AI Practitioner Course

AI Factor Institute

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Course Overview
Course Outline
Who Should Enroll
Key Takeaways

AI Practitioner Course:

Based on the four-step process outlined in the book The AI Factor, this course delves into understanding the four quadrants of AI application. It also explores the data readiness framework, the prioritization value and performance indicators, and the “AI Flywheel” concept. The course focuses on the practical application of these concepts to real-world business scenarios. This will help participants prioritize and position them for effective implementation.

Course Outline: AI Practitioner

Module 1: Analyzing Business Quadrants

Identifying Your Organization's AI Type
Tailoring AI Projects to Business Priorities
Workbook Exercise: Determining Your Organization's Quadrant

Module 2: Mastering Data Readiness

Assessing the State of Your Organization's Data
Overcoming Data Silos and Structuring Unstructured Data
Workbook Exercise: Assessing Data Readiness for a Single Project

Module 3: Prioritization Techniques

Aligning AI Projects With Organizational Types
Selecting High-Priority AI Projects
Workbook Exercise: Prioritizing AI Initiatives

Module 4: Sustainable, Responsible, and Ethical AI

Principles of Ethical AI: Privacy, Bias, Transparency
Workbook Exercise: Addressing Sustainability in Your AI Project

Module 5: Understanding AI Implementation

Identifying and Implementing AI and Data Projects
Establishing Measurable Project Goals
Workbook Exercise: Defining Project Parameters

Module 6: Finding and Using the Right People

Qualifications and Team Composition for AI Projects
Securing Organizational Buy-In
Workbook Exercise: Staffing for the Target Project

Module 7: Scalability (The AI Flywheel)

Planning and Scaling AI Projects
Framework for Measuring Success
Workbook Exercise: Production Details and Success Metrics

Ideal for business leaders and professionals who will be defining, driving, and influencing AI at strategic level in their businesses.

Key Takeaways:

Organizational AI Typology: Participants will understand their organization's position in the AI landscape and learn to select AI projects that align with their business's unique priorities.
Data Management Proficiency: They will learn to evaluate and prepare their organization's data for AI readiness, focusing on integration, structuring, ownership, and adequacy.
Project Prioritization: The course equips learners with the skills to prioritize AI projects based on organizational type and business goals.
Ethical AI Frameworks: Participants will gain insights into creating AI solutions that are sustainable, responsible, and ethically aligned with societal values and regulatory frameworks.
Implementation Strategies: Learners will understand the intricacies of AI project implementation, from setting goals to defining project scopes and success parameters.
Building Effective Teams: They will learn how to identify the right talent and secure buy-in from key stakeholders, ensuring a competent team is in place for successful AI project execution.
Scalability Mastery: Finally, the course will teach participants how to scale AI projects effectively, from prototyping to production, including establishing metrics for success.