5. AI for Mid-Level Professionals and Managers: AI-Enabled Teams and Processes

Course Positioning

This course is for people who manage workflows, teams, client delivery, and outcomes. The emphasis shifts from personal productivity to process redesign, governance, evaluation, adoption, and ROI. Participants learn to identify use cases, build pilots, manage risk, and lead AI-enabled teams.

Learning outcomes

Identify high-value AI use cases within a team or department.
Separate tasks suitable for AI assistance from decisions requiring human accountability.
Design an AI workflow with inputs, tools, approvals, metrics, and escalation paths.
Create a pilot plan with ROI assumptions, risk controls, training needs, and success metrics.
Lead adoption through policies, playbooks, feedback loops, and change management.

Expanded Topic-by-Topic Coverage

Module 1. From individual productivity to team systems

Module focus: Workflow bottlenecks, handoffs, quality control, role redesign, shadow AI use. Primary live activity or lab: Map a team process end-to-end. Expected take-home output: Process map.

Topics and coverage

Workflow bottlenecks

What it means: show where Workflow bottlenecks appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

handoffs

What it means: define handoffs clearly and connect it to the module focus: Workflow bottlenecks, handoffs, quality control, role redesign, shadow AI use.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

quality control

What it means: define quality control clearly and connect it to the module focus: Workflow bottlenecks, handoffs, quality control, role redesign, shadow AI use.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

role redesign

What it means: show where role redesign appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

shadow AI use

What it means: define shadow AI use clearly and connect it to the module focus: Workflow bottlenecks, handoffs, quality control, role redesign, shadow AI use.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Map a team process end-to-end.
Learners produce: Process map.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 2. Use-case discovery and prioritization

Module focus: Impact, feasibility, data sensitivity, frequency, complexity, risk, ROI. Primary live activity or lab: Score 10 potential AI use cases. Expected take-home output: Use-case priority matrix.

Topics and coverage

Impact

What it means: define Impact clearly and connect it to the module focus: Impact, feasibility, data sensitivity, frequency, complexity, risk, ROI.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

feasibility

What it means: define feasibility clearly and connect it to the module focus: Impact, feasibility, data sensitivity, frequency, complexity, risk, ROI.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

data sensitivity

What it means: connect data sensitivity to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

frequency

What it means: define frequency clearly and connect it to the module focus: Impact, feasibility, data sensitivity, frequency, complexity, risk, ROI.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

complexity

What it means: define complexity clearly and connect it to the module focus: Impact, feasibility, data sensitivity, frequency, complexity, risk, ROI.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

risk

What it means in this course: define risk in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what team leads, managers, senior ICs, business owners must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

ROI

What it means: define ROI clearly and connect it to the module focus: Impact, feasibility, data sensitivity, frequency, complexity, risk, ROI.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Score 10 potential AI use cases.
Learners produce: Use-case priority matrix.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 3. AI workflow architecture

Module focus: Inputs, model/tool selection, prompts, templates, human review, storage, audit trail. Primary live activity or lab: Design a controlled workflow for one team task. Expected take-home output: Workflow blueprint.

Topics and coverage

Inputs

What it means: define Inputs clearly and connect it to the module focus: Inputs, model/tool selection, prompts, templates, human review, storage, audit trail.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

model/tool selection

What it means: place model/tool selection inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

prompts

What it means: explain how prompts changes the interaction between human intent, model behavior, external information, and final output.
What to cover: inputs, constraints, examples, output format, grounding, iteration, failure modes, and when a human must intervene.
Demonstration: show a weak attempt, a stronger structured attempt, and a reviewed final version with explicit checks.
Evidence of learning: learners create a reusable prompt, schema, retrieval note, or workflow pattern and test it on at least two examples.

templates

What it means: define templates clearly and connect it to the module focus: Inputs, model/tool selection, prompts, templates, human review, storage, audit trail.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

human review

What it means: define human review clearly and connect it to the module focus: Inputs, model/tool selection, prompts, templates, human review, storage, audit trail.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

storage

What it means: explain how storage changes the interaction between human intent, model behavior, external information, and final output.
What to cover: inputs, constraints, examples, output format, grounding, iteration, failure modes, and when a human must intervene.
Demonstration: show a weak attempt, a stronger structured attempt, and a reviewed final version with explicit checks.
Evidence of learning: learners create a reusable prompt, schema, retrieval note, or workflow pattern and test it on at least two examples.

audit trail

What it means: define audit trail clearly and connect it to the module focus: Inputs, model/tool selection, prompts, templates, human review, storage, audit trail.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Design a controlled workflow for one team task.
Learners produce: Workflow blueprint.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 4. Quality and evaluation

Module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards. Primary live activity or lab: Create an evaluation rubric for an AI-assisted output. Expected take-home output: Quality rubric.

Topics and coverage

Acceptance criteria

What it means: define Acceptance criteria clearly and connect it to the module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

benchmark examples

What it means: define benchmark examples clearly and connect it to the module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

error modes

What it means: define error modes clearly and connect it to the module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

sampling review

What it means: define sampling review clearly and connect it to the module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

rubrics

What it means: define rubrics clearly and connect it to the module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

dashboards

What it means: define dashboards clearly and connect it to the module focus: Acceptance criteria, benchmark examples, error modes, sampling review, rubrics, dashboards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Create an evaluation rubric for an AI-assisted output.
Learners produce: Quality rubric.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 5. Governance and policy

Module focus: Confidentiality, data classification, allowed tools, disclosure, vendor risk, escalation. Primary live activity or lab: Draft team AI usage policy. Expected take-home output: Policy one-pager.

