Depth
Applied
Applied-level articles focus on day-to-day practitioner work — methods, patterns, and deliverables used in live transformation engagements.
180 articles at this depth.
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Calibrate (40)
- M1.2
Prompt Anatomy and the Operator-User Distinction
AI Use Case Management - M1.3
Artifact Template: Experiment Brief
AI Governance Structure - M1.3
Case Study: Zillow Offers and the Missing Shadow Evaluation
AI Governance Structure - M1.3
Continuous Delivery and Governed Promotion
AI Governance Structure - M1.3
Continuous Integration for ML
AI Governance Structure - M1.3
Evaluating LLMs
AI Governance Structure - M1.3
Experiment Brief and Experiment Report
AI Governance Structure - M1.3
Experiment Cost and Compute Budget
AI Governance Structure - M1.3
Experiment Tracking, Reproducibility, and Replicability
AI Governance Structure - M1.3
Hyperparameter Search and Model Selection
AI Governance Structure - M1.3
Hypothesis Formulation and Metric Design
AI Governance Structure - M1.3
Lab 01: Design and Execute an Offline Evaluation Harness
AI Governance Structure - M1.3
Lab 02: Design an Online A/B Test with Sample-Size Calculation and Rollback Criteria
AI Governance Structure - M1.3
Offline Evaluation
AI Governance Structure - M1.3
Online Evaluation
AI Governance Structure - M1.3
Pipelines and Orchestration
AI Governance Structure - M1.3
Red-Team Experimentation for Safety
AI Governance Structure - M1.3
Regulatory Documentation for Experiments
AI Governance Structure - M1.3
What an AI Experiment Is
AI Governance Structure - M2.1
Client Discovery and Needs Assessment
AI Use Case Management - M2.1
Engagement Scoping and Architecture
AI Use Case Management - M2.1
Organizational Readiness Pre-Assessment
AI Use Case Management - M2.1
Risk Management in COMPEL Engagements
AI Use Case Management - M2.1
Stakeholder Alignment and Engagement Governance
AI Use Case Management - M2.1
Team Design and Resource Planning
AI Use Case Management - M2.1
The AITP as Engagement Leader — Professional Practice and Ethics
AI Use Case Management - M2.1
The Anatomy of a COMPEL Engagement
AI Use Case Management - M2.1
The Engagement Kickoff — Setting the Transformation in Motion
AI Use Case Management - M2.1
The Statement of Work — From Proposal to Contract
AI Use Case Management - M2.2
Agentic AI Maturity Assessment: Extending the 20-Domain Model
ML Operations and Deployment - M2.2
Assessment as a Continuous Practice
AI Governance Structure - M2.2
Assessment Data Analysis and Insight Generation
AI Governance Structure - M2.2
Beyond the Baseline — Advanced Assessment Philosophy
AI Governance Structure - M2.2
Cross-Domain Diagnostic Patterns
AI Governance Structure - M2.2
Data Quality and Technology Assessment Deep Dive
AI Governance Structure - M2.2
Deep-Dive Domain Assessment Techniques
AI Governance Structure - M2.2
Multi-Rater Assessment Methodology
AI Governance Structure - M2.2
Organizational Culture Assessment for AI Readiness
AI Governance Structure - M2.2
Stakeholder and Political Landscape Assessment
AI Governance Structure - M2.2
The Assessment Report — Communicating Findings with Impact
AI Governance Structure
Organize (14)
- M1.2
Foundational Prompting Patterns
AI Use Case Management - M2.3
Affected Community Engagement
AI Use Case Management - M2.3
Conducting a UNESCO-Aligned Ethical Impact Assessment
AI Use Case Management - M2.3
From Assessment to Action — The Roadmap Imperative
AI Use Case Management - M2.3
Gap Analysis and Initiative Identification
AI Use Case Management - M2.3
Initiative Sequencing and Dependencies
AI Use Case Management - M2.3
Resource Planning and Investment Architecture
AI Use Case Management - M2.3
Risk-Adjusted Roadmap Design
AI Use Case Management - M2.3
Roadmap Governance and Adaptive Management
AI Use Case Management - M2.3
Stakeholder-Specific Roadmap Communication
AI Use Case Management - M2.3
The Four-Pillar Roadmap Architecture
AI Use Case Management - M2.3
The Roadmap as a Living Document — Integration with the COMPEL Cycle
AI Use Case Management - M2.3
Tracking and Managing Ethical Debt
AI Use Case Management - M2.3
Value Milestones and Quick Wins
AI Use Case Management
Model (1)
Produce (14)
- M1.2
RAG Prompts and Grounding
AI Use Case Management - M2.4
AI Use Case Delivery Management
AI Project Delivery - M2.4
Change Execution — Operationalizing the People Pillar
AI Project Delivery - M2.4
From Roadmap to Reality — The Execution Challenge
AI Project Delivery - M2.4
Governance Execution — Building the Framework in Practice
AI Project Delivery - M2.4
