Artificial Intelligence for Managers

Learning Outcome
Students should be able to understand key aspects of Artificial Intelligence relating to various businesses, and the role of Artificial Intelligence in making businesses succeed.

1.1 Difference between technology and Artificial Intelligence - Examples
1.2 Computer Vision, Image Processing, Generative AI with applications.
1.3 The vision for businesses of the future - AI first businesses.
1.4. Key terminology of AI.

Practical Component
Practical Demonstrations on zones of practical use cases, and ways they've been applied.

Learning Outcome
Students should be able to understand the end to end planning, and execution of an Artificial Intelligence transformation, and management strategy.

1.5 The readiness factor.
1.6 Pyramid method of effective adoption, and development.
1.7 Choosing between Build, Purchase, or External Development
1.8 Acceptance Criteria, and Assessment Team.

Practical Component
Practical Demonstrations on zones of practical use cases, and ways they've been applied

Learning Outcome
Students should be able to understand and identify whether a system is an AI system through the Dataviv Strategy, and uncover the underlying aspects of the system. They will also be able to reason about why Artificial Intelligence was needed in this situation, and how is it better than other primitive approaches.

1.9 Key Components of An Artificial Intelligence
1.10 Contrasting the same with Coding, RPA, and other software.
1.11 The Advantage of Artificial Intelligence over others.

Practical Componenet
Practical Demonstrations on zones coding related performance, and AI related performance and key differences.

Learning Outcome
Students should be able to understand why everyone says Garbage in and Garbage Out, but also understand that solely data is not the key to intelligence, the right intelligence capture is quintessential as well. They will learn hands on how to prepare data for various capture formats, and be adept at basis data preparation decision making.

1.12 Is my data collection as per requirements.
1.13 Team and Adoption readiness.
1.14 Data Security, and protocols.
1.15 Success Criteria

Practical Component
Practical Demonstrations on zones of practical use cases, and ways they've been applied.