Introduction
Artificial Intelligence (AI) projects differ significantly from traditional IT and software initiatives. AI implementations require specialized approaches for data governance, model management, ethical oversight, iterative development, operationalization, and continuous monitoring. Using the CPMAI™ methodology shall provide participants with a structured and vendor-neutral methodology for successfully managing AI initiatives from opportunity identification through operational deployment and handover.
This 5-day training course is designed to provide participants with practical and managerial competencies aligned with the CPMAI™ Phases, tasks, and enablers. Participants will gain the ability to manage AI initiatives strategically, technically, operationally, and ethically within enterprise environments.
This GLOMACS Cognitive Project Management in AI (CPMAI) training course will highlight the 6 Core Phases of the CPMAI™:
- Business Understanding
- Data Understanding
- Data Preparation
- Model Development
- Model Evaluation
- Model Operationalization (and Continuous Improvement)
Objectives
At the end of this Cognitive Project Management in AI (CPMAI) training course, you will learn to:
- Understand the CPMAI™ framework and AI project lifecycle.
- Identify and evaluate AI business opportunities and use cases.
- Manage AI project scope, risks, governance, and ROI.
- Manage the 6 phases of the CPMAI™ and apply Better Governance
- Develop AI performance metrics, KPIs, and monitoring mechanisms.
Training Methodology
This training course will utilise a variety of proven adult learning techniques to ensure maximum understanding and retention of the information presented. The facilitator will introduce each of the core topics using a lecture format. Presentations are supported by reinforcement exercises to emphasize the application of theory in real-world project settings. Peer learning is promoted as delegates are encouraged to discuss how techniques may apply within their own environments.
Organisational Impact
The Organisation will have the following benefits;
- Better governance and control of AI initiatives leading to improved success rates.
- Enhanced AI risk management and regulatory compliance.
- Stronger alignment between AI initiatives and business strategy as well as operation teams
- Improved AI organizational AI maturity, AI operational readiness and sustainability.
- Improved AI ethics, transparency, and accountability.
Personal Impact
At the end of this Cognitive Project Management in AI (CPMAI) training course, the participants will gain the following;
- Practical understanding of managing AI projects end-to-end aligned with CPMAI™.
- Improved strategic and technical decision-making skills.
- Better understanding of AI data and model lifecycle management.
- Increased professional value in AI transformation programs.
- Exposure to industry best practices in responsible AI management.
Who should Attend?
This GLOMACS Cognitive Project Management in AI (CPMAI) training course is suitable for a wide range of professionals but will greatly benefit:
- Project Managers and PMO Professionals
- AI Program and Product Managers
- Digital Transformation Leaders, IT and Technology Managers
- Data Governance Professionals, Business Analysts and Solution Architects
- Risk, Compliance, and Governance Teams
- Strategy, Engineering and Technical Managers
- Consultants managing AI initiatives
Cognitive Project Management in AI (CPMAI)
- Introduction to AI, Machine Learning, and Cognitive Technologies
- Differences Between Traditional Projects and AI Projects
- Overview of CPMAI™ Methodology and AI Project Lifecycle
- Responsible and Trustworthy AI Principles
- AI Governance, Ethics, Transparency, and Explainability
- AI Privacy, Security, and Data Protection Regulations (GDPR, CCPA)
Identifying AI Business Needs and AI-Project Planning
- Identifying AI Opportunities and Business Problems
- Stakeholder Analysis, Personas, Use Cases and Change Management
- AI Feasibility Assessment and Organizational Readiness
- AI Risk Assessment, Project Scope and Success Criteria
- ROI Analysis, Business Case Development, and Cost-Benefit Analysis
- AI Solution Architecture, Integration Planning and Resource Planning
AI Data Management and Data Readiness
- Understanding AI Data Requirements, Types and Governance
- Identifying Data Sources and Data Collection and Data Validation
- Data Privacy, Compliance, and Access Management
- AI Workspace Setup and Infrastructure Readiness
- Data Evaluation, Exploratory Analysis, and Bias Detection
- Data Readiness Assessment and Go/No-Go Decisions
- Communicating Data Readiness to Leadership
AI Model Development, Training, and Evaluation
- AI/ML Algorithms and Model Selection Techniques
- Supervised, Unsupervised, and Reinforcement Learning Overview
- Data Preparation, Cleaning, and Feature Engineering
- Model Training, Validation, AI Model QA/QC and Configuration Management
- AI Performance Metrics, Benchmarking and Model Explainability and Interpretability
- Go/No-Go Decision Criteria for Operationalization
AI Deployment, Governance, and Operationalization
- AI Solution Deployment Planning, Execution and Organizational Integration
- Monitoring Model Drift and Performance Degradation
- KPI Dashboards and AI Performance Monitoring
- AI Incident Management, Contingency Planning and Transition to Operations
- Lessons Learned, Knowledge Transfer, Final Reporting and Continuous Improvement
- Upon successful completion of this training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
Endorsed Education Provider
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