Topics and coverage

Confidentiality

What it means in this course: define Confidentiality in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what team leads, managers, senior ICs, business owners must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

data classification

What it means: connect data classification to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

allowed tools

What it means: define allowed tools clearly and connect it to the module focus: Confidentiality, data classification, allowed tools, disclosure, vendor risk, escalation.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

disclosure

What it means: define disclosure clearly and connect it to the module focus: Confidentiality, data classification, allowed tools, disclosure, vendor risk, escalation.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

vendor risk

What it means in this course: define vendor risk in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what team leads, managers, senior ICs, business owners must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

escalation

What it means: define escalation clearly and connect it to the module focus: Confidentiality, data classification, allowed tools, disclosure, vendor risk, escalation.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Draft team AI usage policy.
Learners produce: Policy one-pager.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 6. Change management

Module focus: Training, champions, resistance, incentives, adoption metrics, management communication. Primary live activity or lab: Plan a rollout for a 30-day pilot. Expected take-home output: Pilot adoption plan.

Topics and coverage

Training

What it means: place Training inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

champions

What it means: define champions clearly and connect it to the module focus: Training, champions, resistance, incentives, adoption metrics, management communication.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

resistance

What it means: define resistance clearly and connect it to the module focus: Training, champions, resistance, incentives, adoption metrics, management communication.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

incentives

What it means: define incentives clearly and connect it to the module focus: Training, champions, resistance, incentives, adoption metrics, management communication.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

adoption metrics

What it means: define adoption metrics clearly and connect it to the module focus: Training, champions, resistance, incentives, adoption metrics, management communication.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

management communication

What it means: show where management communication appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

Practice and evidence of learning

Learners complete or discuss: Plan a rollout for a 30-day pilot.
Learners produce: Pilot adoption plan.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 7. Automation and integration

Module focus: No-code automations, CRM/ERP/document integrations, APIs, human-in-the-loop triggers. Primary live activity or lab: Prototype a low-risk automation flow on paper or with tools. Expected take-home output: Automation sketch.

Topics and coverage

No-code automations

What it means: define No-code automations clearly and connect it to the module focus: No-code automations, CRM/ERP/document integrations, APIs, human-in-the-loop triggers.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

CRM/ERP/document integrations

What it means: define CRM/ERP/document integrations clearly and connect it to the module focus: No-code automations, CRM/ERP/document integrations, APIs, human-in-the-loop triggers.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

APIs

What it means: define APIs clearly and connect it to the module focus: No-code automations, CRM/ERP/document integrations, APIs, human-in-the-loop triggers.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

human-in-the-loop triggers

What it means: define human-in-the-loop triggers clearly and connect it to the module focus: No-code automations, CRM/ERP/document integrations, APIs, human-in-the-loop triggers.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Prototype a low-risk automation flow on paper or with tools.
Learners produce: Automation sketch.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 8. Executive presentation

Module focus: Business case, risks, roadmap, budget, staffing, success criteria. Primary live activity or lab: Present pilot proposal. Expected take-home output: AI pilot deck.

Topics and coverage

Business case

What it means: show where Business case appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

risks

What it means in this course: define risks in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what team leads, managers, senior ICs, business owners must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

roadmap

What it means: define roadmap clearly and connect it to the module focus: Business case, risks, roadmap, budget, staffing, success criteria.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

budget

What it means: define budget clearly and connect it to the module focus: Business case, risks, roadmap, budget, staffing, success criteria.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

staffing

What it means: define staffing clearly and connect it to the module focus: Business case, risks, roadmap, budget, staffing, success criteria.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

success criteria

What it means: define success criteria clearly and connect it to the module focus: Business case, risks, roadmap, budget, staffing, success criteria.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Present pilot proposal.
Learners produce: AI pilot deck.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Labs, projects, and assessments

Lab 1: Department AI opportunity audit.
Lab 2: Build a use-case scoring matrix and select one pilot.
Lab 3: Create a team AI playbook with prompt templates, review steps, and prohibited uses.
Capstone: AI pilot proposal with workflow, budget, success metrics, and risk controls.

Evaluation approach

20% opportunity audit.
25% workflow blueprint.
20% governance playbook.
35% pilot proposal and presentation.

Recommended tools and materials

AI assistant, process mapping tool, Sheets/Excel, project management tool, automation platform such as Zapier/Make/n8n if available.
Optional: internal knowledge base and ticketing/CRM sandbox.

Safety, ethics, and governance emphasis

Include data classification and approval requirements before using AI with sensitive information.
Do not automate decisions affecting employees, customers, patients, finances, or legal obligations without explicit governance.
Measure both speed and quality; speed-only adoption creates hidden risk.

Delivery notes

Best delivered with real team leads and real workflows.
Encourage participants to leave with a pilot that can be started within 30 days.

Instructor Build Checklist

Prepare one short demo for each module and one learner activity that creates a saved artifact.
Prepare examples that match the audience, local context, and likely tools learners can access.
Add a verification step to every AI-generated output: factual check, source check, data sensitivity check, and quality review.
Keep a running portfolio folder so each module contributes to the final project or learner playbook.
Reserve time for reflection on what the learner did, what AI did, what was checked, and what remains uncertain.