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code: ECC- CAIPM | version: v1

CAIPM - Certified AI Program Manager

Certified AI Program Manager (C|AIPM) credential is designed to transform experienced professionals into enterprise-ready AI program managers. This program bridges the gap between technical AI knowledge and business execution, equipping professionals to align AI with strategy, people, governance, risk management, and measurable ROI.

 

C|AIPM prepares you to:

  • Assess AI readiness across teams and processes
  • Prioritize AI use cases tied to business outcomes
  • Design adoption and rollout roadmaps
  • Coordinate delivery across cross-functional teams
  • Implement governance, Responsible AI, and security controls
  • Track performance and ROI to prove value

 

Certified AI Program Manager (C|AIPM) is not about building models. It is about making AI work on an enterprise scale predictably, securely, and sustainably.

 

Essential Skills You Will Gain with C|AIPM:

  • Strategize and Lead Adoption
    • Translate business goals into AI roadmaps
    • Define KPIs, measure ROI, and drive change
  • Operationalize AI
    • Understand MLOps and life cycle practices needed for production
    • Plan scalable AI architectures and operational workflows
  • Secure and Govern AI
    • Apply Responsible AI and risk governance
    • Integrate AI-specific security and compliance Practices
  • Harness Generative AI
    • Use GenAI safely and effectively for business outcomes
    • Apply prompt engineering methods to improve output quality
  • Deploy and Scale
    • Execute real-world rollouts across industries (e.g., finance, healthcare, manufacturing)
    • Drive adoption, usability, and sustained performance

 

Who is C|AIPM Ideal For:

  • Program and Technology Leadership
    • Program managers leading AI initiatives
    • Technology strategists and system integrators enabling AI missions
  • Policy, Risk, and Compliance
    • Policymakers overseeing responsible AI adoption
    • Compliance officers governing AI operational risk
  • Business and Operations
    • Business leaders aligning AI investments to ROI
    • Operations managers driving AI-enabled transformation
  • Security and IT Operations
    • Cybersecurity professionals involved in AI adoption and transformation
    • IT administrators supporting AI integration and deployment
  • Data and Analytics
    • Data analysts transitioning into AI operations roles
    • Data engineers supporting AI deployment pipelines

 

Each participant in an authorized training CAIPM - Certified AI Program Manager held in Compendium CE will receive a free CAIPM certification exam voucher.

 

