Artificial Intelligence for Senior Leaders (Batch-2)

Artificial Intelligence(AI) has become a decisive technology for the growth of every organization. Sophistication in AI is expected to be the main differentiator between high-performing companies and low-performing companies. The use of AI and its components such as statistical, machine and deep learning are expected to increase the stakeholder value and customer experience and satisfaction. The algorithmic aspects of AI are available readily through many sources; however, many companies still struggle with adapting AI to the organization. For example, companies struggle to find answers to the following questions :

Intuitive understanding of AI; Relationship between AI, ML, SL and DL; Converting a business problem into an analytics problem, analytics problem solving framework; Use cases of AI across different functional areas and different industries; Business process automation using AI.

Machine Learning: Supervised, Unsupervised and Reinforcement Learning Algorithms.

AIML Model Development: Feature Extraction; Feature Engineering; Feature Selection; Model Selection and Model Deployment.

Objectives of Descriptive Analytics: Storey telling using Data; Predictive Analytics Models: Regression, and Logistic Regression; Prescriptive Analytics: Linear Programming and Multi-Criteria Decision Making.

This short-duration program is aimed at the managers who are currently leading and are likely to lead AI initiatives in the organization. The course covers the aspects such as Organizational journey of AI transformation, data governance, data preparation for analytic model building and descriptive, predictive and prescriptive analytics. The objectives of the program are as follows:

Data governance strategy.

Technology and Platform Strategy

People and Skill Strategy

Learn to create a roadmap for AI-first company.

Understand key factors that can lead to success or failure of AI projects

Understand how to choose the right use cases and prioritize key AI project

Learn concepts and techniques in AI, such as statistical learning, machine learning, deep learning and Understand Generative AI and Large Language Models. Understand Generative Pre-Trained Transformer (GPT) and its business applications.

Learn tools and techniques of descriptive, predictive and prescriptive analytics

Understand the applications of supervised, unsupervised and reinforcement learning algorithms.

Understand what tasks can be automated using AI.

Understand data governance and data readiness for the application of AI.

Learn how an organization can build an AI team? Roles and responsibilities of AI team and hiring or training to build an AI team.

Learn about common mistakes while making AI transformation and how to avoid them

AI and society responsible AI

How AI can be used or what are the different use cases in different industries.