Programs Calendar FY 26-27 >>
Programs Calendar FY 26-27 >>

Artificial Intelligence for Business Managers

For mid-level managers with 3–9 years of professional experience who are likely to interact with or oversee AI-driven applications in the near future. Vertical-agnostic participants from any industry or functional background who wish to build AI literacy for business decision-making
  • Deep conceptual clarity on the AI methods introduced in the curriculum
  • A practitioner’s intuition for interpreting and leveraging AI-driven insights
  • Applied proficiency in working with datasets, experimenting with techniques, and building models
  • Strategic acumen to evaluate business problems and deploy the most appropriate AI approaches

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Dates
Start Date: Saturday, 12 Sep 2026
Register By: 25 Aug 2026
Early Bird Deadline: 15 Aug 2026
Special Offer Deadline: 12 June 2026

Program Fee
Regular: Rs. 45,000 + GST
Early Bird Fee: Rs. 40,500 + GST
Special Offer Fee: Rs. 36,000 + GST

Duration
Live-Online, 12 Weeks
2 sessions /week
Sat/Sun: 7 pm to 8:15 pm IST

Click below to “Enroll and Pay” for the program – Enroll and Pay

Program Overview

Artificial intelligence (AI) is an interdisciplinary field of study that tries to mimic or recreate human intelligence in machines. Previously, the term “artificial intelligence” was used to describe machines that mimic and display “human” cognitive skills that are associated with the human mind, such as “cognition”, “thinking”, “learning” and “problem-solving”. However, for lack of flexibility, this view has been modified by major AI researchers. Thus, AI is now described in terms of rationality and acting rationally. This flexibility does not limit how intelligence can be articulated. One such definition by Stuart Russell and Peter Norvig, defines AI as the field that studies intelligent agents, where an “intelligent agent” refers to any system that perceives its environment and takes actions that maximise its chance of achieving its goals.

AI applications, having emerged out of the laboratory, have started diffusing not just across businesses but also in normal day-to-day life. These include advanced web search engines (e.g., Google), recommendation systems (YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). Business surveys regularly rank AI as one of the most interesting and fastest-growing fields, and it is already generating yearly revenues of over a trillion dollars. One widely respected AI expert Kai-Fu Lee predicts that AI’s impact will be “more than anything in the history of mankind.”

Considering the transformative impact that AI is poised to have across all sectors, it is increasingly essential for managers to understand AI from a business perspective. Such knowledge enables them not only to apply AI solutions within their functional domains but also to identify new opportunities for solving business challenges using AI-driven tools and techniques.

The course “Artificial Intelligence for Business” has been thoughtfully designed with a balanced blend of concepts and hands-on practice. Participants will gain a solid understanding of contemporary AI techniques and the confidence to apply these tools effectively in real-world business contexts.

AI adoption has increased dramatically in the last few years as shown in the graph below –

Here are industry statistics (global reports from McKinsey, IBM, PwC, and Deloitte) that make it imperative or business managers to upskill themselves on various aspects of AI:

Adoption & Demand

  • 85% of global executives believe AI will significantly change how their organisations operate in the next 3–5 years (McKinsey, 2024).
  • 66% of companies have increased their training budgets specifically for AI-related upskilling.
  • 75% of executives say AI skills are now critical for managerial and leadership roles (Deloitte Human Capital Trends).

Skills & Capability Gap

  • Only 1 in 4 managers feel confident in their ability to use AI tools to support decision-making.
  • 82% of organisations report a shortage of AI-ready business managers who can translate AI capabilities into business value (IBM Global AI Index).

INCENTIVE FOR EARLY SIGN ON!

A special 20% discount on the program fee applies to all enrollments made on or before 12 June 2026. Participants have the option to pay the program fee in two easy installments.

Program Objectives

  • To provide a theoretical and practical understanding of the broad approaches in Artificial Intelligence, inclusive of Classical AI, Machine Learning (Supervised, Unsupervised and Reinforcement Learning), Deep Learning & Foundation Models (LLMs) and AI-driven applications like recommender systems, web search, disease identification, etc.
  • To provide a critical understanding of how an AI-driven business can transcend traditional levers of competitive advantage, such as economies of scale and scope, while delivering high-quality services at an unprecedented scale.
  • To help participants develop an intuitive understanding of the applicability of AI tools and techniques, this approach provides a business decision-making context for each of the discussed methods.

Pedagogy

  • Lectures
  • Hands-on exercises using Python
  • Home assignments – readings
  • Case studies

Who is it for?

• Mid-level managers with 3–9 years of professional experience
• Professionals who currently have limited exposure to Artificial Intelligence
• Managers who are likely to interact with or oversee AI-driven applications in the near future
• Vertical-agnostic participants from any industry or functional background who wish to build AI literacy for business decision-making

Program Content

Total Sessions: 24; Theory Sessions: 18; Hands-On Sessions: 6; Session Duration: 75 minutes each)
Note: The instructor will prescribe multiple readings and case studies during the duration of the course.

Module 1: Introduction to Artificial Intelligence

  • What is Artificial Intelligence?
    Understanding AI as a field, scope, and key capabilities.

