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Professional Certificate in Business Excellence using Artificial Intelligence

Duration: 6 Months / 24 Weeks / 110 hrs

Industry Acceptance

Professional Certificate in Business Excellence with artificial intelligence (AI) is a combines the principles of business excellence with the power of AI to achieve outstanding results. It involves using AI to optimize processes, improve decision-making, and gain a competitive edge. By using AI to optimize processes, improve decision-making, and gain a competitive edge, you can achieve your business goals and reach new heights of success.

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Registration Fees

₹ 1500

non-refundable

Course Fees

₹ 85,000

Flexible EMI options available.

Batch Start

Aug, 2025

Duration

6 Months / 24 Weeks / 110 hrs

Program Structure

Eligibility: Undergoing a Diploma / Graduate in any discipline or equivalent In Mathematics

Duration: 6 Months / 24 Weeks / 110 hrs

Batch Start Date: Aug, 2025

Batch Size: 25-30

Courses and Certification Conduct Policy:

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Program Structure:

Module 1: AI and ML OVERVIEW
Course Content +
  • Explain key reasons behind the rise of AI
  • Identify the uses and application of different AI methods for an organisation
  • Explain the use of AI in managerial functions
  • Describe key AI methods
  • Explain the uses of different AI methods for different sectors and functions
  • Outline a plan to use specific AI methods for improving business performance
  • Explain the benefits of different AI technologies for different sectors and functions
Module 2: SETTING UP STAGE FOR AI
Course Content +
  • Compare and contrast different AI methods based on accuracy and complexity.
  • Describe the concept of augmented intelligence explaining how AI can aid humans in making better decisions.
  • Recognise the limitations of AI.
Module 3:BUSINESS TRANSFORMATION
Course Content +
  • Recognise the ways to harness AI to benefit businesses and enable disruption.
  • Outline the framework to develop strategies using AI that enable companies disrupt market and industry.
  • Use 'Business Models and Technology Matrix' to access the stage which a company or a brand is in the Business Transformation Journey.
Module 4: ADDRESSING IMPLEMENTATION CHALLENGES
Course Content +
  • Evaluate the preparedness of a company for AI adoption and deployment
  • Outline the framework for Monetisation of data and AI
  • Explain the assets an capabilities of an AI led company
  • Apply the learning of data and AI led disruptive innovation to a Fintech context
Module 5: BUSINESS MODELS FOR AI-LED BUSINESS
Course Content +
  • Describe the framework for integrating value creation and value appropriation in Business Innovation.
  • Outline the 'Business Innovation and Organisational Transformation' framework for a company or brand.
  • Apply the 'Business Innovation and Organisational Transformation' framework to transform a company.
  • Describe data driven Business Innovations.
  • Apply the learning of data driven Business Innovation to enhance performance of your company.
  • Evaluate the use of AI and analytics in driving Market Value for a company.
Module 6: LEADERSHIP IN THE AGE OF AI
Course Content +
  • Explain how Digital Capabilities such as AI are transforming Busiess Modules in the digital age.
  • Describe the opportunities for Humans Augmenting Machines, Machines Augmenting
  • Humans and Reimagining Business Processes.
  • Describe AI Value Creation by business problem and function.
  • Explain the AI-enabled Business Model for transformation.
  • Describe how to design and implement AI-enabled change in the organisation.
  • Descuss the Leadership Issues around the future evolution of AI.
  • Explain how to change mindsets and culture for enabling Digital and AI-enabled Change, e.g., Microsoft.
  • Identify consciousness and Ethical Considerations in the future of AI.
  • Outline a plan to transform a business at different levels of AI-enabled transformational leadership.
  • Recognise the ethics, privacy, ergulations in AI and ML.
Module 7: Recognise the ethics, privacy, ergulations in AI and ML.
Course Content +
  • Leadership in times of disruption
  • Attitude vs Aptitude
  • Leadership Charisma and Executive Presence
  • 5 Levels of Leadership
  • Why should anyone be led by you?
  • Influencing skills, why, how, Beyond Hierarchies
