Leading AI at Work: Governance, Rollout and Practice
Professional · IT · Certificate available
186 lessons 2.4h runtime 9 units 244 links between lessons Test yourself · 9 quizzes, a mid exam, and a final
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Begin with Unit 1: Putting the Method Into Practice. It sets the foundation for everything that follows.
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Unit 1: Putting the Method Into Practice 22 lessons, 1 lecture part
Lecture: Watch · 28:46
- 001 One Analyst, One Monday
- 002 Nobody Has to Approve It
- 003 Before What Happens, What It Happens To
- 004 Stories Inside a Stronger Frame
- 005 When the Method Pays Off
- 006 Three Levels of Ambition
- 007 Path One: Individual Practice
- 008 Steering a Meeting Toward Things worked example
- 009 Path Two: A Single-Project Pilot
- 010 Three Additions to Existing Work
- 011 Evidence at the End of a Pilot
- 012 Path Three: An Organizational Pattern
- 013 Running Governance Checkpoints worked example
- 014 The Object Layer Under Any Framework
- 015 No New Platform Needed
- 016 Fitting the Method Into Backlogs and Wikis worked example
- 017 Fitting the Method Into Data, Tests and Prompts worked example
- 018 Prompt Plus Pack
- 019 Five Levels of Maturity
- 020 Level Three or Four Is Enough
- 021 Choosing Your Adoption Path worked example
- 022 An Analyst Starts Alone review
Unit 2: The 90-Day Pilot 24 lessons, 1 lecture part
Lecture: Watch · 29:47
- 026 A Few Things Defined Well
- 027 What a Pilot Delivers
- 028 Not Already on Fire
- 029 Days One to Fifteen: Scope and Seed worked example
- 030 Deliverables by Day Fifteen
- 031 Days Sixteen to Forty: First Packs and Cards worked example
- 032 Deliverables by Day Forty
- 033 Days Forty-One to Sixty: Tracing Gold Things worked example
- 034 Deliverables by Day Sixty
- 035 Days Sixty-One to Ninety: Use It and Measure worked example
- 036 Before-and-After Signals
- 037 The Story You Tell at the End
- 038 Who Does What in a Pilot
- 039 Who Owns a Gold Thing's Purpose
- 040 The Minimum Viable Adoption worked example
- 041 The Smallest Version That Helps
- 042 Resistance Shrinks Scope
- 043 "This Looks Like More Work"
- 044 "We're Agile"
- 045 "Our Domain Is Too Messy"
- 046 "We Already Have Models"
- 047 "Our Vendor Won't Support It"
- 048 A Conservative Return Scenario worked example
- 049 A Pilot Plan for Claims review
Unit 3: Introducing an AI Feature Safely 18 lessons, 1 lecture part
Lecture: Watch · 30:57
- 051 Where AI Helped in the Case
- 052 Where AI Was Not Allowed to Lead
- 053 Mistakes That Stood Out
- 054 Suggest or Automate
- 055 Not Ready to Ship
- 056 Introducing a Feature: Purpose and Things worked example
- 057 Introducing a Feature: Packs, Matrices, Contract worked example
- 058 Introducing a Feature: Oversight worked example
- 059 Flying Blind
- 060 The Gold Thing Safety Checklist worked example
- 061 Assessing AI Risk Around a Gold Thing worked example
- 062 Questions Before Deploying
- 063 Starting a New Initiative worked example
- 064 If You Do Nothing Else
- 065 The First Two Weeks worked example
- 066 A Drafting Assistant for Notices review
- 067 A Triage Score Near a Gold Thing review
- 068 A Feature Not Ready to Ship review
Unit 4: Domain Playbooks 22 lessons, 1 lecture part
Lecture: Watch · 26:27
- 076 "But That's Healthcare"
- 077 Every Field Has Its Incident Report
- 078 How the Playbooks Work
- 079 Draft Around, Never Decide
- 080 Benefits: Core and Gold Things
- 081 Benefits: Cards for the Determination worked example
- 082 Benefits: Where AI Must Not Decide
- 083 Healthcare: Orders and Consents
- 084 Healthcare: Consent and Medication Cards worked example
- 085 Wrong in a Friendly Way
- 086 Lending: The Credit Decision
- 087 Lending: Decision and Notice Cards worked example
- 088 Mitigated Is Not the Same as Fair
- 089 Insurance: Claims and Decisions
- 090 Triage Fills the Assessment
- 091 Platforms: Tenants and Subscriptions
- 092 Features, Not Magic
- 093 People Systems: Reviews, Applications, Pay
- 094 Screening Errors Are Silent
- 095 Six Domains, Same Moves
- 096 The Micro-Playbook for Your Domain worked example
- 097 A Playbook for a Licensing Office review
Unit 5: The Operating Model 25 lessons, 1 lecture part
Lecture: Watch · 32:30
- 101 The Pack Nobody Opened
- 102 Four Things an Operating Model Needs
- 103 Hats, Not Job Titles
- 104 The Steward and the Pack Editor
- 105 The Matrix Custodian
- 106 The AI Safety Lead
- 107 Alongside, Not Above
- 108 The Domain Council
- 109 Ritual One: Object Reviews worked example
- 110 Ritual Two: One Question in Change Control worked example
- 111 Ritual Three: The AI Feature Gate worked example
- 112 The One Step for AI Governance
- 113 Drawing the Suggest-or-Decide Boundary worked example
- 114 Boundaries Drift
- 115 Ritual Four: Definition-of-Done Lines worked example
- 116 Weaving the Method Into Delivery worked example
- 117 Metrics: Clarity and Rework
- 118 Metrics: Rewrites and Matrix Use
- 119 Acting on Overrides worked example
- 120 Where Everything Lives
- 121 Four Failure Modes
- 122 Recovering From a Failure Mode worked example
- 123 From Local Craft to a Center of Excellence
- 124 Evaluating a Change: Now or Later worked example
- 125 An Operating Model for a Claims Team review
Unit 6: Governing AI: NIST's Govern and Manage 22 lessons, 1 lecture part
Lecture: Watch · 27:19
- 126 The Regulator's Three Questions
- 127 Documents as Evidence
- 128 Govern Runs Throughout
- 129 What Govern Does
- 130 Documentation and Accountability
- 131 Effort Matched to Risk
- 132 Keeping an Inventory of AI Use worked example
- 133 Retiring an AI System Safely
- 134 Leadership Owns the Risk Decisions
- 135 Defined Roles for People and AI
- 136 Many Voices in Decisions
- 137 Feedback From Outside the Team
- 138 When a Third Party Fails
- 139 What Manage Does
- 140 Should It Proceed at All?