Conspect Show list

  • Module 1 - AI Fundamentals for Business Adoption
    • Define AI and Distinguish it from Automation and Analytics in Business Contexts
      • Artificial Intelligence (AI)
      • Benefits and Limitations of AI
      • Evolution of AI
      • Automation, Analytics, and AI
      • AI as Augmentation vs. Automation
    • Identify Core AI Capabilities, Data Dependencies, and Common Failure Modes in Practice
      • How AI Transforms Data into Insights
      • AI Functional Capabilities
      • Data Dependencies
      • Common Failure Modes
      • Misinterpretations of AI Outputs
    • Differentiate Between Machine Learning, Deep Learning, Generative AI, and Agent Technologies
      • Types and Categories of AI
      • Types of AI in Business
      • Comparing AI Types for Business
      • What is Machine Learning?
      • Machine Learning Concepts
      • Neural Networks
      • Neural Network Architecture
      • Deep Learning (DL)
      • How DL Overcomes Limitations of ML
      • Working of DL
      • Large Language Models (LLMs)
      • Small vs. Large Language Models
      • Computer Vision
      • Natural Language Processing (NLP)
      • What is Generative AI?
      • Traditional AI vs Generative AI
      • Foundation Models
      • AI Agents and Copilots
      • Workflow Automation with AI
      • Embedded AI in Enterprise Applications
      • Key Terms for GenAI and Language Models
    • Identify Real-world AI Applications and Their Impact Across Industries
      • AI for Transforming Business Operations
      • AI for Business Collaboration
      • AI-Powered User Support
      • AI for Decision Quality Improvement and Business Innovation
      • AI Applications Healthcare and Finance
      • AI Applications in E-commerce and Manufacturing
      • AI Applications in Automotive and Telecommunications
      • AI Applications in Education and Utilities
      • AI Applications in Logistics and Media
      • AI Applications in Agriculture and Security
    • Understand AI Project Lifecycle and the Role of MLOps And DataOps In AI Adoption
      • Data Operations (DataOps) in AI Technology Stack
      • AI Development and Operations (MLOps) Lifecycle
      • Integration of DataOps, MLOps, and DevSecOps in AI
      • AI Project Lifecycle Phases and Gates
      • Initiation and Concept Development
      • Data Collection and Preparation
      • Model Development and Experimentation
      • Model Training, Validation, and Testing
      • Deployment and Release Management
      • Monitoring and Performance Tracking
      • Maintenance and Model Retraining Schedules
      • Retirement and Decommissioning Procedures
      • Post-deployment Evaluation and Success Metrics
      • Version Management and Rollback Procedures
    • Analyze Emerging AI Trends, Technology Drivers, Future Opportunities and Challenges
      • Emerging Trends in AI
      • Technological Advancements Driving AI
      • The Road Ahead: Opportunities and Challenges
  • Module 2 - Organizational Readiness and AI Maturity Assessment
    • Assess Organizational AI Readiness Across Strategic, Workforce, Data, and Technology Dimensions
      • Four Dimensions of AI Readiness
      • Strategic Readiness and Leadership Commitment
      • Workforce Readiness and Skill Distribution
      • Data Quality
      • Data Quality Metrics and KPIs
      • Data Readiness and Governance Maturity
      • Data Governance Framework
      • Data Privacy and Compliance for AI
      • Data Architecture for AI Workloads
      • Data Lifecycle Management for AI
      • Data Stewardship Roles and Responsibilities
      • Master Data Management for AI
      • Technology Readiness and Infrastructure
      • Cloud Infrastructure for AI Workloads
      • MLOps Capabilities Assessment
      • AI Security Considerations
      • Integration and API Readiness
      • GPU and Compute Requirements
      • Network and Latency Considerations
      • AI Model Monitoring and Observability
      • AI Disaster Recovery and Business Continuity
    • Apply AI Maturity Models to Benchmark Organizational Capabilities and Identify Progression Pathways
      • Five Stages of AI Maturity
      • Stages 1-2: Initial and Emerging
      • Stages 3-4: Defined and Managed
      • Stage 5: Optimized - AI Leadership
      • Centralized vs Decentralized AI Operating Models
      • Industry and Peer Benchmarking
    • Conduct AI Readiness Assessments Using Surveys, Interviews, Heat Maps, and Gap Analysis Techniques
      • Assessment Techniques Overview
      • Surveys and Stakeholder Interviews
      • Capability Heat Maps
      • Gap Analysis Framework
    • Identify and Categorize AI Adoption Risks Across Cultural, Process, Technology, and Regulatory Dimensions
      • Four Categories of Adoption Risk
      • Cultural and Behavioral Resistance Risks
      • Process and Operating Model Risks
      • Technology and Regulatory Risks
      • Risk Assessment Framework