  • Politics, Managing Upwards
  • Storytelling
  • Creating Diverse Teams
  • Building Psychological Safety
  • Leadership: Taking Ownership
  • The First 100 Days
  • Crafting Culture
Module 8: LEADING CHALLENGES: CRISIS MANAGEMENT, SOCIETIES, THROUGH ESG CHALLENGE
Course Content +
  • Crisis Management Skills and BCP, Simulations
  • Ten Rules of Crisis Management with Examples
  • Ethics and the purpose of Business
  • Crafting a Tool Kit to help us navigate the Tradeoffs
  • The Emerging ESG Challenge
  • Environment and Sustainability. Strategies and Leadership
Module 9: INTRODUCTION TO GENERATIVE AI
Course Content +
  • Introduction to Generative AI
  • History and evolution of AI: Why Deep Learning has taken off in recent years
  • History of Neural Natural Language Processing
  • Structure of Artificial Neural Networks
  • Steps in Training an Artificial Neural Network; Parameters and Hyperparameters
  • Types of Neural Networks - Multi Layer Perceptrons, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Memory Networks (LSTM)
  • Limitations of LSTM and RNN in modeling long range sequences
  • Types of Generative AI Models - Generative Adversarial Networks (GANs); Variational Autoencoders; Transformers
  • Transformers and Attention mechanism in detail
  • Generative AI and Large Language Models (Language Models and Foundation Models; Development of Neural Natural Language Processing)
  • Two most popular Generative AI chatbots - BARD and ChatGPT - LLMs that Power these Chatbots - Architecture; Training Data
  • Tutorial codes on CNN, RNN and VAEs using TensorFlow
Module 10: GENERATIVE AI MODELS
Course Content +
  • Generative vs Discriminative models
  • Generative AI Architecture
  • Transforming text to numerical (vector) representations - Word Embeddings
  • Applications of Word Embeddings; Word Embedding Models
  • Training an LLM
  • Evaluation metrics - Tasks: Classification, Summarisation, Question Answering, Text Generation; Corresponding Metrics - Micro F1; ROUGE-L; Exact Match; BLEU, ROUGE-L
  • Pretraining and Transfer Learning
  • Architecture and Training Process of GANs
  • Tutorial codes on GAN using TensorFlow (Image Generation)
Module 11: WORKING WITH GENERATIVE AI
Course Content +
  • Prompt Engineering - Zero Shot vs Few Shot Learning
  • Limitations of LLMs: How to overcome with Retrieval Augmented Generation (RAG) using LangChain; Fine-tuning
  • Generative AI Creativity Tools - Generative AI tools for art and image creation; creative writing; DALL·E 3; Stable Diffusion (text-to-image model)
  • Generative and Discriminative Models integration
  • Ethical considerations and Risks of Generative AI - Misinformation, proliferation of bias in training data (social bias, gender bias), environmental concerns in training, security concerns
  • Tutorial codes on prompt engineering
Module 12: DOMAIN SPECIFIC - LEADERSHIP
Course Content +
  • Current landscape of Generative AI; Framework for integration of LLMs with Proprietary Data (company-specific) using LangChain; Vector databases
  • How managers and corporates can increase productivity with Generative AI
  • Applications of Generative AI in various industries with special focus on Financial Services
  • Generative AI applications in Finance - Case studies (Q&A on Financial Disclosures and Fundamental Analysis using LLMs on 10Ks); Creating domain-specific Chatbots using LangChain
  • Content Creation - Text Generation (News article generation given a headline; Summarisation of RBI speeches)
  • Sentiment Analysis with few-shot prompting
  • Video Creation

Projects

Project 1

Predict customer churn using ensemble prediction model

Analyze customer data to predict churn and develop retention strategies.

Project 2

Cluster vehicles by fuel attributes and train regression model

Classify vehicle types using features from silhouettes and other attributes.

Project 3

Automate traffic density detection using computer vision

Develop bounding box detection to assist traffic management systems.

Project 4

Street view house number classification

Use neural networks to recognize multi-digit numbers from street-level images.

Project 5

Predict patient condition and perform targeted marketing

Predict health outcomes and build marketing models using supervised learning.

Project 6

Build a recommendation system

Use collaborative filtering and popularity models to recommend mobile phones.

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