Human-Agent Collaboration Patterns and Oversight Design
ML Operations and Deployment - M2.4
Multi-Workstream Coordination
AI Project Delivery - M2.4
Multi-Workstream Coordination
AI Project Delivery - M2.4
Operational Resilience for Agentic AI: Failure Modes and Recovery
ML Operations and Deployment - M2.4
Quality Assurance and Delivery Standards
AI Project Delivery - M2.4
Stakeholder Management During Execution
AI Project Delivery - M2.4
Technical Execution — Platform, Data, and Model Delivery
AI Project Delivery - M2.4
The Evaluate Transition — From Execution to Assessment
AI Project Delivery - M2.4
Troubleshooting and Recovery — When Execution Stalls
AI Project Delivery
Evaluate (19)
- M1.2
Tool Use and Function Calling
AI Use Case Management - M2.5
Agentic AI Cost Modeling: Token Economics, Compute Budgets, and ROI
ML Operations and Deployment - M2.5
Audit Trails and Decision Provenance in Multi-Agent Systems
ML Operations and Deployment - M2.5
Building the AI Business Case — Beyond Simple ROI
AI Project Delivery - M2.5
Business Value and ROI Quantification
AI Project Delivery - M2.5
Designing Measurement Frameworks for Agentic AI Systems
ML Operations and Deployment - M2.5
Designing the Measurement Framework
AI Project Delivery - M2.5
From Measurement to Decision — Data-Driven Transformation Management
AI Project Delivery - M2.5
Governance and Risk Metrics
AI Project Delivery - M2.5
Governance as Velocity Enabler — The Evidence
AI Project Delivery - M2.5
Maturity Progression Measurement
AI Project Delivery - M2.5
Measuring AI Adoption: Active Use, Time-to-Value, and NPS
AI Project Delivery - M2.5
Measuring AI Sustainability: Energy, Carbon, and Cost per Inference
AI Project Delivery - M2.5
Measuring AI Value: ROI, Outcome Attainment, and Productivity Uplift
AI Project Delivery - M2.5
People and Change Metrics
AI Project Delivery - M2.5
Technology and Process Performance Metrics
AI Project Delivery - M2.5
The Evaluate Stage in Practice
AI Project Delivery - M2.5
The Measurement Imperative in AI Transformation
AI Project Delivery - M2.5
Value Realization Reporting and Communication
AI Project Delivery
Learn (1)
Lifecycle-Wide (91)
- M1.1
Bias-Relevant Variables and Subgroup Coverage
AI Strategy and Alignment - M1.1
Case Study — Amsterdam SyRI and Rotterdam Welfare-Fraud Algorithm
AI Strategy and Alignment - M1.1
Data Governance and Data Contracts
AI Strategy and Alignment - M1.1
Data Lineage, Provenance, and Documentation
AI Strategy and Alignment - M1.1
Data Quality Dimensions Extended for AI
AI Strategy and Alignment - M1.1
Drift Monitoring, Incident Classification, and Sustainment
AI Strategy and Alignment - M1.1
Feature Stores and Vector Stores as Governance Artifacts
AI Strategy and Alignment - M1.1
Lab 1 — Dataset Profiling and Quality Scoring
AI Strategy and Alignment - M1.1
Lab 2 — Data Contract and Datasheet for a RAG Source
AI Strategy and Alignment - M1.1
Labeling Strategy and Annotation Governance
AI Strategy and Alignment - M1.1
Privacy, Sensitive Data Classes, and Data Minimization
AI Strategy and Alignment - M1.1
Template — AI Data Readiness Scorecard
AI Strategy and Alignment - M1.1
The Readiness Scorecard
AI Strategy and Alignment - M1.1
Third-Party and Open-Source Data Readiness
AI Strategy and Alignment - M1.1
What Data Readiness Is (and What It Is Not)
AI Strategy and Alignment - M1.2
Case Study 01: Three Chatbot Incidents — Chevrolet of Watsonville, Air Canada, and DPD
AI Use Case Management - M1.2
Lab 01: Build and Evaluate a Prompt Template Across Three Model Providers
AI Use Case Management - M1.2
Lab 02: Design an Evaluation Harness for a Retrieval-Augmented Feature
AI Use Case Management - M1.2
Prompt Evaluation Harness
AI Use Case Management - M1.2
Prompt Injection and Safety Boundaries
AI Use Case Management - M1.2
Prompt Lifecycle Governance
AI Use Case Management - M1.2
Template 01: Prompt Registry Entry and Test Plan
AI Use Case Management - M1.2
Transparency and Regulatory Obligations
AI Use Case Management - M1.4
AI Operating Model Blueprint Template
Integration Architecture - M1.4
Capability Mapping and AI-Impact Ranking
Integration Architecture - M1.4
Case Study: DBS Bank's Hybrid AI Operating Model
Integration Architecture - M1.4
Centre of Excellence Design
Integration Architecture - M1.4
Decision Rights, Accountability, and Separation of Duties
Integration Architecture - M1.4
Funding and Cost-to-Serve
Integration Architecture - M1.4
Integration with Existing Frameworks
Integration Architecture - M1.4
Lab: Build a Decision-Rights Matrix for an AI Risk Escalation
Integration Architecture - M1.4
