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Who should attend
The program is designed for leaders with at least 10 years of experience who are either working in the field of AI or planning to set up AI team
About the course
Artificial Intelligence has become a decisive technology for growth of every organization. Sophistication in AI is expected to be the main differentiator between high performing companies and low performing companies. Use of AI and its components such as statistical learning, machine learning and deep learning are expected to increase the stakeholder value and customer experience and satisfaction. Algorithmic aspects of AI is available readily through many sources, however, many companies still struggle with adapting AI to the organization. For example, companies struggle to find answers for questions such as: 1. What makes an AI company? 2. What should be the strategy for building an AI initiative within an organization, 3. How to build an AI team, and 4. What kind of problems can be solved using AI? 5. How to be AI-first company?
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 will cover aspects such as Organizational journey of AI transformation, data governance, data preparation for analytic model building, descriptive, predictive and prescriptive analytics. The objectives of the program are:
Understand how to create strategy for building AI initiative within the organization. Special focus will be on:
- 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 right use cases and prioritize key AI projects?
- Learn concepts and techniques in AI such as statistical learning, machine learning, deep learning and their applications with use cases from different sections of the industry.
- 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 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 common mistakes done while making AI transformation and how to avoid them
- AI and society / responsible AI
- How AI can be used or what are different use cases in different departments – Sales, Finance, HR, etc?
Introduction to Artificial Intelligence (AI), Machine Learning (ML), Statistical Learning (SL) and Deep Learning (DL):
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.
AI/ML Model Development: Feature Extraction; Feature Engineering; Feature Selection; Model Selection and Model Deployment.
Introduction to Descriptive, Predictive and Prescriptive Analytics:
Objectives of Descriptive Analytics: Storey telling using Data; Predictive Analytics Models: Regression, and Logistic Regression; Prescriptive Analytics: Linear Programming and Multi-Criteria Decision Making.
- Package Pricing at Mission Hospital
- Improving Sales Conversion at Eureka Forbes Using Machine Learning Algorithms.
Setting up an AI team:
Choosing the right team; roles and responsibilities; Organizational Structure: Centralized and Distributed Models; Key skill set; fresh hire vs internal training.
Analytics Technology Landscape:
Choosing the right tools and platforms for development and deployment of AI based solutions.
Data Governance Framework; Data Privacy, Security, Quality and Responsibility; General Data Protection Regulation (GDPR);
AI in sales and marketing: opportunity and sales conversion; channel optimization; customer lifetime value; AI in Operations: supply chain analytics; AI in Retail: Assortment planning, brand switching, promotion effectiveness; AI in Banking and Finance: Credit Rating, Fraud Detection.
The program would result in the following benefits:
- Understanding of AI and its components.
- Ability to develop an AI initiation strategy for the organization.
- Understand various AI techniques and its applications across different functional areas and sectors.
- Understanding of data governance and setting up AI team within the organizations.
- Learn framework of developing deployable solution using AI.
U Dinesh Kumar’s research interest includes Business Analytics and Big Data, Artificial Intelligence, Machine Learning, Deep Learning Algorithms, Stochastic models (Reinforcement Learning Algorithms), Reliability, Optimization, Six Sigma and Performance Based Logistics. He has published several r...
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