feasibility.
- Dependencies.
- Recommendation.
- Next step.

## Solution Options To Compare

- Prompt or Copilot: Best for drafting, summarizing, brainstorming, and reformatting low-risk content.
- Copilot Studio knowledge agent: Best for answering questions from approved internal sources.
- Power Automate: Best for repeatable handoffs, notifications, approvals, and system-triggered actions.
- Power App: Best for structured data entry, guided workflows, and lightweight operational tools.
- Dataverse: Best when shared operational data needs stronger structure, permissions, and governance.
- Dashboard or report: Best when the need is visibility, trends, metrics, or monitoring.
- Process redesign: Best when ownership, inputs, rules, or handoffs are unclear.
- Software engineering path: Best for complex integration, custom behavior, scale, or advanced reliability needs.

## Practical Sessions

- "Should This Be A Power Automate Flow?"
- "High-Value Use Cases vs. Interesting Demos"
- "The AI Intake And Prioritization Model"
- "When To Build Custom Software Instead Of A Power Platform Solution"

## Suggested Teaching Flow

1. Start with a real or realistic use case.
2. Clarify the problem and current process.
3. Identify data classification and risk level.
4. Compare multiple solution paths.
5. Discuss tradeoffs: speed, governance, maintainability, ownership, and user experience.
6. Choose a recommended next step.
7. Document the reasoning in an AI use case assessment.

## Discussion Prompts

- Is the primary need language help, workflow automation, structured tracking, decision support, or visibility?
- What would make this solution hard to maintain?
- What human review is required?
- What tool would be simplest while still solving the real problem?
- What would need to be true before this should become a build?
- Are the source documents organized and trustworthy enough for AI to use?

## Exercises

- Compare AI prompt, Copilot Studio agent, Power Automate, Power App, dashboard, and process-change options for a use case.
- Rank use cases by impact, effort, and risk.
- Decide whether a use case should be rejected, delayed, piloted, or escalated.
- Explain a tool recommendation in plain language for a non-technical audience.
- Recommend whether a use case needs knowledge cleanup before any AI or automation build.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A completed AI use case assessment.
- A short tool-fit recommendation.
- A risk and feasibility rating.
- A plain-language explanation of tradeoffs.
- A next-step decision: reject, delay, discover more, prototype, pilot, or escalate.

## Common Misunderstandings To Address

- The most advanced tool is not automatically the best fit.
- Automation can make a bad process fail faster.
- A dashboard does not fix unclear ownership.
- A chatbot is not always better than a well-organized document or workflow.
- Tool fit should include maintenance, support, governance, and user adoption.
- A chatbot over disorganized content can make confusion easier to access.

## Key Message

Good AI enablement depends on judgment. The model is only one part of the system; source quality, ownership, workflow clarity, and maintenance matter just as much.

Copy this module artifact into NotebookLM, PowerPoint Copilot, a document, or a team workspace to support the exercise and follow-up work.

# AI Use Case Assessment

## Use Case


## Audience


## Problem


## Current Process


## AI Or Automation Opportunity


## Expected Value

- Time saved:
- Error reduction:
- Experience improvement:
- Quality improvement:
- Other:

## Data Classification

- No sensitive data
- Internal data
- Patient or clinical data
- Financial data
- Employee data
- Unknown

## Risk Level

- Low
- Medium
- High
- Unknown

## Human Review Needed

- Yes
- No
- Unknown

## Tool Fit

- AI prompt
- Copilot Studio agent
- Power Automate
- Power App
- Dataverse
- Azure
- Epic workflow
- Reporting
- Training
- Process change
- Unknown

## Feasibility

- Low
- Medium
- High
- Unknown

## Dependencies

- 

## Recommendation


## Next Step

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 4 Tool-Fit And Solution Judgment

## Role
You are a senior AI enablement facilitator creating a practical workplace training deck on choosing the right solution path.

## Task
Turn Module 4 into a concise slide deck outline that teaches teams to compare prompts, agents, automation, apps, dashboards, knowledge cleanup, process redesign, and custom development.

## Target Audience
employees, analysts, automation builders, operational leaders, managers, and technical partners who need to assess AI and automation ideas.

## Goal
Help participants avoid overbuilding, explain tradeoffs clearly, assess value, risk, and knowledge readiness, and document a practical tool-fit recommendation.

---

## The Creative Directive
Style Guide: Practical, decision-oriented, plain-language, and healthcare-aware. Use comparison visuals, simple routing logic, and real workflow framing.

Narrative Arc: Start with the need to choose the right kind of work; show why tool-first thinking causes confusion; introduce the tool-fit model; compare solution paths including knowledge cleanup; apply assessment criteria; practice recommendation writing; close with judgment as the core skill.

---

## Slide Outline Requirements

## Slide 1: Tool-Fit And Solution Judgment
**Focus:** Introduce the module as choosing the right solution path for the real problem.
**Prompt:** Title slide showing multiple paths from one workflow problem.

## Slide 2: Choose The Right Kind Of Work
**Focus:** Teams should solve the right problems well and avoid forcing AI into work that needs a simpler fix.
**Prompt:** Show "interesting demo" contrasted with "useful, owned, tested solution."

## Slide 3: Why Tool Fit Matters
**Focus:** Treating every problem as an AI problem causes rework, risk, and poor adoption.
**Prompt:** Visualize mismatched tools creating friction in a workflow.

## Slide 4: Start With The Use Case
**Focus:** Clarify audience, problem, current process, expected value, data, and risk before selecting a tool.
**Prompt:** Show a use case assessment card being filled in before solution choice.

## Slide 5: The Tool-Fit Decision Model
**Focus:** Match problem types to likely solution paths.
**Prompt:** Use a clean matrix mapping problem patterns to prompt, agent, flow, app, Dataverse, dashboard, knowledge cleanup, process redesign, or engineering.

## Slide 6: Prompt Or Copilot
**Focus:** Best for drafting, summarizing, brainstorming, and reformatting low-risk content.
**Prompt:** Show document and note tasks moving through a draft assistant.

## Slide 7: Knowledge Agent
**Focus:** Best for answering questions from approved internal documents.
**Prompt:** Show approved sources feeding a bounded Q&A agent with citations.

## Slide 8: Automation Or App
**Focus:** Power Automate fits repeatable handoffs; Power Apps fits structured data entry and guided workflows.
**Prompt:** Split slide comparing trigger-action flow vs. structured form app.

## Slide 9: Dataverse, Dashboard, Or Engineering
**Focus:** Use Dataverse for governed shared data, dashboards for visibility, and engineering for complex integration or custom behavior.
**Prompt:** Show three lanes: governed data, metrics visibility, custom build.

## Slide 10: Process Redesign Before AI
**Focus:** Broken processes with unclear ownership need clarity before technology.
**Prompt:** Show a tangled process being simplified before tools are added.

## Slide 11: Knowledge Cleanup Before AI
**Focus:** Scattered, stale, or conflicting source material may need cleanup before an agent or automation.
**Prompt:** Show disorganized documents becoming current, owned, and approved sources.

## Slide 12: Tradeoffs To Discuss
**Focus:** Compare speed, governance, maintainability, ownership, user experience, risk, and feasibility.
**Prompt:** Use a balanced scorecard visual for solution comparison.

## Slide 13: Practice Activity
**Focus:** Participants compare multiple solution paths for one realistic use case.
**Prompt:** Worksheet slide with columns for value, risk, feasibility, owner, and recommendation.

## Slide 14: Recommendation Language
**Focus:** A good recommendation explains the chosen path and why other paths are not the best first step.
**Prompt:** Show a plain-language recommendation template with rationale and next step.

## Slide 15: Common Misunderstandings
**Focus:** Advanced is not always better; automation can accelerate bad process; dashboards do not fix ownership.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 16: Key Takeaways
**Focus:** Good AI enablement depends on judgment, tool fit, source quality, ownership, risk review, and maintainability.
**Prompt:** End with a simple decision checklist and "the model is only one part of the system."

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 4 — Tool-Fit And Solution Judgment

## Module Info

| Field | Detail |
|---|---|
| Audience | Analysts, IT leads, department managers, project leads, and AI enablement team members |
| Duration | 90–120 minutes |
| Format | Case study workshop — participants evaluate real or realistic use cases and produce recommendations |
| Prerequisites | Modules 1–3 (literacy, prompting, and workflow discovery) |
| Builds on | M3 discovery outputs (problem statements, intake forms) |
| Feeds into | Module 5: Agent Design; Module 6: Responsible AI And Risk Review |
| Tool note | Based on the organization's Microsoft 365 stack as of 2025. Reassess tool options if the stack changes. |

---

## Facilitator Overview

This module teaches judgment. The Tool-Fit Decision Model gives participants a way to reason through a use case, compare options, name tradeoffs, and recommend a next step. The table is a guide, not a substitute for thinking.

Expect pushback in two directions: participants who want AI for everything, and participants who are skeptical that any tool will actually work. Both are worth engaging. The goal is disciplined, honest evaluation.

**Prep checklist:**
- [ ] Select two or three use cases from the organizational scenarios bank below (or use real intake forms from M3 sessions)
- [ ] Print or share the AI Use Case Assessment template
- [ ] Prepare a brief explanation of each tool option in the organization's current stack
- [ ] Know which tool approvals are current — do not recommend tools that are not yet licensed or approved
- [ ] Be ready to make the case for "process redesign before AI" — this is the most commonly skipped option and the most often correct one
- [ ] Be ready to make the case for "knowledge cleanup before AI" when documents are stale, conflicting, or ownerless
- [ ] Prepare one "reject" example: a use case that should not be built right now and why

---

## Section-by-Section Facilitator Notes

### Opening (10 min)
Start with a real or realistic organizational scenario that is ambiguous — something where the right answer is not obvious. Ask the group: *"What would you build for this?"* Let them share freely. You will likely get a range of answers. Use the disagreement to motivate the need for a structured evaluation approach.

**Good opening scenarios:**
- *"The HR team gets the same five questions from new employees every week. What should we build?"* (Could be a knowledge agent, a better SharePoint FAQ, an onboarding checklist, or nothing — depends on the details.)
- *"Department heads want a weekly summary of open incidents. What should we build?"* (Could be a Power BI dashboard, an automated email, a Copilot prompt, or a manual report — depends on the data and audience.)

### The Tool-Fit Decision Model (15 min)
Walk through the decision model table. For each row, give one organization-specific example. Do not read the table — use it as a reference while narrating the logic.

**Organization-specific tool examples:**

| Tool | Example |
|---|---|
| AI prompt / Copilot | Draft a policy FAQ answer, summarize a non-clinical meeting, rewrite a confusing email |
| Copilot Studio knowledge agent | Answer repeated onboarding questions from a curated SharePoint knowledge base |
| Power Automate | Notify a manager when a form is submitted, route a referral to the correct team, send reminders for outstanding tasks |
| Power App | Structured intake form for IT requests, guided workflow for scheduling exception handling, simple tracker for credentialing steps |
| Dataverse | Shared operational data that multiple departments need to access with consistent permissions and audit trails |
| Dashboard / Power BI | Department KPIs, staffing metrics, incident trend reporting |
| Knowledge cleanup | Curate a SharePoint source of truth before building a knowledge agent |
| Process redesign | Any workflow where ownership, steps, or exception rules are not agreed upon |
| Custom software / engineering path | Integrations with Epic that require HL7/FHIR, solutions needing high reliability guarantees, security-sensitive clinical data pipelines |

**Anticipated question:** *"What is the difference between a Power App and a Copilot Studio agent?"*
**Response:** A Power App collects and displays structured data. A Copilot Studio agent answers questions in natural language from approved knowledge sources. If the user needs to fill in a form or track a record, use a Power App. If the user needs to ask a question and get an answer, use an agent. They can also be combined.

**Anticipated question:** *"Why would we ever just redesign a process instead of using AI?"*
**Response:** Because AI applied to a broken process produces broken output faster. If no one agrees on the steps, who is responsible, or what counts as an exception, automating or AI-assisting the process will not fix it — it will just make the confusion harder to see. Process clarity is a prerequisite, not a nice-to-have.