Lab: Design a CoE for a 5,000-Person Organization
Integration Architecture - M1.4
Operating-Model Archetypes
Integration Architecture - M1.4
Operating-Model Maturity and Evolution
Integration Architecture - M1.4
Talent Models and Partner Ecosystems
Integration Architecture - M1.4
The Operating Model Blueprint
Integration Architecture - M1.4
What an AI Operating Model Is
Integration Architecture - M1.5
Adoption Metrics and Reinforcement
Regulatory Compliance - M1.5
AI Literacy Strategy
Risk Management - M1.5
AI-Specific Resistance Diagnosis
Risk Management - M1.5
Case Study: The Klarna Customer-Service AI Reversal
Regulatory Compliance - M1.5
Change Portfolio Management and Fatigue
Regulatory Compliance - M1.5
Classical Change Models and When Each Applies
Regulatory Compliance - M1.5
Communication Strategy
Risk Management - M1.5
Lab: Building a Resistance-Handling Playbook for a Contested AI Rollout
Regulatory Compliance - M1.5
Lab: Designing an AI Literacy Programme for Three Persona Groups
Regulatory Compliance - M1.5
Role Redesign and Human-AI Collaboration Patterns
Regulatory Compliance - M1.5
Stakeholder Landscape and Sponsor Strength
Regulatory Compliance - M1.5
Template: AI Change Plan
Regulatory Compliance - M1.5
The AI Change Plan
ML Operations and Deployment - M1.5
Training and Enablement Design
Data Management and Quality - M1.5
What AI Change Management Is
Regulatory Compliance - M1.6
Agent Architecture Patterns and Inventory
Change Management Capability - M1.6
Agent Observability and Audit
Change Management Capability - M1.6
Agentic Risk Taxonomy
Change Management Capability - M1.6
Autonomy Classification
Change Management Capability - M1.6
Case Study — Moffatt v. Air Canada (2024 BCCRT 149) as an Agentic Governance Failure
Change Management Capability - M1.6
Cross-Organizational and Supply-Chain Agents
Change Management Capability - M1.6
Delegation, Authority Chains, and Legal Implications
Change Management Capability - M1.6
Human Oversight Design Under EU AI Act Article 14
Change Management Capability - M1.6
Kill-Switch, Containment, and Incident Response
Change Management Capability - M1.6
Lab — Autonomy Classification Exercise
Change Management Capability - M1.6
Lab — Human Oversight Regime Design for a Finance Agent
Change Management Capability - M1.6
Memory Governance and Poisoning Defense
Change Management Capability - M1.6
Multi-Agent Systems and A2A Protocols
Change Management Capability - M1.6
Regulatory Obligations for Agentic Systems
Change Management Capability - M1.6
Template — Agent Governance Charter
Change Management Capability - M1.6
The Agent Governance Pack
Change Management Capability - M1.6
Tool-Use Governance and Excessive Agency
Change Management Capability - M1.6
What Agentic AI Is
Change Management Capability - M2.6
Building EU AI Act Evidence Portfolios
AI Use Case Management - M2.6
Case Study Methodology and Analytical Practice
AI Use Case Management - M2.6
COMPEL for Procured AI: Adapting the Methodology
AI Use Case Management - M2.6
Cross-Industry Pattern Analysis — Universal Themes and Sector-Specific Variations
AI Use Case Management - M2.6
Data Localization and AI — Navigating Residency Requirements
AI Use Case Management - M2.6
Energy and Utilities — AI Transformation in Critical Infrastructure
AI Use Case Management - M2.6
EU AI Act Compliance for Practitioners
AI Use Case Management - M2.6
Financial Services — AI Transformation in a Regulated Industry
AI Use Case Management - M2.6
Healthcare and Life Sciences — AI Transformation in Clinical Environments
AI Use Case Management - M2.6
Implement Once, Comply with Many: The COMPEL Harmonization Approach
AI Use Case Management - M2.6
Industry Context and the Universal COMPEL Framework
AI Use Case Management - M2.6
ISO 42001 Implementation Using COMPEL
AI Use Case Management - M2.6
Manufacturing and Industrial — AI Transformation on the Production Floor
AI Use Case Management - M2.6
Multi-Jurisdictional AI Compliance
AI Use Case Management - M2.6
NIST AI RMF Alignment with COMPEL Stages
AI Use Case Management - M2.6
Public Sector and Government — AI Transformation Under Public Accountability
AI Use Case Management - M2.6
Retail and Consumer — AI Transformation in a Competitive Marketplace
AI Use Case Management - M2.6
Shadow AI Discovery and Inventory Methodology
AI Use Case Management - M2.6
Technology and Software Companies — AI Transformation Beyond the Product
AI Use Case Management - M2.6
Vendor AI Due Diligence: The Comprehensive Assessment
AI Use Case Management - M2.7
AI-Augmented Governance — Using AI to Scale Oversight
AI Use Case Management