- 141 Choosing a Risk Response worked example
- 142 Residual Risk, Written Down
- 143 The Non-AI Alternative
- 144 Planning to Switch It Off worked example
- 145 Monitoring After Release worked example
- 146 Generative AI Warrants More Review
- 147 A Pre-Filled Field, Governed review
Unit 7: Teaching the Method 22 lessons, 1 lecture part
Lecture: Watch · 29:47
- 151 The Clinic That Ended an Argument
- 152 Practise on Real Confusion
- 153 Start With Their World
- 154 Concrete Examples, Early
- 155 Collaborative, Not a Lecture
- 156 Layer the Complexity
- 157 Tie It to AI and Change
- 158 The One-Day Workshop: Who and How Many
- 159 Workshop Morning: Framing, Map, Cards worked example
- 160 Workshop Afternoon: Pack, Matrix, AI worked example
- 161 AI in the Workshop: No Personal Data
- 162 Turning Energy Into Commitments
- 163 The Ninety-Minute Clinic worked example
- 164 Practise on Fictional Domains
- 165 Three Fictional Exercises worked example
- 166 Drill: Translate the Spec worked example
- 167 Drill: Align the Data Model worked example
- 168 Drill: Test Cases From a Card worked example
- 169 Drill: A Thing-Aware Prompt Library worked example
- 170 A Self-Study Path worked example
- 171 Measure Outcomes, Not Templates
- 172 Planning a Workshop for an Agency review
Unit 8: Your Next 90 Days, and the Limits 20 lessons, 1 lecture part
Lecture: Watch · 24:37
- 176 What You Do Differently
- 177 From Writer to Steward
- 178 From Translator to Conceptual Architect
- 179 From AI Consumer to AI Framer
- 180 Four Personal Habits
- 181 Your First Seven Days worked example
- 182 Your First Thirty Days worked example
- 183 Your First Ninety Days worked example
- 184 Why the Portfolio Matters
- 185 Coaching, Not Policing
- 186 A Bridge, Not a Rival
- 187 Answering Personal Skepticism worked example
- 188 A Pack for Your Own Practice
- 189 The Six Ideas, Plainly
- 190 From Requirements to Living Things
- 191 Capability Is Not Alignment
- 192 What the Method Cannot Do
- 193 Whose Work Is It?
- 194 Role Guides: Analyst, Architect, Tester worked example
- 195 Role Guides: Manager and AI Engineer worked example
Unit 9: Capstone: Your Own 90-Day Plan 11 lessons, 1 lecture part
Lecture: Watch · 31:15
- 201 The Small Challenge
- 202 Drafting Your First Silver Pack worked example
- 203 Showing It and Using It Once worked example
- 204 The Method on One Page
- 205 Capstone One: Path and Scope review
- 206 Capstone Two: The Pilot Plan review
- 207 Capstone Three: An AI Feature Through the Gate review
- 208 Capstone Four: Playbook, Rituals, Metrics review
- 209 Capstone Five: Govern and Manage review
- 210 Capstone Six: Starting Your Team review
- 211 The Whole Arc review
Who this subject is for
- Team leads and managers rolling out AI
- Business analysts and product owners
- Architects, testers and AI engineers
- Risk, governance and policy staff
Why Leading AI at Work: Governance, Rollout and Practice matters
Knowing a method is not the same as getting a team to use it. This course takes The M-CaReM-AI Method into practice: a ninety-day pilot, AI features introduced safely, an operating model that outlasts its champions, and governance that NIST's questions can check. It applies to:
- Piloting a new way of working
- Introducing AI features safely
- AI governance and oversight
- Running a methods practice
- Teaching and coaching colleagues
Time to complete
- of lessons (186, measured)
- 2.4 h
- of lectures (9)
- 4.4 h
- learning hours on the certificate, with quizzes and the final
- 10 h
Times are measured from the recordings and the exam time limits. Go at your own pace; nothing expires.
Micro-certificates from this course
Short sets of lessons from Leading AI at Work: Governance, Rollout and Practice, each with a free 10-question test and a $5 micro-certificate and badge when you pass.
Pathway · $35
Human in the Loop Pathway
The Full Certificates in Generative AI at Work, The M-CaReM-AI Method and Leading AI at Work, plus the nine micro-certificates built from those courses. Hold all twelve and claim the pathway certificate and badge.
Explore the Human in the Loop Pathway →Sources for Leading AI at Work: Governance, Rollout and Practice — the textbook it was written against, and the second source it was checked against.
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