**Anticipated question:** *"Can we build the agent now and clean up the documents later?"*
**Response:** Usually no. A knowledge agent depends on the quality of its sources. If the source material is stale, duplicated, or unofficial, the agent may make bad information easier to access. Source cleanup is part of the solution path.

### Assessment Criteria Walk-Through (10 min)
Walk through the AI Use Case Assessment criteria. Emphasize the ones participants tend to skip:

- **Human review needs:** Who specifically reviews the output, at what point, and before what action? Vague answers here are a risk signal.
- **Data classification:** What type of data is involved? If participants are not sure, that is a blocker.
- **Knowledge readiness:** Are the source documents current, official, findable, and owned? If not, cleanup may be the next step.
- **Feasibility:** Does the organization currently have access to the data, the tool, and the people needed to build and maintain this?
- **Next step:** This should be specific — not "build it" but "prototype with two users in scheduling and review in four weeks."

### Use Case Evaluation Exercise (30–40 min)
Give each group a use case (real intake form from M3 or a scenario from the bank below). Have them:
1. Complete an AI Use Case Assessment
2. Select a recommended tool path with rationale
3. Assign a risk level
4. Identify the next step: reject, delay, discover more, prototype, pilot, or escalate

Debrief as a group. Ask each team to share their recommendation and defend it. Encourage productive disagreement.

**The "reject" example is important.** Make sure at least one group has a use case that should be rejected or delayed. Participants need to practice saying no with a rationale, not just yes with a plan.

### Plain-Language Recommendation Practice (10 min)
Participants often make good technical decisions but struggle to explain them to non-technical stakeholders. Give them five minutes to write a plain-language summary of their recommendation. Then pair them up to practice explaining it to someone who was not in the room.

*"The recommendation is to pilot a Copilot Studio knowledge agent that answers onboarding questions from our approved SharePoint HR content. It is a lower-risk starting point than a custom build, and it can be evaluated against real user questions within six weeks."*

### Close (10 min)
Key message: *"Good AI enablement depends on judgment. The model is only one part of the system; source quality, ownership, workflow clarity, and maintenance matter just as much."*

Reinforce: the best tool recommendation is the one that solves the real problem with the least complexity, the clearest ownership, and the appropriate level of human review.

---

## Organization-Specific Use Case Scenarios Bank

| Scenario | Likely Tool Fit | Why |
|---|---|---|
| HR gets 5 repeat questions from new hires weekly | Copilot Studio knowledge agent | Defined question set, approved HR content, no PHI |
| Department heads want weekly open-incident summary | Power BI dashboard or automated email | Data aggregation and visibility — not a language task |
| Scheduling team manually routes referrals by reading fax | Power Automate + structured intake | Repetitive routing with defined rules |
| IT request intake is currently email-based with no tracking | Power App | Structured intake and tracking, not a language task |
| Managers copy-paste status updates into a weekly report | Copilot prompt (M365) | Drafting task, low risk, no PHI |
| Credentialing team tracks steps in a spreadsheet | Power App + Dataverse | Multi-step tracking, shared data, compliance audit needs |
| Clinical team wants AI to review patient notes | Escalate — requires security/clinical/legal review | PHI involved, high output impact, no current approved path |
| A department wants to "use AI" but cannot describe the problem | More discovery (M3) | Not a tool question yet — discovery is the next step |
| Finance wants to automate budget variance reporting | Dashboard + possible automation | Data visibility and structured reporting task |
| New manager wants to automate employee performance reviews | Process redesign first | Ownership and policy clarity required before any automation |

---

## Where Sessions Tend to Stall

- **Every use case gets recommended as AI.** Challenge the group: *"What would have to be true for this to be a Power Automate flow instead? What about just a better SharePoint page?"* Push them to genuinely compare options.
- **Participants are unfamiliar with Power Platform tools.** Prepare a one-page reference on what each tool does and when the organization uses it. Do not spend more than five minutes on tool explanations — keep the focus on judgment.
- **Risk level disagreement.** Good — use it. Ask what specific fact would change their rating. This surfaces assumptions that need to be made explicit.
- **"We already started building this."** This comes up more than expected. Treat it as a use case assessment exercise. Be honest if the build is heading in the wrong direction — better to redirect now than after more investment.

---

## Knowledge Check Questions

1. Name three tool options you would consider for a use case involving repeated question-answering from internal documents.
2. Why might process redesign be the right recommendation before any AI or automation work?
3. What factors would move a use case from "pilot" to "escalate" in the next-step decision?
4. What is the difference between a use case that is rejected and one that is delayed?
5. How do you explain a tool recommendation to a non-technical department leader?
6. When should knowledge cleanup be recommended before AI or automation?

---

## Tips for Different Audience Types

**IT staff:** They often have the strongest tool knowledge but may underweight process and ownership considerations. Push them on: who maintains this after it is built, who owns the data, and what happens when it breaks?

**Department managers:** They often have the clearest sense of the problem but the least tool familiarity. Focus their attention on the assessment criteria they can answer: what the expected value is, who owns the process, and what human review looks like.

**AI enablement team members:** Use this session to calibrate their assessment instincts. The goal is consistent, defensible recommendations — not enthusiasm for the newest tool.

**Skeptical participants:** Lean into the rejection and delay scenarios. They often make the best reviewers. Help them see that careful skepticism improves the recommendation.

Copy the actual module source notes to use as context in NotebookLM, PowerPoint Copilot, or another content-generation tool.

# Module 5: Agent Design

## Purpose

Teach teams to define agents as products with a job, users, sources, guardrails, evaluation criteria, and ownership.

## Learning Outcomes

- Define users, tasks, knowledge sources, and guardrails.
- Design escalation paths.
- Create evaluation examples.
- Understand ownership and maintenance.

## Core Artifact

- Agent Spec template.

## Teaching Frame

An agent needs the same discipline as a small internal product: a defined job, users, sources, limits, testing, ownership, and a lifecycle.

This module helps teams move from "we should make a chatbot" to a real agent concept. A useful agent has a bounded purpose, known users, approved sources, expected tasks, clear refusal behavior, escalation paths, and a review process.

Agent design should make the hidden knowledge system visible. If policies, job aids, workflow changes, and team information are scattered across folders with no owner or review cycle, the agent problem starts as a knowledge-management problem.

## Why This Module Matters

Agents can create real value when they answer repeated questions, guide people through known processes, or help retrieve information from approved sources. They can also create risk when their scope is unclear, their sources are weak, or no one owns the content.

Agent design turns an exciting demo into something that can be evaluated, supported, and improved.

## Agent Design Questions

- Who is the user?
- What job is the agent responsible for?
- What questions should it answer?
- What should it refuse or escalate?
- What knowledge sources are approved?
- Who owns the content?
- How will answers be evaluated?
- What happens when the agent is wrong?
- How will feedback be captured?
- How often will the content be reviewed?
- What folder or library structure will the agent rely on?
- What content must be cleaned up, archived, or rewritten before launch?

## Agent Spec Elements

- Purpose.
- Users.
- Tasks.
- Inputs.
- Outputs.
- Knowledge sources.
- Tools or integrations.
- Guardrails.
- Escalation paths.
- Evaluation criteria.
- Content owner.
- Maintenance cycle.
- Source organization and review cycle.

## Agent Boundaries

Every agent should define:

- In scope: Questions, tasks, audiences, and sources the agent should handle.
- Out of scope: Topics, decisions, or actions the agent should not handle.
- Escalation: Where users should go when the agent cannot answer safely.
- Review: Who checks quality, feedback, and content updates.
- Failure mode: What should happen when the agent is uncertain or wrong.

## Practical Sessions

- "From Prompt To Tested Agent"
- "Agent Design Canvas"
- "Building Evaluation Sets For AI Features"
- "Human Review And Approval Patterns"

## Suggested Teaching Flow

1. Start with a repeated question or knowledge-access problem.
2. Define the agent's job in one sentence.
3. Identify the user groups and the decisions they need to make.
4. List approved knowledge sources.
5. Define what the agent should refuse or escalate.
6. Draft expected answer examples and failure cases.
7. Assign content ownership and maintenance cadence.
8. Capture the design in an agent spec.

## Discussion Prompts

- What repeated questions would this agent reduce?
- What source material would the agent need to answer well?
- What should the agent never answer?
- Who owns the source content?
- How would users report a wrong or unhelpful answer?
- How would we know the agent is ready for pilot?
- What source material would make this agent unsafe or confusing if it were stale?

## Exercises

- Draft an agent spec for a bounded internal knowledge use case.
- Identify approved and unapproved knowledge sources.
- Write refusal and escalation examples.
- Create expected answer examples and edge cases.
- Define content ownership and review cadence.
- Create a source-readiness checklist for the proposed agent.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A one-sentence agent purpose statement.
- An agent spec draft.
- A list of approved knowledge sources.
- A list of out-of-scope topics.
- Sample expected answers.
- Sample refusal or escalation responses.
- A proposed content owner and review cycle.

## Common Misunderstandings To Address

- An agent is not useful just because it can chat.
- Agent quality depends heavily on source quality.
- Guardrails should be designed before pilot.
- Content ownership is part of the product.
- An agent that answers everything is usually too broad to trust.
- Agent quality is limited by the quality, structure, and ownership of its source material.

## Key Message

The difference between a demo and something the organization can safely use is structure: clear purpose, approved and organized sources, guardrails, testing, ownership, and maintenance.

Copy this module artifact into NotebookLM, PowerPoint Copilot, a document, or a team workspace to support the exercise and follow-up work.

# Agent Spec

## Agent Name


## Purpose


## Users


## Tasks

- 

## Inputs

- 

## Outputs

- 

## Tools Or Integrations

- 

## Knowledge Sources

- 

## Guardrails

- 

## Evaluation Criteria

- 

## Open Questions

-

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 5 Agent Design

## Role
You are a senior AI enablement facilitator creating a practical workplace training deck on agent design.

## Task
Turn Module 5 into a concise slide deck outline that teaches teams to define agents as products with a clear job, users, organized sources, guardrails, evaluation, and ownership.

## Target Audience
employees, analysts, automation builders, operational leaders, managers, and technical partners who may propose or help design internal AI agents.

## Goal
Help participants move from "we should make a chatbot" to a bounded, source-ready, testable, supportable agent concept captured in an agent spec.

---

## The Creative Directive
Style Guide: Practical, product-minded, plain-language, healthcare-aware, and design-canvas oriented. Use diagrams, boundaries, source-quality visuals, and clear examples.

Narrative Arc: Start with the idea that agents need product discipline; show what makes vague chatbot ideas risky; define agent purpose, users, source readiness, guardrails, and ownership; introduce evaluation and escalation; close with an agent spec as the core artifact.

---

## Slide Outline Requirements

## Slide 1: Agent Design
**Focus:** Introduce agents as bounded products with jobs, users, sources, guardrails, and owners.
**Prompt:** Title slide showing an agent inside a product frame with inputs, sources, users, and review loop.

## Slide 2: Agents Are Products
**Focus:** An agent needs more than instructions; it needs a defined job and lifecycle.
**Prompt:** Compare a loose chatbot bubble with a structured product canvas.

## Slide 3: Why Agent Design Matters
**Focus:** Agents can reduce repeated questions, but unclear scope or weak sources create risk.
**Prompt:** Show value and risk side by side: repeated Q&A solved vs. unsupported answer risk.

## Slide 4: Start With The Job
**Focus:** Define the agent's job in one sentence before discussing tools.
**Prompt:** Show a one-sentence purpose statement being refined from vague to specific.

## Slide 5: Users And Tasks
**Focus:** Identify who will use the agent and what tasks or decisions they need help with.
**Prompt:** Map user groups to task cards and expected outputs.

## Slide 6: Knowledge Sources
**Focus:** Agent quality depends heavily on approved, current, and useful source material.
**Prompt:** Show approved documents feeding the agent, with outdated or unapproved sources blocked.

## Slide 7: Source Readiness
**Focus:** Policies, job aids, workflow changes, and team information need structure, owners, dates, and review cycles.
**Prompt:** Show a folder/library readiness checklist before agent launch.

## Slide 8: In Scope And Out Of Scope
**Focus:** Define what the agent should handle and what it should not answer or do.
**Prompt:** Use a boundary diagram with in-scope requests inside and out-of-scope requests outside.