  • Module 3 - AI Use Case Identification and Value Prioritization
    • Identify Business Problems Suited for AI by Recognizing Key Task Characteristics
      • What Makes a Problem AI-Suitable?
      • Repetitive and Rules-Based Activities
      • Data-Driven Activities
      • High-Volume Processes
      • High-Variability Processes
      • Human Judgment vs. AI Decision Boundaries
      • AI Suitability Decision Framework
    • Apply Structured Discovery Methods to Identify and Evaluate AI Opportunities
      • Use Case Discovery Methods
      • Functional Ideation Sessions
      • Cross-Functional Ideation Sessions
      • Process Mapping for AI Discovery
      • Pain-Point Analysis
      • Value Chain Opportunity Identification
    • Evaluate AI Use Cases Using Data, Feasibility, Complexity, and Risk Criteria
      • Use Case Qualification Framework
      • Data Availability Assessment
      • Data Quality Requirements
      • Feasibility Assessment
      • Implementation Complexity
      • Risk, Ethics and Compliance
      • Use Case Qualification Scorecard
    • Prioritize AI Use Cases Using Value Metrics, ROI Analysis, and Strategic Fit
      • Value and ROI Framework
      • Cost Savings Analysis
      • Revenue Impact Assessment
      • Risk Reduction Value
      • Time-to-Value and Scalability
      • Strategic Alignment Scoring
      • Value vs. Feasibility Prioritization Matrix
  • Module 4 - AI Strategy and Roadmap Development
    • Develop AI Strategy Aligning Vision, Guardrails, and Portfolio Investment Decisions
      • Two Approaches to AI Strategy
      • Business-Driven AI Strategy
      • Technology-Driven AI Strategy
      • AI Vision Statements
      • Strategic Guardrails for AI
      • Portfolio Approach to AI Initiatives
      • Balancing the AI Portfolio
    • Build AI Roadmaps Sequencing Initiatives by Dependencies, Value, and Readiness
      • AI Adoption Roadmap Components
      • Short-Term Pilots and POCs
      • Long-Term Transformation Initiatives
      • Dependency Mapping Framework
      • Dependency Analysis Process
      • Sequencing and Phasing AI Initiatives
      • Roadmap Governance and Review
    • Design AI Operating Models with Clear Roles, Accountability, and Decision Rights
      • AI Operating Models Overview
      • Center of Excellence (CoE) Model
      • Federated Model
      • Hybrid Model
      • Choosing the Right Model
      • Key AI Roles
      • Decision Rights and RACI
      • Accountability Framework
  • Module 5 - Change Management and AI Enablement
    • Understand AI Workforce Impact and Build Trust Through Transparent Change Leadership
      • Understanding AI-Induced Change
      • Workforce Role Evolution
      • Job Redesign Approaches
      • Skill Shifts and Reskilling Requirements
      • Building a Reskilling Program
      • Psychological Impacts of AI
      • Building Trust in AI
    • Apply ADKAR and Kotter Frameworks to Lead Successful AI Adoption Initiatives
      • Why Change Management for AI
      • The ADKAR Model
      • Applying ADKAR to AI Programs
      • Kotter's 8-Step Change Model
      • Applying Kotter to AI Programs
      • Sponsorship and Leadership
      • Communication Strategy
      • Managing Resistance
      • Transitioning Users to Approved AI Tools
      • Addressing Fear of Displacement
    • Design Role-based AI Training Programs that Build Practical Workforce Capabilities
      • AI Literacy Framework
      • Foundational AI Awareness Training
      • Role-Based AI Enablement
      • Prompt Engineering for Business Users
      • Prompt Troubleshooting Techniques
      • Executive AI Fluency
      • Manager AI Enablement
      • Building an AI Learning Culture
      • Enablement Program Metrics
    • Implement Champions, Communities, and Incentives that Sustain AI Adoption Momentum
      • Why Reinforcement Matters
      • AI Champions Program
      • Super-User Networks
      • Communities of Practice
      • Running Effective CoPs
      • Incentives and Recognition
      • Gamification and Challenges
      • Measuring Reinforcement Effectiveness
  • Module 6 - AI Platforms, Tools, and Ecosystem
    • Navigate Enterprise AI Landscape Including Generative Platforms, Copilots, and Custom Solution Evaluation
      • The AI Tool Landscape
      • Generative AI Platforms
      • Understanding AI Copilots
      • Major Enterprise Copilots
      • AI Embedded in Enterprise SaaS
      • AI-Embedded SaaS by Category
      • Custom AI Solutions
      • Configurable AI Solutions
      • Custom vs. Configurable Decision Framework
      • Build vs. Buy Considerations
      • Emerging AI Tool Trends
    • Apply Structured Frameworks to Evaluate AI Tools for Fit, Security, and Vendor Maturity
      • AI Tool Evaluation Framework
      • Functional Fit Assessment
      • Usability Assessment
      • Security Considerations
      • Privacy and Data Handling
      • Access Controls and Governance
      • Vendor Maturity Assessment
      • Roadmap and Support Evaluation
