AI+ Program Director – Practitioner™
Master AI Leadership with Practical Program Management
This course shows how AI can transform business strategy by teaching participants to design and implement AI initiatives that align with organizational goals and drive innovation. Learners gain hands-on experience leading AI projects, managing timelines, resources, and collaboration to ensure successful execution. The program also covers AI program integration, demonstrating how to embed AI into business processes for seamless adoption and maximum value. Participants develop skills to lead cross-functional AI teams, fostering collaboration and continuous improvement, while also learning how to future-proof AI programs by staying ahead of trends and adapting strategies for long-term competitiveness.
Enrollment Fee:
MUR 15,275
What You'll Learn
1.1 Understanding of AI, ML, and Deep Learning
1.2 AI Lifecycle & Real-World Applications
1.3 Societal Impact of AI
1.4 Use Case: Triage System (AI for Emergency Services)
1.5 Case Study: Retail Recommendation System (Personalizing Customer Experience)
1.6 Hands-on: Use Teachable Machine to Build a Simple AI Classifier
2.1 Introduce AI Strategy Alignment Frameworks: AI Canvas, Value vs Feasibility Matrix
2.2 Signs That a Process May Benefit from AI: Repetitive Tasks, Data-Rich Environments, Personalization Needs
2.3 Prioritization Techniques: Weighted Scoring, Risk-Adjusted ROI
2.4 Use-Case: Financial AI – Fraud Detection Systems Using AI
2.5 Case Study: AI-Driven Project Management System for a Program Director
2.6 Hands-on: Use Trello to Create a Board and Prioritize AI Opportunities Within a Given Scenario
3.1 Responsible AI Principles
3.2 AI Bias & Risk Mitigation
3.3 Use-case: Auditing Bias in AI-Powered Recruitment to Ensure Fair Hiring
3.4 Case Study: Mitigating Algorithmic Bias in Credit Scoring Models to Ensure Fair Lending Practices
3.5 Hands-on: Use Google’s What-If Tool in Google Colab to Evaluate Model Fairness and Bias
4.1 AI Project Planning & CRISP-DM
4.2 Integration: Build vs Buy vs Partner
4.3 AI Project Management Tools
4.4 Use Cases: AI for Predictive Maintenance (Asset Management in Manufacturing)
4.5 Tool-Based Hands-on Activity: Simulate an AI Project in Asana
5.1 Data Governance & Quality
5.2 Setting up Data Pipelines for AI
5.3 Sensitive Data Management
5.4 Use Case: Retail Inventory System — AI-driven Restocking and Demand Prediction
5.5 Case Study: Healthcare Data Security — Managing Patient Privacy in AI-Based Healthcare Systems
5.6 Tool-Based Hands-on Activity: Set up Airbyte Cloud and Build a Basic Data Pipeline
6.1 Evaluating AI Solutions
6.2 Vendor Evaluation & Management
6.3 Use Case: AI Vendor Selection — Choosing Predictive Maintenance Solutions for a Manufacturing Plant
6.4 Tool-Based Hands-on Activity: Use a Vendor Selection Template to Evaluate AI Vendors (Google Sheets)
7.1 Regulatory Frameworks
7.2 Bias Detection & Mitigation
7.3 Use Case: Facial Recognition Bias (Law Enforcement Systems)
7.4 Case Study: AI in Finance: Ensuring Compliance in AI Deployments
7.5 Tool-Based Hands-on Activity: Bias Testing & Fairness Evaluation Using KNIME and Google PAIR Facets Fairness Explorer
8.1 AI Project Management Tools
8.2 Data Management Tools
8.3 Case Study and Use Case: AI Workflow Management: Using project management tools for AI deployment in the retail sector
8.4 Tool-Based Hands-on Activity: Use Asana to simulate project timelines, setting up tasks and milestones for an AI initiative
9.1 Leading AI Teams & Change Management
9.2 Managing Stakeholders & Communication
9.3 Use Case: AI in Manufacturing: Leading AI Implementation in a Large-Scale Manufacturing Operation
9.4 Tool-Based Hands-on Activity: Use Miro to Map Stakeholder Communication Strategies and Identify Key Influencers
10.1 From Pilot to Full-Scale Deployment
10.2 Organizational Maturity Models for AI
10.3 Use Case: Scaling AI in Retail: Expanding AI-driven Recommendations Globally
10.4 Tool-Based Hands-on Activity: Create a Scaling Roadmap Using Lucidchart Outlining Key steps in Scaling AI Initiatives.
11.1 Emerging AI Technologies
11.2 Use Case / Case Study: AI in Autonomous Vehicles: The future of AI in self-driving cars
11.3 Tool-Based Hands-on Activity: Explore Hugging Face Transformers for NLP and TensorFlow for Deep Learning Applications
12.1 Capstone Project Overview
12.2 Presentation & Feedback
12.3 Final Review & Certification – Method, Process, and Feedback Mechanism
Prerequisites
- AI Fundamentals: Basic AI/ML concepts and terminology familiarity.
- Project Management: Experience managing projects, timelines, and stakeholders.
- Business Strategy: Understanding of business strategy and KPI-driven decision-making.
- Governance & Compliance: Working knowledge of data privacy, risk, and compliance.
- Leadership & Change: Comfort with cross-functional leadership and change management.
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 15,275