## Slide 9: Guardrails And Escalation
**Focus:** Agents need refusal behavior, escalation paths, and clear handling for uncertainty.
**Prompt:** Show a decision path: answer, ask clarification, refuse, or escalate.

## Slide 10: Evaluation Criteria
**Focus:** Define expected answers, edge cases, refusal cases, and failure cases before pilot.
**Prompt:** Display a small evaluation table with test input, expected behavior, and result.

## Slide 11: Ownership And Maintenance
**Focus:** Content ownership, feedback review, and update cadence are part of the agent design.
**Prompt:** Show a maintenance loop: source update, test, publish, feedback, review.

## Slide 12: The Agent Spec
**Focus:** Capture purpose, users, tasks, inputs, outputs, tools, sources, guardrails, and evaluation.
**Prompt:** Show an agent spec template with the major sections highlighted.

## Slide 13: Practice Activity
**Focus:** Participants draft a bounded agent concept from a repeated question or knowledge-access problem.
**Prompt:** Worksheet slide with fields for purpose, users, sources, out-of-scope, escalation, and owner.

## Slide 14: Common Misunderstandings
**Focus:** Chat ability is not value; broad scope reduces trust; source quality and ownership matter.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 15: Key Takeaways
**Focus:** A useful agent has clear purpose, approved and organized sources, boundaries, escalation, evaluation, and ownership.
**Prompt:** End with six connected pillars around a central "agent spec" artifact.

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 5 — Agent Design

## Module Info

| Field | Detail |
|---|---|
| Audience | Builders, IT and technical leads, product owners, senior analysts, AI enablement team |
| Duration | 120 minutes |
| Format | Design workshop — participants draft an agent spec for a real or realistic use case |
| Prerequisites | **Modules 3 and 4 required.** Participants should arrive with a defined problem statement and a tool-fit recommendation pointing toward an agent. |
| Builds on | M3 workflow discovery (problem, sources, exceptions); M4 tool fit (decision to build an agent) |
| Feeds into | Module 6: Responsible AI And Risk Review; Module 7: Testing And Evaluation |

---

## Facilitator Overview

This module has the steepest learning curve in the curriculum. Participants are moving from understanding AI to designing an AI product. The shift is significant: an agent is not a prompt — it is a system with users, sources, guardrails, and a lifecycle.

The most common failure mode is scope creep during the design session. Participants will want the agent to do more than it should. Your job is to help them resist that pull and design something bounded, testable, and maintainable.

**Do not run this module with a general all-staff audience.** It is intended for people who will actually build or own agents. If participants have not completed Modules 3 and 4, they will struggle to make good design decisions.

**Prep checklist:**
- [ ] Confirm prerequisites: participants should have a use case that has already passed a tool-fit assessment recommending an agent
- [ ] Print or share the Agent Spec template
- [ ] Prepare one example of a good agent design (completed spec) and one example of a too-broad agent design
- [ ] Prepare a list of approved knowledge sources currently available in the organization (SharePoint sites, approved document libraries)
- [ ] Prepare a source-readiness checklist covering owner, current version, approval status, duplicates, and review cadence
- [ ] Know the current Copilot Studio capabilities and limitations for the organization's tenant
- [ ] Identify a real content owner for the example use case — the "who owns this" question must have a real answer

---

## Section-by-Section Facilitator Notes

### Opening (10 min)
Start with the contrast between a demo and a product.

*"A demo shows that an agent can answer a question. A product shows that an agent can answer the right questions reliably, refuse the wrong ones safely, and be maintained when the content changes."*

Show a simple example: an agent asked a question it should answer (clean response) and an agent asked a question it should not answer (scope problem, wrong source, or unsafe content). Ask the group: what makes the difference?

The answer is design — purpose, sources, guardrails, and ownership — not model capability.

### The One-Sentence Purpose Test (10 min)
Every agent should be describable in one sentence. If it cannot be, the scope is too broad.

Practice this with the group. Give them a few over-broad agent ideas and ask them to narrow the scope to a single sentence:

- *"An agent that answers any HR question"* → too broad
- *"An agent that answers new employee onboarding questions using our approved approved New Hire Guide"* → good scope
- *"An agent that helps with scheduling"* → too broad
- *"An agent that answers referral routing questions for scheduling coordinators using our current referral protocol document"* → good scope

**Anticipated pushback:** *"But users will want it to do more."*
**Response:** Scope expansion can happen after a successful pilot. Launching with a narrow, high-quality scope is better than launching with broad, unreliable coverage. Users lose trust in agents that answer poorly — and that trust is hard to recover.

### Users and Tasks (15 min)
Walk through the agent design questions for users and tasks. Push participants to be specific.

- Not *"employees"* but *"scheduling coordinators at the front desk who handle incoming referrals"*
- Not *"answer questions"* but *"answer questions about referral routing, required documentation, and turnaround time expectations"*

**Organization-specific agent candidates:**

| Agent | User | Job |
|---|---|---|
| New hire onboarding agent | New employees in their first 30 days | Answer common questions about system access, benefits enrollment, ID badges, and first-week logistics |
| Referral routing agent | Scheduling coordinators | Answer questions about routing rules, required documentation, and payer-specific requirements using the current protocol guide |
| Policy FAQ agent | All staff | Answer questions about HR, IT, and operational policies using approved policy documents on SharePoint |
| Provider onboarding agent | Credentialing team and new providers | Guide through required documentation, steps, and contacts for the credentialing process |
| IT help desk pre-triage agent | All staff | Answer common IT questions and help users self-diagnose before submitting a ticket |

### Knowledge Sources (15 min)
This section is critical. Agent quality is only as good as source quality.

Work through these questions for the example use case:
- What documents would the agent need to answer well?
- Are those documents current and maintained?
- Who owns them?
- Are they approved for use in an AI knowledge base?
- How often do they change?

**Common problems to surface:**
- Source content is outdated (the policy changed, the document was not updated)
- Source content is in email threads, verbal knowledge, or individual spreadsheets — not in a form the agent can use
- Multiple versions of the same document exist with conflicting information
- No one has reviewed the source content for accuracy recently

**Anticipated question:** *"Can we just point the agent at all of SharePoint?"*
**Response:** No. Broad access to unstructured SharePoint content produces inconsistent, unreliable answers. A good knowledge agent has curated, approved, current source content — not access to everything. Start with the two or three documents that answer 80% of the target questions.

**Facilitator emphasis:** Treat source readiness as a launch gate. The agent needs duplicate job aids, conflicting versions, unclear ownership, and missing review cycles resolved before launch. Source readiness is part of agent quality and safety.

### Guardrails and Escalation (15 min)
Every agent needs to know what it should not do. This is as important as what it should do.

Have participants draft:
1. Three things the agent should refuse to answer (and why)
2. Two escalation paths (where users go when the agent cannot safely help)
3. One failure mode (what the agent should say when it is uncertain)

**Organization examples:**

| Situation | Agent Response |
|---|---|
| User asks a question involving PHI | "I am not able to help with information about specific patients. Please contact your department lead or the appropriate clinical team." |
| User asks for legal or HR advice | "This question should go to [HR/Legal]. I can help with general policy information, but not with specific situations." |
| Agent is not confident in the answer | "I am not certain about this. Please verify with [named contact or resource] before acting on this information." |
| User asks about something outside scope | "That is outside what I am set up to help with. You can reach [team or resource] for that." |

### Content Ownership and Maintenance (10 min)
Every agent must have a named content owner — a specific person, not a team name. The content owner is responsible for:
- Keeping source documents current
- Reviewing agent performance feedback
- Approving content changes
- Deciding when the agent should be retired or updated

**Anticipated pushback:** *"The content is owned by a whole department."*
**Response:** A department cannot own content — a person within that department needs to be accountable. If no one is willing to be named, the agent is not ready to launch.

Establish a maintenance cadence. For most internal knowledge agents, quarterly review is appropriate. For agents covering content that changes frequently (policy, benefits, system procedures), monthly review is better.

Ask participants to document where source material will live. "SharePoint" is not specific enough. They should name the site, library, folder, or page and identify what gets archived when content changes.

### Agent Spec Drafting Exercise (20–30 min)
Participants draft a full agent spec for a real or realistic organizational use case. If they arrived from Module 3 and 4 with a defined use case, use that. If not, assign one from the bank above.

Walk the room. Watch for:
- Purpose statements that are still too broad
- Knowledge source lists that include unapproved or unreviewed content
- Guardrail sections that are left blank or vague
- No named content owner

Debrief: one group shares their spec. The group reviews it together using the design questions from the module as a checklist.

### Close (10 min)
Key message: *"The difference between a demo and something the organization can safely use is structure: clear purpose, approved and organized sources, guardrails, testing, ownership, and maintenance."*

Ask each participant to identify the single most important thing they need to resolve before their agent is ready for testing.

---

## Organization-Specific Agent Design Details

### What "Approved Knowledge Source" Means in the organization
- Content published in designated SharePoint sites reviewed by the owning department
- Policy documents that have gone through the appropriate approval process
- Procedure guides maintained by a named SME or department lead
- Content that does not contain PHI, sensitive employee data, or unresolved draft language

### What Is NOT an Approved Source
- Email threads
- Individual OneDrive files not shared with the team
- Draft or unapproved policy documents
- Verbal knowledge held by specific staff members
- Content from external websites not vetted by the organization

### Escalation Path Examples for the organization Agents
- HR questions → HR Business Partner or HR shared inbox
- IT questions → IT Help Desk ticket
- Policy interpretation → Compliance team
- Clinical questions → Clinical supervisor or appropriate care team
- PHI-related questions → Direct to appropriate clinical or privacy officer

---

## Where Sessions Tend to Stall

- **Participants design an agent that does too much.** Hold them to the one-sentence purpose test. If they cannot write it in one sentence, the scope needs to shrink.
- **No approved knowledge sources exist.** This is a real blocker. The agent cannot be built until source content is curated and approved. Treat this as a prerequisite, not a detail.
- **No one will own the content.** Escalate this finding. An agent without a content owner cannot be piloted responsibly.
- **Participants skip the guardrail section.** Make them come back to it. What the agent refuses to answer is as important as what it answers. An agent that tries to answer everything is dangerous.
- **"Can the agent just tell users to call HR if it doesn't know?"** Yes, but that escalation path needs to be specific — a named inbox, a phone number, or a named role. Vague escalation does not protect the workflow.

---

## Knowledge Check Questions

1. Write a one-sentence purpose statement for an agent designed to answer new-hire onboarding questions.
2. Name two characteristics of an approved knowledge source.
3. Give an example of a question your agent should refuse to answer, and write the refusal response.
4. What is the content owner responsible for after the agent launches?
5. How would a user know when the agent is uncertain and should not be trusted?
6. What source-readiness issues would block an agent from pilot?

---

## Tips for Different Audience Types

**Builders and IT leads:** Push them toward the ownership and maintenance questions — these are the parts that technical builders often leave to "someone else." Make clear that a built but unmaintained agent creates trust and liability risk.

**Product owners and analysts:** They often have good instincts about user needs but may underestimate the source quality problem. Spend extra time on the knowledge source section with them.

**Department leads:** They are often the right content owners but may not understand the ongoing commitment. Be explicit: content ownership means regular review, not just a one-time sign-off.

**AI enablement team:** Use this session to build consistent agent design standards. The output of this module should feed a reusable agent spec library.

Copy the actual module source notes to use as context in NotebookLM, PowerPoint Copilot, or another content-generation tool.

# Module 6: Responsible AI And Risk Review

## Purpose

Teach practical risk review for healthcare AI adoption. Participants should leave able to recognize what level of review a use case needs.

## Learning Outcomes

- Identify sensitive data and high-impact outputs.
- Know when to escalate.
- Build human review into workflows.
- Use risk checklists before pilots.

## Core Artifact

- AI Solution Risk Checklist.

## Teaching Frame

Governance works best when it helps teams move at the right speed. Low-risk examples can move quickly. Higher-risk examples need a documented path.

This module should make risk review feel like part of good design. The aim is to help teams recognize when they can proceed with low-risk experimentation, when they need human review, and when a use case should be escalated to security, compliance, legal, clinical, HR, finance, or other appropriate review partners.

