Certified AI Program Manager (CAIPM)

Price
$1,795.00 USD

Duration
3 Days

Delivery Methods
Virtual Instructor Led
Private Group

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Who Should Attend?

AI Program/Project managers, IT & Digital Transformation leaders, Business Strategy Leaders, Product Managers (AI-enabled products), Operations Managers driving AI initiatives

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Course Prerequisites

Not beginner-level Prior experience in IT, business, or cybersecurity is expected/recommended 2–3+ years professional experience recommended

Agenda

Module 01: 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 02: 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 03: 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 04: 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 05: 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 06: 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 07: 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 08: 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 09: 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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