Applying Generative AI for Decision Makers and Architects

At Course Completion

Working in an interactive learning environment, led by our engaging AI expert, you will:

· Gain a solid understanding of Generative AI, its evolution, and its role in shaping innovative business solutions, positioning you to effectively drive AI initiatives in your organization.

· Acquire the ability to identify potential Generative AI use cases in your industry and evaluate their feasibility and value, empowering you to make informed decisions that can enhance business operations.

· Learn how to manage the lifecycle of an AI project efficiently, equipping you with the knowledge to guide projects from inception to successful deployment, with continuous monitoring and improvement.

· Develop an understanding of AI and data security best practices, along with the crucial role of data in Generative AI, fostering your ability to safeguard your AI systems effectively.

· Stay ahead of the curve by gaining insights into the upcoming trends and technologies in Generative AI, enabling you to maintain a competitive edge in your organization in this rapidly evolving field.

Audience Profile

This introductory-level course is ideally suited for business professionals, decision makers, and architects who are eager to harness the power of Generative AI within their organization. Attendees roles would include Executives, Project Managers, Business Analysts, IT Leaders, Architects or anyone involved in strategic decision making or team building for AI projects.

Prerequisites

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

A basic understanding of business operations and decision-making processes: This will aid in understanding the potential applications of Generative AI in solving business problems and improving efficiency.
Familiarity with the principles of project management: As the course includes a component on managing the lifecycle of an AI project, having foundational knowledge in project management will be beneficial.
Basic knowledge of data management: This can help in grasping the role of data in Generative AI, and the best practices for data security.

Next Steps / Follow-on Courses: We offer a wide variety of follow-on courses and learning paths for Generative AI, AI for Business, GPT, Applied AI, Azure OpenAI, Google BARD, AI for developers, testers, data analytics, machine learning, deep learning, programming, intelligent automation and many other related topics. Please see our catalog for the current AI & Machine Learning Courses, Learning Journeys & Skills Roadmaps, list courses and programs.

Outline

Please note that this list of topics is based on our standard course offering, evolved from current industry uses and trends. We'll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

Introduction to AI in Business

Impact of AI technologies in modern businesses
What is AI and Why Does It Matter
The ROI of AI Adoption
Quick View: AI Ethics and Regulation
AI's Role in Different Business Units
Quick View: Backbone of an AI-Driven Business
Real-World AI Transformations

Understanding Generative AI: Its Evolution and Impacts

Overview of Generative AI and its evolution
Generative AI Defined - What is it, how it works
Impacts and opportunities of Generative AI in business
Real-world examples of successful Generative AI applications

Unpacking Use Cases: Where and How Can You Apply Generative AI

Identifying potential use cases in your industry
Practical examples of how Generative AI can solve business problems
Frameworks for evaluating the feasibility and value of Generative AI projects
Use Case: ROI and Value Analysis for Generative AI in Healthcare

Key Considerations in Implementing Generative AI

Understanding the technology stack behind Generative AI
The role of data in Generative AI: requirements, preparation, and management
How to choose the right AI model for your needs

Securing Your Generative AI

Understanding AI and data security
Best practices for securing Generative AI systems
Roles & Skills in Securing Generative AI
Responsible decision making: Role of privacy in Generative AI
Ethical and Legal Concerns in using Generative AI

Managing the Lifecycle of a Generative AI Project

Planning and managing an AI project
Testing and validating your AI system
Deploying, monitoring, and maintaining your Generative AI solution
Deep Dive Example: Guide to Applying GenAI in a Healthcare Scenario
Ensuring Scalability and Sustainability of Generative AI Systems in Healthcare
Problem Solving Strategies for Pitfalls and Challenges in AI Implementation

Evaluating the Success of Your Generative AI Projects

Key performance indicators (KPIs) for Generative AI projects
Tools and techniques for measuring the effectiveness and ROI of AI
Lessons learned and continuous improvement in AI

Building Your Generative AI Team: Who You Need

Explore the key roles and skills necessary for forming a generative AI team.
Understand the core team roles and required skill sets.
Discover the top ten considerations for assembling your team.
Navigate the decisions between training vs. hiring.
Foster effective team collaboration.
Establish team KPIs and performance metrics.
Manage virtual teams: Tips for leveraging technology for remote collaboration.
Managing an AI project: Best practices and common pitfalls

Future Trends in Generative AI

What's next for Generative AI: upcoming trends and technologies
Staying ahead: How to keep your organization competitive in the AI field
Q&A and course wrap-up