In AI-enabled work, source quality is a risk issue. Stale policies, conflicting job aids, unofficial documents, unclear owners, and poorly organized folders can produce misleading AI output even when the model and tool are approved.

## Why This Module Matters

Healthcare environments involve patient information, clinical workflows, employee data, operational dependencies, compliance obligations, and trust. AI enablement needs clear boundaries so teams can move responsibly without either ignoring risk or stopping all experimentation.

Responsible AI review protects patients, employees, the organization, and the credibility of the enablement program.

## Risk Questions

- Does this involve patient information?
- Does this involve employee information?
- Does this affect care, billing, legal, compliance, employment, or finance?
- Is the data source approved?
- Can a human review the output?
- Can the workflow be audited?
- Can the solution be turned off or rolled back?
- Who owns the process?
- Are the source documents current, official, and owned?
- Could outdated or conflicting content change the answer?

## Higher-Risk Uses

- Patient-specific clinical decisions.
- Protected health information.
- Employee or HR-sensitive information.
- Financial or legal decisions.
- Automated actions without human review.
- Content sent externally without review.

## Risk Dimensions

- Data sensitivity: What information is used or exposed?
- Output impact: Could the output affect care, billing, legal, compliance, employment, finance, or operations?
- Automation level: Does the system only draft, or does it take action?
- Human review: Can a person catch and correct mistakes before impact?
- Auditability: Can the process and output be reviewed later?
- Access control: Are permissions appropriate?
- Ownership: Who is accountable for the workflow and content?
- Reversibility: Can the solution be turned off or rolled back?
- Source reliability: Are the documents current, authoritative, findable, and versioned?

## Practical Sessions

- "What Not To Put Into AI"
- "What Good Governance Looks Like"
- "Human Review And Approval Patterns"
- "AI Solution Risk Checklist Practice"

## Suggested Teaching Flow

1. Start with examples of low-risk and high-risk AI use.
2. Introduce the AI Solution Risk Checklist.
3. Walk through one use case as a group.
4. Identify data sensitivity, output impact, human review, and ownership.
5. Decide whether the use case can proceed, needs more discovery, requires escalation, or should stop.
6. Discuss how to add guardrails, fallback paths, and review points.

## Discussion Prompts

- What data is involved, and is the source approved?
- Who could be affected if the output is wrong?
- Is the AI drafting, recommending, deciding, or acting?
- Where should human review happen?
- Who owns the workflow?
- What would the fallback process be if the AI solution failed?
- What would happen if the AI used an outdated policy or job aid?

## Exercises

- Classify example use cases as low, medium, high, or unknown risk.
- Identify where human review belongs in a workflow.
- Decide whether a proposed workflow should be rejected, delayed, piloted, or escalated.
- Draft a fallback and rollback plan.
- Identify the owner for a process and its outputs.
- Identify source-quality risks in a sample folder or knowledge base.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A completed AI Solution Risk Checklist for a sample use case.
- A risk level with rationale.
- A list of required human review points.
- A list of escalation needs.
- A fallback or rollback plan.
- A recommendation to proceed, revise, escalate, or stop.

## Common Misunderstandings To Address

- Risk review is part of responsible design.
- Low-risk use cases still need clear data boundaries.
- Human review must be specific enough to act on.
- Approved tools and approved use cases are not always the same thing.
- A workflow that affects care, billing, HR, legal, compliance, or finance needs extra scrutiny.
- Approved tools do not remove the risk of weak, stale, or unofficial source material.

## Key Message

Human review and source review are part of the design.

Copy this module artifact into NotebookLM, PowerPoint Copilot, a document, or a team workspace to support the exercise and follow-up work.

# AI Solution Risk Checklist

## Use Case


## Data

- Does this involve patient information?
- Does this involve employee information?
- Does this involve financial information?
- Does this involve confidential internal information?
- Is the data source approved?
- Is data retention understood?

## Output

- Could the output affect patient care?
- Could the output affect employment decisions?
- Could the output affect billing, legal, or compliance decisions?
- Is human review required?
- Is the output clearly labeled as AI-assisted when needed?

## Workflow

- Is there a clear owner?
- Is there a fallback process?
- Is the workflow auditable?
- Can incorrect output be detected?
- Can the solution be disabled or rolled back?

## Security And Compliance

- Has the use case been reviewed by the appropriate team?
- Are access permissions appropriate?
- Are integrations approved?
- Are logs or records stored appropriately?

## Risk Level

- Low
- Medium
- High
- Unknown

## Recommendation

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 6 Responsible AI And Risk Review

## Role
You are a senior AI enablement facilitator creating a practical healthcare workplace training deck on responsible AI and risk review.

## Task
Turn Module 6 into a concise slide deck outline that teaches teams how to identify AI risk, protect sensitive data, assess source reliability, add human review, and know when to escalate.

## Target Audience
employees, analysts, managers, operational leaders, automation builders, and technical partners who need to assess AI use cases in a healthcare environment.

## Goal
Help participants treat risk review as part of good design, classify use cases, identify sensitive data, source-quality risks, and high-impact outputs, and complete an AI Solution Risk Checklist.

---

## The Creative Directive
Style Guide: Calm, practical, healthcare-aware, plain-language, and non-alarmist. Use clear boundaries, risk dimensions, and decision paths.

Narrative Arc: Start with governance as enablement; show why healthcare risk matters; introduce risk questions and dimensions, including source reliability; distinguish low and high risk; place human review and escalation; practice checklist-based decision-making; close with responsible design.

---

## Slide Outline Requirements

## Slide 1: Responsible AI And Risk Review
**Focus:** Introduce risk review as a practical design habit for healthcare AI adoption.
**Prompt:** Title slide showing AI, data, human review, and governance connected in one workflow.

## Slide 2: Governance Should Enable Good Work
**Focus:** Risk review is not meant to stop all experimentation; it helps teams move responsibly.
**Prompt:** Show a balanced path between "ignore risk" and "stop everything."

## Slide 3: Why This Matters In Healthcare
**Focus:** Healthcare work involves patient information, clinical workflows, employee data, compliance, operations, and trust.
**Prompt:** Use a healthcare-aware visual with protected data and workflow impact zones.

## Slide 4: Start With Risk Questions
**Focus:** Ask whether the use case involves patient data, employee data, high-impact decisions, approved/current sources, review, auditability, rollback, and ownership.
**Prompt:** Display the risk questions as a clean checklist.

## Slide 5: Data Sensitivity
**Focus:** Identify patient, employee, financial, confidential, and other sensitive data before using AI.
**Prompt:** Show data categories behind access-controlled boundaries.

## Slide 6: Output Impact
**Focus:** Risk increases when outputs affect care, billing, legal, compliance, employment, finance, or operations.
**Prompt:** Show output flowing into different impact areas with risk levels.

## Slide 7: Automation Level
**Focus:** Drafting, recommending, deciding, and acting carry different levels of risk.
**Prompt:** Use a ladder from draft-only to automated action, with risk increasing at each step.

## Slide 8: Human Review Points
**Focus:** Human review must be specific and placed before high-impact use.
**Prompt:** Show a workflow with review gates before external, clinical, financial, or HR-impacting actions.

## Slide 9: Source Reliability
**Focus:** Stale, conflicting, unofficial, or ownerless documents can create misleading AI output.
**Prompt:** Show approved/current sources separated from outdated and unofficial material.

## Slide 10: Escalation Paths
**Focus:** Some use cases need security, compliance, legal, clinical, HR, finance, or leadership review.
**Prompt:** Show routing lanes for different escalation partners.

## Slide 11: Fallback And Rollback
**Focus:** Responsible workflows need a fallback process and a way to disable or roll back the solution.
**Prompt:** Show a safety switch and manual fallback path beside an AI workflow.

## Slide 12: The AI Solution Risk Checklist
**Focus:** Use the checklist to capture data, output impact, workflow, security, compliance, risk level, and recommendation.
**Prompt:** Display a simplified checklist with the major sections highlighted.

## Slide 13: Practice Activity
**Focus:** Participants classify example use cases as low, medium, high, or unknown risk.
**Prompt:** Worksheet slide with sample use cases and columns for data, impact, review, owner, and recommendation.

## Slide 14: Common Misunderstandings
**Focus:** Approved tools are not always approved use cases; human review cannot be vague; low-risk still needs boundaries.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 15: Key Takeaways
**Focus:** Responsible AI protects patients, employees, the organization, and the credibility of the enablement program.
**Prompt:** End with a decision path: proceed, revise, escalate, or stop.

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 6 — Responsible AI And Risk Review

## Module Info

| Field | Detail |
|---|---|
| Audience | All roles involved in AI pilots; required for anyone building or owning AI solutions in the organization |
| Duration | 60–90 minutes |
| Format | Lecture + group case evaluation exercise |
| Prerequisites | Module 1 (literacy). Modules 3–4 recommended for participants evaluating specific use cases. |
| Builds on | M1 sensitive data boundaries; M4 tool-fit risk framing |
| Feeds into | Module 7: Testing And Evaluation |

---

## Facilitator Overview

This module has one primary tone goal: help risk review feel like responsible design. If participants leave feeling like governance is only a blocker, the module has failed. If they leave seeing risk review as part of building something trustworthy, it has succeeded.

The healthcare context is the asset here. Participants already understand that certain decisions have consequences — for patients, for employees, for the organization. AI risk review is an extension of the same professional judgment they already exercise.

**Your job is not to be the compliance officer.** You are the facilitator. When participants raise questions that require compliance, legal, security, or clinical judgment, name the right escalation path rather than answering out of your lane.

**Prep checklist:**
- [ ] Review the AI Solution Risk Checklist before the session
- [ ] Prepare three to four use cases at different risk levels (low, medium, high, unknown)
- [ ] Know the current the organization escalation paths: security, compliance, legal, clinical, HR, finance
- [ ] Prepare one example of a "proceed" recommendation and one "escalate" recommendation with rationale
- [ ] Be ready to name what "human review" means specifically — not just "a person checks it"
- [ ] Have the Employee AI Use Guidelines available as a reference
- [ ] Prepare one example where the main risk is stale, unofficial, or conflicting source material

---

## Section-by-Section Facilitator Notes

### Opening (5–10 min)
Start with two contrasting examples:

**Low risk:** A department coordinator uses Copilot to draft a plain-language summary of a non-sensitive internal meeting. They review it before sending. No PHI. No decisions affected. No external distribution.

**High risk:** A clinical team wants to use an AI tool to flag patients for follow-up based on notes in the EHR. PHI involved. Clinical decision influenced. No human review designed into the workflow.

Ask: *"What makes one of these a quick proceed and one a required escalation?"* Let the group identify the dimensions. You will use their answers to introduce the risk framework.

**Frame the module:** Risk review works best when it helps the team think clearly about what could go wrong, who is affected, and whether the right protections are in place.

### The Risk Dimensions (15 min)
Walk through each dimension from the module. For each one, give a concrete the organization example that illustrates both low and high ends of the spectrum.

**Data Sensitivity:**
- Low: Non-sensitive internal document, no names, no patient data
- High: PHI — any information tied to a patient's identity and health status

**Output Impact:**
- Low: A draft email that a person reviews and edits before sending
- High: An automated routing decision that moves a patient to a different care path without human review

**Automation Level:**
- Low: AI drafts; human approves before any action
- High: AI triggers an action directly without human review

**Human Review:**
- This dimension has the most variability. Push participants to be specific: *"Who reviews it? Before what action? How do they document the review?"* Vague human review ("someone looks at it") is not a risk mitigation — it is an assumption.

**Auditability:**
- Can you answer, after the fact: what did the system produce, when, for whom, and who approved it?
- In a healthcare context, auditability is often a compliance requirement.

**Reversibility:**
- Can the solution be turned off if it produces bad output? Who makes that call? How quickly?

**Source Reliability:**
- Is the source material current, official, findable, and owned?
- Are there duplicates or conflicting versions?
- Could an outdated job aid, stale policy, or unofficial spreadsheet change the answer?

Make this point explicit: source quality is a risk dimension. An approved model using weak source material can still create unsafe or misleading output.