      • Evaluation Scorecard
      • Evaluation Process
    • Integrate AI Tools with Enterprise IT Systems Using Data Pipelines and Access Controls
      • AI Integration Landscape
      • Integration Patterns
      • Data Pipelines for AI
      • RAG Architecture Pattern
      • Interoperability Challenges
      • Identity and Access Management
      • Usage Controls and Policies
      • Deployment Models
      • Implementation Checklist
  • Module 7 - Governance, Ethics, and Safe AI Adoption
    • Establish AI Governance with Defined Roles, Policy Enforcement, and Escalation Handling Processes
      • Why AI Governance Matters
      • AI Governance Framework
      • Governance Roles Across Adoption Lifecycle
      • Key Governance Roles
      • AI Steering Committee
      • Policy Enforcement at Usage Level
      • Adoption-Centric Vendor Due Diligence for AI Usage Authorization
      • Identifying and Governing Unauthorized AI Usage
      • Usage Policies in Practice
      • Legal and Regulatory Clearance for AI Usage Authorization
      • SaaS AI Licensing and Consumption Risk Assessment
      • Escalation Pathways
      • Exception Handling Process
      • Governance Maturity Stages
    • Implement AI Usage Incident Handling and Corrective Actions
      • AI Incident Management and Response
      • Common AI Adoption Incidents
      • AI Incident Response Workflow
      • Escalation Pathways
      • User-Level Corrective Actions
      • Post-Incident Governance Updates
    • Implement Ethical AI with Bias Awareness, Human Oversight, and Acceptable use Guidelines
      • Why Ethics Matter in AI Adoption
      • Bias Awareness for Business Users
      • Common Types of AI Bias
      • Human Oversight Principles
      • Decision Accountability
      • Misuse Prevention
      • Acceptable Use Guidelines
      • Building an Ethical AI Culture
    • Navigate AI Risk and Compliance with Regulatory Awareness, Auditability, and Traceability Requirements
      • Risk Landscape for AI Adoption
      • Adoption-Stage vs. Development-Stage Risks
      • Common AI Adoption Risks
      • Risk Exposure from Shadow AI
      • Regulatory Landscape
      • Global AI Regulatory Landscape
      • EU AI Act: Risk-Based Framework
      • US AI Regulatory Framework
      • Sector-Specific AI Regulations
      • Data Privacy Laws and AI
      • GDPR: AI-Relevant Requirements
      • US Privacy Laws Affecting AI
      • Data Security Standards and Frameworks
      • ISO/IEC 42001:2023
      • ISO 42001 Structure and Clauses
      • ISO 42001 Implementation and Certification
      • Government Data Governance for AI
      • Publicly Procured Data and AI Use
      • FedRAMP and FISMA for AI Systems
      • NIST SP 800-218A: Secure GenAI Development
      • SP 800-218A: Key GenAI Security Practices
      • DoDI 8510.01: Risk Management Framework
      • RMF 7-Step Process
      • RMF for AI/ML Systems
      • Major Laws, Frameworks and Standards Reference
      • Internal Policy Requirements
      • Change Readiness Validation
      • Traceability Expectations
      • AI Compliance Checklist
      • ML Blind Spots and Edge Cases
      • Impacts of Blind Spots and Edge Cases
      • Mitigating Blind Spots and Edge Cases
    • Apply DoD Ethical AI Principles and Responsible AI Practices in Mission Critical Defense Contexts
      • The Five DoD AI Ethical Principles
      • Responsible and Equitable
      • Traceable and Reliable
      • Governable - Human Control
      • Responsible AI (RAI) Framework
      • Analyzing Mission Priorities for AI
      • RAI Implementation Checklist
      • Staying Current on RAI Advancements
  • Module 8 - AI Pilot Execution and Scaled Deployment
    • Design AI Pilots with Clear Scope, Success Metrics, and Governance Risk Controls
      • Why Pilots Matter
      • Defining Pilot Scope
      • Setting Pilot Boundaries
      • Success Metrics for Pilots
      • Exit Criteria
      • Pilot-to-Authorization Decision Gates
      • Adoption Readiness Sign-Off Checklist
      • Governance Controls During Pilots
      • Risk Controls During Pilots
      • Pilot Planning Checklist
    • Execute AI Deployments through Phased Rollouts, Communication Plans, and Readiness Checkpoints
      • From Pilot to Production
      • Phased Rollout Strategies
      • Rollout Sequencing Options
      • Communication Planning
      • Training Alignment
      • Change Readiness Validation
      • Support Model for Rollout
      • Rollout Planning Checklist
    • Scale AI Adoption by Capturing Lessons and Mitigating Enterprise-wide Expansion Risks
      • Capturing Lessons Learned
      • Applying Pilot Insights
      • Scaling Across Teams
      • Scaling Across Regions
      • Adoption Risks at Scale
      • Risk Mitigation Strategies
      • Continuous Optimization
      • Scaling Success Indicators
  • Module 9 - Measuring AI Adoption Impact and Value
    • Measure AI Adoption Effectiveness Through Engagement Metrics, Skill Progression, and Behavioral Signals
