AI+ Program Director – Practitioner™

AI Strategy Development: Learn to design and implement AI strategies that align with business goals, driving innovation and performance.
Leading AI Projects: Gain skills in managing AI projects, ensuring timely execution, resource allocation, and effective collaboration.
AI Program Integration: Understand how to integrate AI into business processes for seamless transitions and maximum value.
Managing AI Teams: Lead cross-functional teams, fostering collaboration and driving continuous improvement in AI initiatives.
Future-Proofing AI Programs: Stay ahead of AI trends and adapt strategies to ensure long-term competitiveness in the evolving landscape.

Overview What You'll Learn Certification Modules Certificate Industry Opportunities FAQ Prerequisites Exam Details Exam Blueprint

At a Glance: Course + Exam Overview

Category	AI Business, AI Professional, All Courses, Available Now, English, Language, Program Director
Program Name	AI+ Program Director Practitioner™
Duration	Instructor-Led:5 days (live or virtual) \| Self-Paced:40 hours of content
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 Format	50 questions, 70% passing, 90 minutes, online proctored exam

What You'll Learn

AI Program Management

Learn how to lead AI programs from strategy to execution, ensuring alignment with business objectives and optimizing resource use.

Cross-Functional Team Leadership

Gain skills in managing cross-disciplinary teams, coordinating between technical and non-technical stakeholders to deliver AI-driven projects.

AI Integration and Implementation

Understand how to integrate AI solutions into existing business systems, streamlining processes and enhancing operational efficiency.

Risk and Compliance Management

Learn how to assess and mitigate risks associated with AI projects while ensuring compliance with data privacy, security, and ethical standards.

Performance Evaluation and Optimization

Develop the ability to measure AI project success, optimize performance, and ensure that outcomes align with strategic business goals.

Certification Modules

Module 1: Foundations of AI for Program Strategy – Introduction

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

Module 2: Identifying AI Opportunities & Use Cases

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

Module 3: Governance & Ethics in AI

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

Module 4: AI Project Lifecycle & Integration

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

Module 5: Data Strategy & Infrastructure for AI

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

Module 6: AI Integration — Build vs Buy vs Partner

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)

Module 7: AI Risk Management & Compliance

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

Module 8: AI Tools & Techniques for Project Management

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

Module 9: Leadership in AI

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

Module 10: Scaling AI Initiatives

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.

Module 11: Future Trends in AI

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

Module 12: Capstone Project & Presentation

12.1 Capstone Project Overview
12.2 Presentation & Feedback
12.3 Final Review & Certification – Method, Process, and Feedback Mechanism

Finish the course and get certified

Industry Opportunities

AI Program Manager

Lead AI projects, ensuring alignment with organizational goals, managing timelines, and optimizing resources for successful AI program delivery.

AI Integration Specialist

Oversee the integration of AI solutions into existing systems, optimizing processes and ensuring smooth adoption across departments.

AI Strategy Consultant

Advise organizations on AI strategies, helping them align AI initiatives with business objectives to drive innovation and enhance efficiency.

AI Transformation Leader

Guide organizations through the AI adoption process, managing change and ensuring successful transformation using AI-powered solutions.

AI Portfolio Manager

Manage a portfolio of AI projects, balancing resources, timelines, and risks to ensure the successful execution and scaling of AI initiatives across the business.

Frequently Asked Questions

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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.