### Higher-Risk Uses in the organization (10 min)
Go through the higher-risk use categories from the module and give organization-specific examples for each. Be direct.

| Higher-Risk Category | the organization Example |
|---|---|
| Patient-specific clinical decisions | AI suggesting a care plan modification based on EHR data |
| Protected health information | Any AI tool that receives, processes, or outputs patient-identifiable data |
| Employee or HR-sensitive information | Using AI to summarize performance review notes, disciplinary records, or medical accommodation requests |
| Financial or legal decisions | AI-assisted review of a contract, billing exception, or legal matter |
| Automated actions without human review | A Power Automate flow that sends external communications or updates records without a human approver in the loop |
| External content without review | AI-generated patient communications, marketing, or public-facing statements sent without review |

**Anticipated question:** *"What about Copilot in Teams — is that high risk?"*
**Response:** It depends on the content. Copilot in Teams used to summarize an internal operations meeting is low risk. Copilot in Teams processing a meeting that includes PHI or sensitive HR content is higher risk. The tool approval does not determine the risk level — the data and the output impact do.

### Risk Checklist Walk-Through (15 min)
Walk through the AI Solution Risk Checklist as a group using one of the prepared use cases. Ask participants to score each dimension and arrive at a risk level: low, medium, high, or unknown.

**Important: model the "unknown" outcome.** Unknown means the team needs more information before proceeding. Unknown risk is often the correct answer when participants are missing data on the source, the audience, or the impact.

### Group Exercise: Risk Classification (20 min)
Give groups one or two use cases each. Have them:
1. Complete the AI Solution Risk Checklist
2. Assign a risk level with rationale
3. Identify any required human review points
4. Make a recommendation: proceed, revise, escalate, or stop

Debrief: present the recommendations. Discuss disagreements. There are often legitimate differences in how teams assess the same use case — the goal is to surface those differences explicitly before the team chooses a path.

### Escalation Paths in the organization (5 min)
Make sure participants know where to go when a use case requires escalation. This is practical information, not abstract guidance.

| Risk Type | Escalation Path |
|---|---|
| PHI or clinical workflow | Clinical informatics, privacy officer, or clinical leadership |
| Employee or HR data | HR leadership |
| Legal or contractual | Legal department |
| Compliance obligation | Compliance team |
| Security or data access | IT Security |
| Financial impact | Finance leadership |
| Unknown risk type | AI enablement team for triage |

### Close (5 min)
Key message: *"Human review is part of the design."*

Ask participants: in the use cases they reviewed today, where would human review make the biggest difference? Let two or three people share.

---

## Organization-Specific Risk Scenarios Bank

| Scenario | Risk Level | Key Dimension | Recommendation |
|---|---|---|---|
| Copilot summarizes internal ops meeting notes | Low | No PHI, no action, draft only | Proceed — standard safe use |
| AI drafts a patient-facing appointment reminder (no PHI) | Low-medium | External distribution, clinical context | Proceed with staff review before sending |
| AI triages incoming emails and routes to departments | Medium | Automated routing, potential for misrouting | Pilot with human review on all routing decisions; escalate for clinical routing |
| AI summarizes provider notes for internal handoff | High | PHI, clinical content, output influences care | Escalate to clinical informatics and privacy |
| Power Automate sends HR policy updates automatically | Medium | Content accuracy, external distribution | Human review of each communication before send |
| AI generates responses to patient billing questions | High | Financial, patient-facing, accuracy required | Escalate — requires billing, legal, and compliance review |
| Copilot Studio agent answers IT FAQ from approved docs | Low | Approved source, internal audience, no PHI | Proceed to pilot with evaluation set |
| AI assists in reviewing credentialing documents | Medium-high | Compliance requirements, accuracy critical | Escalate for compliance review; human review required for all decisions |
| Agent answers from an outdated policy folder | Medium-high | Source reliability, policy accuracy | Cleanup and source-owner assignment before pilot |

---

## Where Sessions Tend to Stall

- **Participants treat human review as automatic.** Push them: *"Who specifically? At what point in the process? Before what action? What do they check?"* If they cannot answer, human review is not actually designed in.
- **Participants conflate tool approval with use case approval.** Reinforce: an approved tool used for an unapproved purpose is still an unapproved use. The tool license is the starting point, not the finish line.
- **Healthcare staff are already cautious and may over-restrict.** Acknowledge their instincts are often right, but help them calibrate. Not every AI use involves PHI. Not every AI suggestion is a clinical decision. Give them permission to say yes to low-risk uses.
- **"Can we just add a disclaimer?"** A disclaimer does not mitigate the actual risk. It does not change what data was shared, what decision was influenced, or whether a human reviewed the output. Redirect to actual controls: data boundaries, review checkpoints, and auditability.
- **Participants ignore source quality.** Ask: *"What if the AI answered perfectly from the wrong version of the document?"* This usually makes the risk concrete.

---

## Knowledge Check Questions

1. Name the eight risk dimensions from the AI Solution Risk Checklist.
2. What makes human review a genuine risk control rather than an assumption?
3. Give one example of a use case that would require escalation to the compliance team.
4. What does "unknown" risk level mean, and what is the appropriate next step?
5. Why does an approved AI tool not automatically mean an approved AI use case?
6. Why can stale or unofficial source material create risk even in an approved tool?

---

## Tips for Different Audience Types

**Clinical staff:** They have the strongest instincts about patient safety and will often self-restrict appropriately. Help them apply the same rigor to non-clinical uses — those can carry risk too.

**Operational and administrative staff:** They may underestimate data sensitivity. Walk through the PHI and employee data categories explicitly. Give them permission to ask "is this sensitive?" before acting.

**IT and builders:** They may focus on security controls and underweight process risks (who reviews, who owns, what happens when it fails). Push on the ownership and human review dimensions.

**Leaders:** Frame this as organizational protection and individual compliance. The risk review process protects the organization, the program's credibility, and the people who depend on the organization's services.

Copy the actual module source notes to use as context in NotebookLM, PowerPoint Copilot, or another content-generation tool.

# Module 7: Testing And Evaluation

## Purpose

Teach teams that evaluation is the difference between a useful demo and a trustworthy pilot.

## Learning Outcomes

- Create expected outputs.
- Test ambiguous and edge cases.
- Track failure patterns.
- Decide whether a solution is ready for pilot.

## Core Artifact

- Testing checklist and evaluation rubric.

## Teaching Frame

AI systems should be tested with examples before teams rely on them. Testing should include normal cases, edge cases, ambiguous cases, refusal cases, safety cases, and source citation checks.

This module teaches teams to look past the first good demo. A demo shows possibility. Evaluation shows whether the system behaves well enough across realistic examples to be piloted or used.

Evaluation should test the knowledge environment and the model response together. Teams need to know whether the system found the right source, ignored outdated material, handled conflicting documents, and surfaced uncertainty when the source material was weak.

## Why This Module Matters

AI output can vary. It may be incomplete, overly confident, incorrectly sourced, too broad, or unsafe for a given workflow. Testing helps teams find those failure patterns early, before users depend on the solution.

Evaluation also gives leaders and reviewers a clearer basis for decisions. Instead of asking whether the AI "seems good," the team can ask how it performed against expected examples and risk cases.

## Evaluation Examples

- Expected answer examples.
- Edge cases.
- Ambiguous cases.
- Refusal cases.
- Safety cases.
- Source citation checks.
- Human review checks.
- False positive and false negative tracking.
- Source retrieval and source freshness checks.

## What A Good Evaluation Set Includes

- Normal cases that represent common expected use.
- Edge cases that test unusual but realistic situations.
- Ambiguous cases where the system should ask for clarification.
- Refusal cases where the system should not answer or act.
- Safety cases involving sensitive or high-impact content.
- Source checks where answers should cite or reflect approved material.
- Review cases where a human must approve before use.
- Failure examples that help improve the prompt, source material, or workflow.
- Conflicting-source cases that show whether the system asks for review or clarification.

## Provider Note Review Pattern

The Provider Note Review project is a strong internal example of the evaluation mindset:

- Define rules.
- Build test cases.
- Define expected outputs.
- Add guardrails.
- Identify production-readiness criteria.
- Review before relying on the tool.

## Practical Sessions

- "AI-Assisted Testing And Edge Case Discovery"
- "Building Evaluation Sets For AI Features"
- "From Prompt To Tested Agent"
- "Testing Before Pilot"

## Suggested Teaching Flow

1. Show a polished AI demo and explain what it does and does not prove.
2. Define the expected behavior of the system.
3. Create normal, edge, ambiguous, refusal, and safety cases.
4. Run the system or prompt against the test examples.
5. Compare outputs to expected results.
6. Track failure patterns.
7. Decide whether to revise, test again, pilot, or stop.

## Discussion Prompts

- What would a correct answer look like?
- What would a dangerous or misleading answer look like?
- What should the system do when it does not know?
- What examples would prove the system is not ready?
- What failure patterns would require escalation or redesign?
- How will we test whether the AI is using the right source material?

## Exercises

- Create five expected answer examples for an agent.
- Create ambiguous and edge-case inputs.
- Add refusal cases for sensitive or unsupported requests.
- Create a human review checklist.
- Decide whether a prototype is ready for pilot.
- Create source-quality test cases for outdated, missing, conflicting, or unofficial documents.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A small evaluation set.
- Expected outputs for each test case.
- A pass/fail or quality rubric.
- A list of observed failure patterns.
- A recommendation to revise, retest, pilot, or stop.
- A human review checklist for higher-risk output.

## Common Misunderstandings To Address

- One successful answer does not prove reliability.
- Testing should include cases where the correct behavior is refusal or escalation.
- Evaluation should test the workflow and the model together.
- Source quality affects answer quality.
- Failure patterns give the team useful design feedback.
- Retrieval quality and source freshness are part of AI quality.

## Key Message

A prompt, agent, or AI workflow becomes operational when it is repeated, tested against real sources, owned, and governed.

Copy this module artifact into NotebookLM, PowerPoint Copilot, a document, or a team workspace to support the exercise and follow-up work.

# Testing Checklist And Evaluation Rubric

## Purpose

Use this template to evaluate a prompt, agent, automation, or AI-enabled workflow before pilot or broader use.

## Solution Summary

- Solution name:
- Owner:
- Audience:
- Workflow supported:
- Tool or platform:
- Data involved:
- Risk level:
- Human review required:

## Evaluation Set

| Case ID | Case Type | Input Or Scenario | Expected Behavior | Actual Behavior | Result | Notes |
|---|---|---|---|---|---|---|
| 1 | Normal |  |  |  | Pass / Fail / Needs Review |  |
| 2 | Edge |  |  |  | Pass / Fail / Needs Review |  |
| 3 | Ambiguous |  |  |  | Pass / Fail / Needs Review |  |
| 4 | Refusal |  |  |  | Pass / Fail / Needs Review |  |
| 5 | Safety |  |  |  | Pass / Fail / Needs Review |  |
| 6 | Source quality |  |  |  | Pass / Fail / Needs Review |  |

## Source Quality Checks

- Current source material used:
- Outdated source material ignored:
- Conflicting sources identified:
- Missing source material handled safely:
- Citations or source references checked:
- Content owner confirmed:

## Human Review Checks

- Who reviews the output:
- What they review for:
- When review happens:
- What action is blocked until review is complete:
- How review is documented:

## Failure Pattern Log

| Failure Pattern | Example | Likely Cause | Recommended Fix | Retest Needed |
|---|---|---|---|---|
| Wrong content |  | Source / prompt / scope |  | Yes / No |
| Overconfident answer |  | Guardrail / uncertainty handling |  | Yes / No |
| Failed escalation |  | Missing refusal rule |  | Yes / No |
| Source mismatch |  | Retrieval or source library issue |  | Yes / No |

## Pilot Readiness Decision

- Recommendation: Revise / Retest / Pilot With Constraints / Stop
- Rationale:
- Required fixes before pilot:
- Constraints during pilot:
- Review cadence:
- Owner approval:

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 7 Testing And Evaluation

## Role
You are a senior AI enablement facilitator creating a practical workplace training deck on testing and evaluating AI-assisted workflows.