      • Why Measure Adoption?
      • Adoption Metrics Framework
      • Adoption Rate Calculations
      • Engagement Depth Funnel
      • Skill Progression Indicators
      • Proficiency Assessment Matrix
      • Behavioral Adoption Signals
      • Metrics for Shadow AI Reduction
      • Leading vs Lagging Indicators
      • Building an Adoption Dashboard
      • Common Measurement Pitfalls
    • Quantify AI Business Value Through Productivity Metrics and Value Realization Tracking
      • AI Cost Inputs in Adoption Measurement
      • AI Balancing Adoption Growth and Cost Efficiency
      • Identifying Overuse and Underuse Through Adoption Metrics
      • Prompt Efficiency as a Cost and Adoption Signal
      • Visualizing AI Cost and Adoption Through Dashboards
      • Cost Ownership and Accountability in AI Adoption
      • The Value Equation
      • Productivity Metrics
      • Efficiency Metrics
      • Quality Metrics
      • Financial vs Non-Financial Benefits
      • Calculating ROI
      • Value Realization Tracking
      • Building Value Stories
    • Communicate AI Value Through Executive Dashboards, Stakeholder Reports, and Feedback Loops
      • The Reporting Challenge
      • Stakeholder Communication Matrix
      • Executive Dashboard Design
      • Report Types and Cadence
      • Data Visualization Tips
      • Feedback Collection Methods
      • Continuous Improvement Loop
      • Acting on Feedback
  • Module 10 - Sustaining AI Transformation
    • Transition AI Pilots into Sustainable, Embedded Operations that Deliver Long-term Business Value
      • The Embedding Challenge
      • Operational Support Model for Embedded AI Adoption
      • Support Metrics for Sustaining Embedded AI
      • AI-Enabled Process Redesign
      • Process Redesign Framework
      • Human-AI Collaboration Models
      • The Collaboration Spectrum
      • Task Allocation Matrix
      • Long-Term Workflow Integration
      • Integration Maturity Staircase
      • Embedding Success Factors
      • Governance for Embedded AI
      • Common Embedding Pitfalls
    • Establish Processes to Continuously Improve AI Adoption and Adapt to Evolving Technology
      • The AI Landscape is Always Changing
      • Adoption Maturity Model
      • Maturity Assessment Dimensions
      • Responding to New AI Capabilities
      • Capability Evaluation Matrix
      • Managing Model, Tool, and Vendor Changes
      • Change Impact Assessment
      • Vendor Risk Management
      • Vendor Evaluation Scorecard
      • Continuous Improvement Cycle
      • Feedback Collection Mechanisms
      • Sustaining User Trust Through Continuous Adoption
      • Building a Learning Organization
      • Common Adaptation Pitfalls
    • Develop Leadership Capabilities and Cultural Practices that Sustain AI Transformation Long-term
      • Building an AI-First Mindset
      • Leadership Behaviors That Drive AI Culture
      • AI Talent Development Framework
      • Development Programs by Tier
      • AI Talent Retention Strategies
      • The AI Value Flywheel
      • AI Governance for Long-Term Success
      • Measuring Long-Term AI Success
      • Success Indicators by Timeframe
      • Common Culture Pitfalls and Fixes
    • Apply Human-centered Design Principles to Create Usable, Transparent, and Trustworthy AI Systems
      • What Is Human-Centered AI Design?
      • Human-Centered Design Principles for AI
      • User Experience Considerations for AI
      • AI Transparency and Explainability
      • Explainability Techniques
      • Building User Trust in AI
      • Human-in-the-Loop Design Patterns
      • Designing for AI Errors
      • Accessibility and Inclusion in AI
      • Ethical AI Design Considerations
      • Human-Centered AI Design Process
      • Common Human-Centered Design Pitfalls
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Additional information

Prerequisites

Students should have at least two years of experience in project management or IT leadership and a basic understanding of the software development life cycle. No prior coding or programming skills are required, as the focus is on strategic AI alignment and risk assessment.
Difficulty level
Duration 3 days
Certificate

The participants will obtain certificates signed by EC-Council (course completion). This course will help prepare you also for the CAIPM certification exam.

CAIPM v1 exam details:

  • Exam Code : 312-41
  • Number of Questions : 100
  • Duration : 3 hours
  • Availability: ECC Exam Portal
  • Test Format : Multiple Choice Question (MCQs)

Each participant in an authorized training CAIPM - Certified AI Program Manager held in Compendium CE will receive a free CAIPM certification exam voucher.
Trainer

Certified EC-Council Instructor (CEI)
Additional informations

The training materials include official EC-Council electronic courseware, 180-day access to iLabs, and an exam voucher.

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