Fractal Analytics
Leadership Strategies for AI and Generative AI Specialization
Fractal Analytics

Leadership Strategies for AI and Generative AI Specialization

AI Adoption Strategies for Business Leaders. A CEO' Perspective for AI Adoption and Generative AI Implementation

Sray Agarwal
Rasesh Shah
Fractal Analytics Academy

Instructors: Sray Agarwal

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4.4

(38 reviews)

Beginner level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.4

(38 reviews)

Beginner level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Evaluate potential applications of generative AI across diverse business domains.

  • Integrate generative AI into business operations effectively, considering data privacy and ethical implications.

  • Establish KPIs, interpret data-driven insights, and devise optimization strategies for generative AI initiatives.

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Taught in English
41 practice exercises

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  • Develop a deep understanding of key concepts
  • Earn a career certificate from Fractal Analytics

Specialization - 4 course series

What you'll learn

  • Definition and distinguishing features of generative AI

  • Core concepts of GANs and VAEs

  • Role of data and training in generative models

  • Strategic framework for integrating generative AI into business

Skills you'll gain

Responsible AI, Key Performance Indicators (KPIs), Generative Model Architectures, Strategic Leadership, AI Product Strategy, Governance, Business Strategy, Customer experience improvement, Business Marketing, Customer Engagement, Corporate Strategy, Generative AI, Data Ethics, and Business Leadership

What you'll learn

  • Explain key components for driving desired outcomes from AI

  • Explain strategies for error reduction and accuracy improvement in AI

  • Gain insights into optimizing organizational effectiveness in AI adoption

Skills you'll gain

User Centered Design, Data Quality, Design Thinking, Artificial Intelligence, Responsible AI, Data Pipelines, Ethical Standards And Conduct, Deep Learning, Generative AI, Strategic Decision-Making, Business Strategy, Human Centered Design, Data Ethics, Organizational Strategy, Organizational Development, Technology Strategies, Governance, Data Strategy, and Business Leadership

What you'll learn

  • Discuss responsible AI principles and their significance in technology, including ethical considerations, fairness, transparency, and accountability.

  • Apply techniques to identify, address, and mitigate bias in AI algorithms and data, promoting fairness and inclusivity in AI systems.

  • Interpret and explain AI decisions, balancing accuracy and explainability to foster trust and accountability in AI systems.

  • Discuss accountability, ethical AI governance, privacy considerations, security measures in the development & deployment of responsible AI systems.

Skills you'll gain

Responsible AI, Data Governance, Information Privacy, Data Ethics, Data Security, Governance, Mitigation, Accountability, Complex Problem Solving, Personally Identifiable Information, Ethical Standards And Conduct, Artificial Intelligence, and Critical Thinking

What you'll learn

  • Explain the lifecycle of DS project and role of structured thinking in DS project.

  • Define a problem statement using the SMART framework. Explain the activities, best practices, and pitfalls in the implementation phase.

  • Construct MECE issue tree to break-down business problems into parts. Create a problem statement worksheet to scope business problems.

  • Explain the role of human centered design during solutioning of business problems.

Skills you'll gain

Problem Solving, Data Science, Human Centered Design, Critical Thinking, Complex Problem Solving, Empathy, Decision Making, Creative Problem-Solving, Data Analysis, and Analytical Skills

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Instructors

Sray Agarwal
Fractal Analytics
1 Course1,504 learners

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