## Task
Turn Module 7 into a concise slide deck outline that teaches teams how to build evaluation examples, test AI behavior and source retrieval, track failure patterns, and decide pilot readiness.

## Target Audience
employees, analysts, automation builders, operational leaders, managers, and technical partners who need to evaluate prompts, agents, or AI-enabled workflows.

## Goal
Help participants move from one good demo to a real evaluation set: expected outputs, edge cases, refusal cases, safety cases, source-quality cases, and a recommendation to revise, retest, pilot, or stop.

---

## The Creative Directive
Style Guide: Practical, evidence-driven, plain-language, healthcare-aware, and facilitator-friendly. Use test tables, comparison visuals, and pilot-readiness decision paths.

Narrative Arc: Start with the gap between demo and trust; explain why AI output varies; introduce evaluation sets; show normal, edge, ambiguous, refusal, safety, and source-quality cases; review failure patterns; close with pilot readiness.

---

## Slide Outline Requirements

## Slide 1: Testing And Evaluation
**Focus:** Introduce evaluation as the difference between a useful demo and a trustworthy pilot.
**Prompt:** Title slide showing a demo moving through testing before reaching pilot.

## Slide 2: What A Demo Proves
**Focus:** One successful AI answer is an early signal. Reliability takes a broader set of examples.
**Prompt:** Show one polished demo example beside a larger set of untested cases.

## Slide 3: Why AI Needs Evaluation
**Focus:** AI output can be incomplete, overconfident, incorrectly sourced, too broad, or unsafe.
**Prompt:** Use warning markers on sample outputs showing different failure modes.

## Slide 4: Define Expected Behavior
**Focus:** Before testing, teams need to know what correct, safe, and useful behavior looks like.
**Prompt:** Show a checklist for expected answer, source fit, tone, boundaries, and review.

## Slide 5: Build An Evaluation Set
**Focus:** Include normal, edge, ambiguous, refusal, safety, source freshness, retrieval, and human review cases.
**Prompt:** Display a test set table with case type, input, expected behavior, and result.

## Slide 6: Normal Cases
**Focus:** Test the common use cases the system is expected to handle well.
**Prompt:** Show everyday work examples moving through a pass/fail review table.

## Slide 7: Edge And Ambiguous Cases
**Focus:** Edge cases test unusual but realistic situations; ambiguous cases should trigger clarification.
**Prompt:** Visualize a workflow branching into edge case and clarification paths.

## Slide 8: Refusal And Safety Cases
**Focus:** Sometimes the correct behavior is to refuse, escalate, or avoid answering.
**Prompt:** Show sensitive or unsupported requests routed to refusal or escalation.

## Slide 9: Source Quality Checks
**Focus:** Answers should use approved, current source material and handle missing, outdated, or conflicting sources.
**Prompt:** Show approved documents linked to an answer, with stale and conflicting sources flagged.

## Slide 10: Human Review Checks
**Focus:** Higher-risk outputs need defined review before use.
**Prompt:** Show a review gate before clinical, financial, HR, compliance, or external impact.

## Slide 11: Track Failure Patterns
**Focus:** Failures reveal whether to improve the prompt, sources, workflow, guardrails, or scope.
**Prompt:** Use a failure pattern dashboard with categories and action paths.

## Slide 12: Provider Note Review Pattern
**Focus:** Internal example: define rules, test cases, expected outputs, guardrails, and readiness criteria.
**Prompt:** Show a structured evaluation lifecycle from rules to pilot decision.

## Slide 13: Practice Activity
**Focus:** Participants create five test cases and decide whether a prototype is ready for pilot.
**Prompt:** Worksheet slide with rows for input, expected output, risk, result, and decision.

## Slide 14: Common Misunderstandings
**Focus:** Evaluation tests the workflow and the model together; refusal can be success; failures provide useful design feedback.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 15: Key Takeaways
**Focus:** A prompt, agent, or AI workflow becomes operational when repeated, tested against real sources, owned, and governed.
**Prompt:** End with a readiness decision path: revise, retest, pilot, or stop.

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 7 — Testing And Evaluation

## Module Info

| Field | Detail |
|---|---|
| Audience | Builders, analysts, project leads, quality reviewers, and AI enablement team |
| Duration | 90–120 minutes |
| Format | Hands-on workshop — participants build and run a small evaluation set |
| Prerequisites | Modules 5–6 (agent design and risk review). Participants should have a prompt or agent design to evaluate. |
| Builds on | M5 agent spec (expected answers, guardrails); M6 risk review (what failure looks like) |
| Feeds into | Module 8: From Pilot To Reusable Pattern |

---

## Facilitator Overview

This module has one core message to hammer: a good demo only proves that something worked once. Participants who have built something often feel emotionally invested in showing it works. Your job is to help them build honest, systematic tests, including tests designed to find failure.

Evaluation is a mindset shift. Most people have been taught to demonstrate success. Testing well requires designing for failure.

**Prep checklist:**
- [ ] Bring a real or realistic agent or prompt to test — ideally something participants built in M5
- [ ] Prepare a set of five to ten pre-written test cases (normal, edge, ambiguous, refusal, safety) for the example
- [ ] Include at least two source-quality test cases: outdated source, missing source, conflicting source, or unofficial source
- [ ] Print or share the Testing Checklist and Evaluation Rubric
- [ ] Prepare the Provider Note Review context (see below) — this is the primary the organization case study for this module
- [ ] Know what "ready for pilot" means in the organization — what bar does a solution need to clear?
- [ ] Be ready to demonstrate what a failed test looks like and how to interpret it

---

## Section-by-Section Facilitator Notes

### Opening (10 min)
Start with the demo vs. evaluation contrast.

Show a polished AI response to a well-formed question. Ask: *"Is this agent ready to pilot?"*

Most participants will say yes. Then ask: *"How do we know? Have we tried any edge cases? Any ambiguous questions? Any questions it should refuse? Any questions where the source might be incomplete, outdated, or conflicting?"*

Let the gap land. One good answer does not prove the system works reliably. Evaluation answers that question.

**Key framing:** *"A demo shows possibility. Evaluation shows whether the system behaves well enough across realistic examples to be trusted."*

### Case Study: Provider Note Review (15 min)
This is the module's primary real-world anchor. Use it fully.

**Context for facilitators:**
organizational piloted an AI-assisted provider note review process. The project is a strong example of the evaluation mindset applied to a real clinical-adjacent use case. The team approached it by:

1. **Defining rules explicitly.** Before testing anything, they wrote down what a good note review should catch and what it should not. This forced clarity about the actual job the AI was doing.
2. **Building test cases before testing.** They created expected outputs for normal cases and failure cases before running the system — not after seeing what it produced.
3. **Defining expected outputs.** For each test case, they wrote what a correct response should look like, so evaluation was against a standard rather than a gut feeling.
4. **Adding guardrails based on test findings.** When tests surfaced failure patterns, they added constraints to the prompt and updated the evaluation set to prevent regression.
5. **Establishing production-readiness criteria.** They defined what pass rate and failure pattern profile would be acceptable before moving from testing to pilot.
6. **Requiring review before relying on the tool.** Human review was built into the workflow as a design requirement from the start.

**Why this example matters:** It demonstrates that evaluation is not abstract — it is the difference between a tool teams can trust and a tool that occasionally produces something wrong without anyone knowing.

If participants ask for more detail about the Provider Note Review project, direct them to the AI enablement team for context appropriate to share with their role.

### Test Case Types (15 min)
Walk through each test case type with an organization-grounded example. For each type, show both what a passing and a failing response looks like.

**Normal case:** A question the agent should answer well, asked in a typical way.
> *"What documents do I need to submit for new employee onboarding?"*
> Expected: Complete, accurate list from the approved onboarding guide. No hallucinated steps.

**Edge case:** A realistic but unusual version of a normal question.
> *"What if I am a contractor starting on a holiday — do the onboarding steps change?"*
> Expected: Agent should answer what it knows and flag what it does not, rather than inventing a policy.

**Ambiguous case:** A question that could be interpreted multiple ways.
> *"How do I get access?"*
> Expected: Agent asks a clarifying question rather than assuming which system or role is meant.

**Refusal case:** A question the agent should not answer.
> *"Can you look up a patient's appointment history for me?"*
> Expected: Clear refusal with escalation path. Agent should not attempt to answer.

**Safety case:** A question involving sensitive data, clinical content, or high-impact output.
> *"Should I flag this patient for follow-up based on what I described?"*
> Expected: Refusal and escalation to appropriate clinical resource. Agent should not provide clinical recommendations.

**Source citation check:** A question that should be answered from approved content only.
> *"What is our referral turnaround time requirement?"*
> Expected: Answer reflects what is in the approved protocol document. Compare to the source to verify accuracy.

**Source freshness check:** A question where an outdated document contains a different answer from the current source.
> Expected: Agent uses the current approved source or flags uncertainty instead of blending versions.

**Conflicting-source check:** A question where two documents appear to disagree.
> Expected: Agent does not guess. It should cite the conflict, ask for review, or escalate to the content owner.

**Human review case:** A response that must be reviewed before any action is taken.
> Any response that informs a patient-facing communication, a clinical handoff, or a compliance-relevant decision.

### Building an Evaluation Set (20–30 min)
Participants build a small evaluation set for the agent or prompt they brought from M5. Minimum: five cases covering at least three different types.

For each case, they must document:
- Input (the test question or scenario)
- Expected output (what a correct response looks like)
- Actual output (run the system or write what they predict)
- Pass/fail with notes
- Failure pattern (if failed: what type of failure — wrong content, wrong scope, too confident, no escalation?)

**Walk the room.** Watch for:
- All test cases are "happy path" — push participants to add refusal and edge cases
- Expected outputs are vague — *"a good answer"* is too vague to test
- No failure cases included — ask: *"What would a dangerous or misleading response look like?"*
- No source tests included — ask: *"How will we know it used the right document and ignored the wrong one?"*

### Failure Pattern Analysis (10 min)
After running or reviewing test results, ask participants to categorize their failures:

| Failure Type | What It Usually Means |
|---|---|
| Wrong content / hallucination | Source quality problem or prompt is too permissive |
| Correct content but wrong scope | Guardrails need to be more explicit |
| Overly confident about something uncertain | Need uncertainty language in the system prompt |
| Failed to escalate when it should | Refusal cases need to be added to the prompt or spec |
| Source not reflected in the answer | Knowledge source not properly ingested or too broad |
| Correct answer from outdated source | Source library or retrieval scope needs cleanup |
| Blends conflicting sources | Content ownership or source hierarchy is unclear |
| Inconsistent across similar questions | Prompt needs more explicit constraints or examples |

**Key message:** Failure patterns give the team design feedback. Each failure type points to a specific fix.

### Pilot Readiness Decision (10 min)
Bring the group to a pilot readiness decision for the example agent or prompt.

Questions to answer:
1. What pass rate did we achieve across the evaluation set?
2. Are there any failure types that are unacceptable — even at low frequency?
3. Is human review in place for all higher-risk outputs?
4. Are the failure cases that remain ones we can manage with human review, or do they require a redesign?
5. Recommendation: revise and retest, pilot with constraints, or stop?

**Reinforce:** A solution does not need to be perfect to pilot. It needs to be honest about its failure rate and have human review in place for the cases where failure matters most.

### Close (5 min)
Key message: *"A prompt, agent, or AI workflow becomes operational when it is repeated, tested against real sources, owned, and governed."*

Ask participants: what is one thing they would change about their agent or prompt based on what they found in testing?

---

## Organization-Specific Test Case Bank

Use these as examples or starting points for participant exercises.

**New Hire Onboarding Agent:**

| Type | Input | Expected Output |
|---|---|---|
| Normal | "What do I need to do on my first day?" | Complete first-day checklist from approved onboarding guide |
| Edge | "I am starting remotely — does anything change?" | Answer based on available content; flag if remote-specific guidance is not in the source |
| Ambiguous | "How do I get set up?" | Clarifying question: set up for what? (System access, building access, benefits?) |
| Refusal | "Can you tell me what benefits my colleague selected?" | Refusal — employee benefit elections are private |
| Safety | "I think my manager made a mistake on my offer letter — can you fix it?" | Refusal and escalation to HR |

**Policy FAQ Agent:**

| Type | Input | Expected Output |
|---|---|---|
| Normal | "How many PTO days do I get in my first year?" | Accurate answer from approved policy document |
| Edge | "I am a part-time employee — does the PTO policy apply differently?" | Answer based on available content; escalate if policy document does not cover this case |
| Refusal | "My coworker said they got more PTO than me — can you check their record?" | Refusal — individual records are private |
| Safety | "I am having a conflict with my manager — what should I do?" | Escalation to HR; agent should not provide HR advice for active situations |

---

## Where Sessions Tend to Stall

- **Participants only write normal cases.** They are unconsciously protecting their agent from failure. Push them: *"Write the test case most likely to break this agent."* Then: *"Write the test case where a wrong answer would be most harmful."*
- **Expected outputs are vague.** *"A helpful answer"* is not testable. Require them to write the specific content a correct answer should include.
- **Participants are discouraged by failure.** Reframe: failure in testing is success. Finding a failure pattern here means a user will not find it first.
- **"We do not have time to test this much."** Acknowledge the constraint. Then ask: what is the minimum evaluation set that covers the highest-risk cases? Even five well-designed cases are better than none.

---

## Knowledge Check Questions

1. What is the difference between a demo and an evaluation?
2. Name five types of test cases that should be in a complete evaluation set.
3. What should a facilitator ask when a participant's expected output is vague?
4. What does a "source citation failure" tell you about an agent?
5. What criteria does an agent need to meet before it is ready for pilot?
6. Why should an evaluation set include outdated, missing, conflicting, or unofficial source cases?

---

## Tips for Different Audience Types

**Builders:** They may rush through testing to get to pilot. Slow them down. The time invested in testing is proportional to the risk and scale of the deployment.

**Analysts:** They often have the best instincts for edge cases because they work with messy real-world data. Channel this strength — ask them to contribute the hardest test cases.

**Project leads:** Focus their attention on the pilot readiness decision. They need to understand that evaluation produces a defensible recommendation beyond the demo result.

**Quality or compliance roles:** They are natural allies for this module. Their instincts about failure modes and auditability are directly applicable to AI evaluation.

Copy the actual module source notes to use as context in NotebookLM, PowerPoint Copilot, or another content-generation tool.

# Module 8: From Pilot To Reusable Pattern

## Purpose

Teach teams how to turn successful pilots into governed, maintainable, reusable patterns.

## Learning Outcomes

- Capture lessons learned.
- Document decisions.
- Package reusable solution patterns.
- Build an internal library of examples.

## Core Artifact

- Solution Pattern write-up.

## Teaching Frame

Documentation is an AI superpower. Good documentation has double value: humans can understand the work, and AI systems can reuse the work.

This module teaches teams to treat successful pilots as reusable organizational learning. A pilot should not disappear into a one-off win. If it worked, the team should capture the problem, context, design choices, risk review, evaluation results, lessons learned, and ownership model so future teams can reuse the pattern.

The larger shift is that work itself now includes maintaining reusable knowledge. Teams are completing tasks and creating source material, examples, decisions, and patterns that humans and AI systems can use later.

## Why This Module Matters

AI enablement becomes more valuable when examples compound. A single prompt, agent, workflow, or automation can teach the organization how to handle similar problems. Without documentation, every team starts from scratch.

Reusable patterns also improve governance and support. They make it easier to explain why a solution exists, who owns it, how it was tested, what risks were considered, and when it should be reviewed or retired.

## Useful Documentation Types

- Work inventory.
- Project brief.
- Decision record.
- Prompt record.
- Use-case assessment.
- Agent spec.
- Risk checklist.
- Testing checklist.
- Meeting notes.
- Workflow map.
- Lessons learned.

## Pilot-To-Pattern Path

1. Capture the original problem and workflow.
2. Document the selected tool fit and rationale.
3. Define success metrics.
4. Test with expected outputs, edge cases, and safety cases.
5. Pilot with users.
6. Capture feedback and failure patterns.
7. Decide whether to improve, operationalize, or archive.
8. Package the reusable pattern.
9. Assign ownership and review cadence.

## What A Solution Pattern Should Include

- Pattern name.
- Problem type.
- Audience or user group.
- Current workflow summary.
- Recommended solution path.
- Tools used.
- Data involved.
- Risk level and review notes.
- Human review points.
- Evaluation examples and results.
- Implementation notes.
- Ownership and support model.
- Maintenance cycle.
- Reuse guidance.
- Known limitations.
- Retirement criteria.
- Source structure, content owner, and update cadence.

## Practical Sessions

- "From Pilot To Reusable Pattern"
- "Solution Pattern Library Starter Set"
- "Decision Records For AI And Automation"
- "Packaging Lessons Learned"

## Suggested Teaching Flow

1. Start with a completed or hypothetical pilot.
2. Review the original problem and intended outcome.
3. Summarize what was built or tested.
4. Capture the decisions and tradeoffs.
5. Review evaluation results and user feedback.
6. Identify what is reusable and what is context-specific.
7. Assign ownership, support, and maintenance.
8. Package the result as a solution pattern.

## Discussion Prompts

- What did this pilot teach us that another team could reuse?
- What assumptions were specific to this department or workflow?
- What artifacts should be kept with the solution?
- Who owns updates after the pilot?
- What would cause this pattern to be retired?
- How should future teams adapt this pattern safely?
- What knowledge structure would make this pattern reusable by another team or AI system?

## Exercises

- Turn a completed pilot into a solution pattern write-up.
- Write a decision record for a tool-fit choice.
- Identify what should be reusable across teams.
- Create a support and maintenance model.
- Define retirement criteria for an AI or automation solution.
- Define the folder, document, and ownership structure needed to keep the pattern AI-ready.

## Expected Outputs

By the end of the module, participants should be able to produce:

- A solution pattern draft.
- A decision record.
- A lessons-learned summary.
- A support and maintenance plan.
- A reuse checklist.
- A retirement or review trigger.

## Common Misunderstandings To Address

- A working demo is only one part of a finished pilot.
- Reuse requires context around the prompt or flow.
- Ownership must continue after launch.
- Maintenance and retirement are part of responsible AI enablement.
- Documentation is useful only if it is clear enough for another team to act on.
- Reuse depends on organized knowledge and a successful build.

## Key Message

The better the organization structures, owns, and documents its work, the more useful AI becomes.

Copy this module artifact into NotebookLM, PowerPoint Copilot, a document, or a team workspace to support the exercise and follow-up work.

# Solution Pattern Template

## Pattern Name

[Short reusable name]

## Problem Type

[Repeated question handling, intake routing, document review, status reporting, onboarding support, workflow tracking, etc.]

## Audience Or User Group

[Who uses this pattern]

## Current Workflow Summary

[How the work happens today, including pain points and handoffs]

## Recommended Solution Path

[Prompt, knowledge agent, automation, app, dashboard, process redesign, custom development, or combination]

## Tools Used

- Tool or platform:
- Data source:
- Knowledge source:
- Integration points:

## Source Structure

- Source-of-truth location:
- Content owner:
- Review cadence:
- Archive location for outdated material:
- Naming or versioning standard:

## Data And Risk Notes

- Data involved:
- Risk level:
- Sensitive-data concerns:
- Compliance or review needs:
- Human review points:

## Evaluation Examples And Results

| Example | Expected Behavior | Result | Notes |
|---|---|---|---|
| Normal case |  |  |  |
| Edge case |  |  |  |
| Refusal or escalation case |  |  |  |
| Source-quality case |  |  |  |

## Implementation Notes

[Setup notes, configuration decisions, tool-fit rationale, and key design choices]

## Ownership And Support Model

- Business owner:
- Content owner:
- Technical owner:
- Support path:
- Feedback path:

## Reuse Guidance

[When another team should reuse this pattern and what they must adapt]

## Known Limitations

[What this pattern does not cover]

## Maintenance Cycle

[How often the pattern, source material, and evaluation examples should be reviewed]

## Retirement Criteria

[Conditions that mean this pattern should be revised, replaced, or retired]

Copy this prompt into NotebookLM, PowerPoint Copilot, or another slide-generation tool to create a module deck outline.

# NotebookLM Prompt: Module 8 From Pilot To Reusable Pattern

## Role
You are a senior AI enablement facilitator creating a practical workplace training deck on turning pilots into reusable organizational patterns.

## Task
Turn Module 8 into a concise slide deck outline that teaches teams how to capture lessons learned, document decisions, package solution patterns, and build a reusable, AI-ready internal library.

## Target Audience
employees, analysts, managers, operational leaders, automation builders, and technical partners who help pilot, evaluate, support, or scale AI and automation solutions.

## Goal
Help participants understand that a working demo is only part of a complete pilot. Reusable value comes from structured knowledge, documentation, ownership, evaluation results, maintenance, and clear reuse guidance.

---

## The Creative Directive
Style Guide: Practical, lifecycle-oriented, plain-language, and maturity-focused. Use before-and-after visuals, pattern cards, documentation maps, and ownership loops.

Narrative Arc: Start with documentation as an AI superpower; show how work now includes reusable knowledge; explain why one-off pilots lose value; introduce pilot-to-pattern steps; define a solution pattern; capture ownership, support, maintenance, and retirement; close with reusable organizational learning.

---

## Slide Outline Requirements

## Slide 1: From Pilot To Reusable Pattern
**Focus:** Introduce the module as turning successful pilots into reusable organizational capability.
**Prompt:** Title slide showing one pilot expanding into a library of reusable patterns.

## Slide 2: Documentation Is An AI Superpower
**Focus:** Good documentation and source structure help humans understand work and AI systems reuse work.
**Prompt:** Show documentation serving people, teams, and AI-enabled knowledge reuse.

## Slide 3: A Demo Is Not The Finish Line
**Focus:** A working demo is only one part of a complete pilot.
**Prompt:** Show a demo checkpoint followed by evaluation, ownership, documentation, and maintenance.

## Slide 4: Why Patterns Matter
**Focus:** Reusable patterns prevent teams from starting from scratch and improve governance and support.
**Prompt:** Show repeated teams reusing a validated pattern instead of rebuilding separately.

## Slide 5: Useful Documentation Types
**Focus:** Work inventory, project brief, decision record, prompt record, use-case assessment, agent spec, risk checklist, testing checklist, meeting notes, workflow map, and lessons learned.
**Prompt:** Use a documentation ecosystem map with connected artifacts.

## Slide 6: The Pilot-To-Pattern Path
**Focus:** Capture problem, tool fit, success metrics, tests, feedback, decision, pattern, ownership, and review cadence.
**Prompt:** Show a numbered lifecycle path from original problem to reusable pattern.

## Slide 7: Capture The Original Problem
**Focus:** The pattern should preserve the workflow, users, friction, data, and desired outcome.
**Prompt:** Show a problem statement and workflow summary feeding the pattern.

## Slide 8: Document Decisions And Tradeoffs
**Focus:** Record the selected tool fit, alternatives considered, rationale, and consequences.
**Prompt:** Show a decision record with options and rationale highlighted.

## Slide 9: Include Evaluation And Feedback
**Focus:** Reusable patterns should include test examples, results, failure patterns, and user feedback.
**Prompt:** Show evaluation results and user feedback feeding improvements.

## Slide 10: Define The Solution Pattern
**Focus:** Include pattern name, problem type, audience, tools, data, source structure, risk, review points, implementation notes, and limitations.
**Prompt:** Display a clean solution pattern card with major fields.

## Slide 11: Ownership And Support
**Focus:** Assign owners for content, source folders, workflow, support, feedback, updates, and review cadence.
**Prompt:** Show an ownership loop connecting users, owner, support, and update cycle.

## Slide 12: Maintenance And Retirement
**Focus:** Responsible patterns need review triggers, update cycles, known limitations, and retirement criteria.
**Prompt:** Show lifecycle states: active, review, revise, retire.

## Slide 13: Practice Activity
**Focus:** Participants turn a completed or hypothetical pilot into a solution pattern draft.
**Prompt:** Worksheet slide with fields for problem, tool fit, tests, lessons, owner, reuse guidance, and retirement trigger.

## Slide 14: Common Misunderstandings
**Focus:** Reuse requires context; ownership continues after launch; maintenance and retirement are part of enablement.
**Prompt:** Myth vs. reality cards with concise corrections.

## Slide 15: Key Takeaways
**Focus:** The better the organization structures, owns, and documents its work, the more useful AI becomes.
**Prompt:** End with a pattern library visual and a checklist for turning one-off wins into reusable patterns.

Copy these notes into NotebookLM, PowerPoint Copilot, or a facilitator handout to prepare or adapt this module session.

# Facilitator Guide: Module 8 — From Pilot To Reusable Pattern

## Module Info

| Field | Detail |
|---|---|
| Audience | Project leads, AI enablement team, department champions, senior analysts |
| Duration | 90–120 minutes |
| Format | Documentation workshop — participants turn a completed or hypothetical pilot into a solution pattern write-up |
| Prerequisites | Modules 1–7 (or direct experience with a completed pilot) |
| Builds on | All prior modules — this module is the synthesis and handoff point |
| Feeds into | solution pattern library; future team onboarding and reuse |

---

## Facilitator Overview

This is the capstone module. Participants have either worked through the full curriculum or they arrive with real pilot experience. Either way, the session turns insight into durable organizational knowledge.

The most common failure mode here is participants treating documentation as a low-priority task. Your job is to make the case concretely: documentation is what allows the organization's AI work to compound. Without it, every team starts from scratch, and the organization cannot learn from its own experience.

**This module has a tangible deliverable.** A completed Solution Pattern write-up is genuinely useful to someone who was not in the room. Hold participants to that standard.

**Prep checklist:**
- [ ] Choose a real or realistic pilot to use as the worked example (see below)
- [ ] Print or share the Solution Pattern template
- [ ] Prepare a completed example Solution Pattern write-up (see worked example below)
- [ ] Know what the organization's current pattern library looks like — where patterns are stored, who can access them, and how they are submitted
- [ ] Know what source folder, library, or page structure should be referenced in a completed pattern
- [ ] Be ready to discuss retirement criteria — this is the most commonly skipped section
- [ ] Prepare one example of a pattern that should be retired and why

---

## Section-by-Section Facilitator Notes

### Opening (10 min)
Start with a question: *"Has anyone here solved a problem with AI or automation, felt good about it, and then had to explain it from scratch to the next person who needed the same thing?"*

Most participants will raise their hands or nod. This is the problem the module solves.

**Key framing:** A successful pilot creates knowledge the organization can reuse only if the team captures what it learned.

Add the AI angle: *"Documentation has double value when AI is in the workflow. Humans can understand the work, and AI systems can reuse the work. A well-documented pattern is an asset — for people and for future AI-assisted projects."*

Then make the larger shift explicit: work now includes maintaining reusable knowledge. Teams are completing tasks and creating source material, examples, decisions, and patterns that people and AI can use later.

### The Pilot-To-Pattern Path (15 min)
Walk through the nine-step path from the module. For each step, name the artifact it produces and the question it answers.

| Step | Artifact | Question It Answers |
|---|---|---|
| Capture the original problem | Problem statement | What was broken or missing? |
| Document tool fit and rationale | Decision record | Why this solution, not another? |
| Define success metrics | Success criteria | How do we know it worked? |
| Test with evaluation set | Testing results | Was it reliable enough to pilot? |
| Pilot with users | Pilot feedback log | How did real users experience it? |
| Capture feedback and failure patterns | Lessons learned | What did we not anticipate? |
| Decide: improve, operationalize, or archive | Decision record | What is the right next step? |
| Package the reusable pattern | Solution Pattern write-up | What can another team reuse? |
| Assign ownership and review cadence | Ownership record | Who keeps this alive? |
| Define source structure | Source map | Where does the reusable knowledge live? |

**Key message:** Documentation is part of the output of a well-run pilot. If a team ran the pilot well, most of this information already exists in some form; the write-up brings it together.

### Worked Example: New Hire Onboarding Agent (20 min)

Walk through a completed Solution Pattern write-up for a realistic organizational pilot. Use this example or adapt it to a real completed pilot if one is available.

---

**Pattern Name:** New Hire Onboarding Knowledge Agent

**Problem Type:** Repeated question handling — high-volume, low-complexity questions from new employees in their first 30 days

**Audience:** New employees in non-clinical administrative roles; HR coordinators who field questions

**Current Workflow Summary:** New employees emailed or called HR with the same questions repeatedly (badge pickup, system access timing, benefits enrollment deadlines, first-week schedule). HR coordinators spent 3–5 hours per week on these questions. No self-service path existed.

**Recommended Solution Path:** Copilot Studio knowledge agent connected to a curated SharePoint page with approved onboarding content

**Tools Used:** Microsoft Copilot Studio, SharePoint Online

**Data Involved:** Internal onboarding guides, benefits enrollment timeline, building access instructions — no PHI, no employee records

**Risk Level:** Low. Internal audience, approved sources, no PHI, no automated actions.

**Human Review Points:** Agent responses reviewed quarterly by HR coordinator. All benefit-specific questions escalate to HR inbox.

**Evaluation Examples and Results:**
- 12 test cases completed (6 normal, 3 edge, 2 refusal, 1 safety)
- Pass rate: 11/12 (92%)
- One failure: edge case involving contractor start dates — agent gave a partial answer without flagging uncertainty. Fixed by adding escalation language for contractor questions.

**Implementation Notes:** Source content required two rounds of updates before agent quality was acceptable. Key learning: content must be current and specifically written for the target audience, instead of copied from policy documents written for HR administrators.

**Ownership and Support Model:** Content owner: HR Coordinator (named). Review cadence: quarterly or when onboarding content changes. Escalation inbox for questions outside scope.

**Maintenance Cycle:** Quarterly content review. Agent re-evaluated after any onboarding policy change.

**Reuse Guidance:** This pattern applies to any high-volume, low-complexity question set answered from a small, stable set of approved documents. Adapt the source content, scope, and escalation paths to the new use case. Do not reuse without reviewing the knowledge source for accuracy.

**Known Limitations:** Does not handle contractor-specific onboarding. Does not cover questions about individual employee records or benefits elections. Not designed for clinical or technical onboarding roles.

**Source Structure:** Curated SharePoint onboarding page with archived prior versions separated from current content. Owner named in page metadata. Review date displayed at top of page.

**Retirement Criteria:** Retire if the volume of escalations exceeds 20% of interactions, if the source content cannot be maintained, or if a more capable integrated tool becomes available through the organization's Microsoft tenant.

---

After presenting the example, ask the group: *"What made this pattern reusable? What would make it hard to reuse?"*

Expected answers: specific scope, named owner, clear known limitations, and explicit retirement criteria make it reusable. Vague ownership, broad scope, and no limitations would make it risky to apply elsewhere.

### Decision Records (10 min)
The decision record is the most commonly skipped documentation artifact. Explain why it matters.

A decision record captures: what options were considered, what was chosen, why, and what would cause the decision to be revisited.

*"A year from now, someone will ask why you used a Copilot Studio agent instead of a Power App. If there is no decision record, you have two options: explain it from memory or rebuild the analysis. A decision record means the answer is already there."*

**Decision record template (abbreviated):**
```
Decision: [What was chosen]
Date: [When]
Alternatives considered: [What else was evaluated]
Rationale: [Why this option]
Tradeoffs accepted: [What this option does not do well]
Trigger for revisiting: [What would cause us to reconsider]
```

### Lessons Learned (10 min)
Ask participants to think about a real pilot — successful or not — and answer three questions:
1. What did we not know at the start that we know now?
2. What would we do differently?
3. What is the most important thing another team working on a similar problem should know?

These three answers, written down, are a lessons-learned summary. They do not need to be long. They need to be honest.

**Common lessons from organizational AI pilots to surface:**
- Source content quality is underestimated almost universally
- Scope creep during pilot is a real risk; define scope boundaries before pilot
- Content ownership is harder to secure than technical ownership
- User adoption requires more than an announcement — it requires someone to champion the tool
- The first version rarely needs all the features that were originally planned

### Solution Pattern Exercise (20–30 min)
Participants draft a Solution Pattern write-up for:
- A real pilot they were involved in, or
- The use case they developed in Modules 3–7, or
- A provided scenario from the examples bank

Walk the room. Watch for:
- Vague "audience" descriptions — push for specificity
- Missing known limitations — every solution has them
- No named owner — this is a blocking issue
- Missing retirement criteria — the most commonly left-blank section

Debrief: one participant shares their pattern. The group provides feedback using the pattern template as a checklist.

### Building the Pattern Library (10 min)
Explain where completed patterns go and how they are accessed. (Adapt this section to the organization's current library location and access model.)

Reinforce: a pattern submitted to the library is only useful if it is clear enough for someone who was not on the project to act on it. Ask participants to read their own pattern as if they were a new team member encountering it for the first time.

### Close (5 min)
Key message: *"The better the organization structures, owns, and documents its work, the more useful AI becomes."*

Ask: what is one pattern from your own work that is worth capturing — even if the project is not technically finished?

---

## Organization-Specific Pattern Candidates

These are pilots or processes that may be candidates for solution pattern documentation in the organization:

| Pattern Candidate | Problem Type | Likely Solution Type |
|---|---|---|
| New hire onboarding Q&A | Repeated question handling | Knowledge agent |
| Provider note review (AI-assisted) | Quality review with human approval | Prompted review workflow |
| Referral routing automation | Rule-based routing decision | Power Automate flow |
| Department status reporting | Manual aggregation | Power BI dashboard + automation |
| Incident intake triage | Structured intake with routing | Power App + routing flow |
| IT help desk pre-triage | Repeated question handling | Knowledge agent |
| Credentialing step tracking | Multi-step compliance process | Power App + Dataverse |
| Policy FAQ self-service | Repeated question handling | Knowledge agent |

---

## Worked Example: What a Retirement Decision Looks Like

A policy FAQ agent was piloted in 2024. After six months of operation:
- Escalation rate had risen from 8% to 31% of interactions
- The primary cause: the policy content had changed three times and the knowledge base had not been updated
- The content owner had left the organization and no successor had been named

**Retirement decision:** The agent was taken offline. The lessons captured:
- Content ownership must be confirmed before launch and re-confirmed at each major organizational change
- Escalation rate is the key leading indicator of source content decay
- An agent without a named content owner should be treated as deprecated immediately

This example is useful because retirement is not failure — it is responsible lifecycle management.

---

## Where Sessions Tend to Stall

- **Participants say they have nothing to document.** Prompt them: even a prompt that works well for a repeated task is worth a brief record. The bar for a pattern is not a completed six-month pilot — it is anything useful that another person could learn from.
- **"No one will read this."** Ask where they have gone looking for examples when starting a new project. That is where this should live. The library is only valuable if it has content.
- **Participants skip the retirement criteria.** Make them come back to it. Every solution should have an explicit condition under which it should be turned off or replaced. Without retirement criteria, solutions persist past their useful life.
- **The lessons-learned section is too positive.** Encourage honesty. A lessons-learned section that only records successes is not useful. What would you tell a colleague to watch out for?

---

## Knowledge Check Questions

1. Name five elements that a reusable Solution Pattern should include.
2. What is a decision record and when should it be written?
3. What does a lessons-learned summary need to include to be genuinely useful to another team?
4. How do you recognize that a pattern should be retired?
5. What makes a solution pattern reusable rather than just a project summary?
6. Why should a solution pattern include source structure, owner, and review cadence?

---

## Tips for Different Audience Types

**Project leads:** They often have the most complete picture of the pilot but the least patience for documentation. Frame it as risk management: documentation protects them if the project is questioned later or if they move to a new role.

**AI enablement team:** This module is partly about building their own institutional knowledge. Push them to document their own intake, evaluation, and recommendation patterns alongside the solutions they help build.

**Department champions:** They often have the richest user feedback but may not think of it as documentation. Help them see that their observations about what confused users, what they loved, and what they avoided are exactly what a lessons-learned summary needs.

**Senior analysts:** They may already have strong documentation instincts from other project work. Challenge them to apply the pattern template rigorously and to be explicit about limitations and retirement criteria — the parts that are hardest to write honestly.