  • How is Intelligence Defined?
    Human vs. machine intelligence; symbolic vs. statistical intelligence.

  • A Brief History of AI
    From early symbolic systems to today’s foundation models.

  • Initial Successes and Applications
    The rise of expert systems and rule-based decision-making.

  • AI Winters
    Lessons from periods of reduced funding and interest.

  • State of the Art
    Deep learning, LLMs, multimodal models, autonomous systems.

  • Risks and Benefits of AI
    Productivity gains vs. privacy, misuse, and safety concerns.

Module 2: The AI Ecosystem

  • How AI Has Transformed Business
    Automation, prediction, personalization, operational excellence.

  • How AI Applications Are Designed
    Data → Features → Algorithms → Deployment → Monitoring.

Module 3: Classical AI Techniques in Problem-Solving

  • Search Techniques
    Uninformed and informed search, heuristics.

  • Constraint Satisfaction Problems (CSPs)
    Scheduling, planning, optimization.

  • Case Study: A Novel Movement Planner System for Dispatching Trains
    Demonstrating search and constraint-based planning in transportation.

Module 4: Case Study: "A novel movement planner system for dispatching trains."

Module 5: Knowledge, Reasoning, and Planning

  • Logic-based reasoning

  • Rule-based systems

  • Planning algorithms and real-world decision frameworks

Module 6: Machine Learning – Supervised Learning

  • Simple & Multiple Linear Regression
    Predictive modelling fundamentals.

  • Decision Trees
    Classification, regression, interpretability.

  • Logistic Regression
    Probability-based classification.

  • Support Vector Machines (SVMs)
    Margin-based learning for complex boundaries.

  • Case Study: Vungle Inc. Improves Monetization Using Big Data Analytics
    How data-driven models enhance digital advertising outcomes.

Module 7: Machine Learning – Unsupervised Learning

  • Concepts of Unsupervised Learning
    Discovering hidden patterns without labels.

  • Hierarchical Clustering

  • K-Means Clustering

  • DBSCAN Algorithm
    Density-based clustering for complex data distributions.

Module 8: Case Study: Vungle Inc. Improves Monetization Using Big Data Analytics

Module 9: Machine Learning – Reinforcement Learning

  • Introduction to RL
    Agents, environment, rewards.

  • Modelling a Checkers Game
    Exploring value functions and policies.

  • Applications
    Robotics, operations research, finance, gaming.

Module 10: Deep Learning & Large Language Models

  • Deep Neural Networks
    Architecture, training, representation learning.

  • CNNs & GANs
    Vision systems, generative models.

  • Foundation Models
    Multimodal, pretrained, adaptable to enterprise use cases.

  • LLMs – ChatGPT, Gemini & Others
    Capabilities, prompt engineering, enterprise applications.

Module 11: Real-World Applications of AI

  • Recommender Systems
    Personalization in e-commerce and media.

  • Self-Driving Cars
    Perception, planning, and control systems.

  • Web Search
    Ranking, relevance, and query understanding.

  • Facial Recognition, NLP, etc.
    Biometrics, document intelligence, conversational agents.

Module 12: Explainable and Ethical AI

  • Bias, Inequity, and Unfairness in AI-Driven Decisions

  • Notions of Fairness
    Group fairness, individual fairness, calibration.

  • Techniques to Mitigate Bias & Unfairness
    Pre-processing, in-processing, post-processing solutions.

  • Real-World Examples
    Lending, hiring, healthcare diagnostics.

Module 13: Issues in AI Governance

  • Need for AI Governance
    Responsible deployment, accountability.

  • AI Regulation and Law
    Global perspectives and sector-specific guidelines.

  • EU AI Act
    Risk-based framework and implications for businesses.

  • Future Skills for Managers
    Data literacy, AI oversight, strategic implementation.

Click below to “Enroll and Pay” for the program –

Enroll and Pay

Key Takeaways

By the end of the course, every committed participant will have developed:

  1. Clear conceptual understanding of the specific AI techniques covered in the program.

  2. Intuitive grasp of the underlying thinking and logic that power modern AI approaches.

  3. Hands-on proficiency in applying AI techniques to explore datasets, build models, and interpret results.

  4. Practical business acumen to identify opportunities and apply the right AI methods to real-world business problems.

Faculty Profile

Rajesh V Natarajan

Dr. Rajesh Natarajan V., Associate Professor – Data Science & Information Systems at IFMR GSB, Krea University, is a seasoned Data Science and Business Analytics professional with 20+ years of experience across Data Science, Data Mining, AI, and Machine Learning.

He brings rich and diverse industry expertise, having worked in roles spanning analytics and data science project delivery, product and platform development, data science consulting and research, establishing Business Analytics Centres of Excellence, and driving pre-sales and business development initiatives.

Dr. Natarajan has designed and deployed analytical solutions for global clients across a wide spectrum of industries, including Banking and Financial Services, Insurance, Retail, Healthcare, Railroads, Hospitality, and Manufacturing. He has held key positions in leading IT and technology organizations such as Cognizant Technology Solutions, Hexaware, Wipro, GE Transportation, and Tech Mahindra.

In his prior role with the Tamil Nadu e-Governance Agency, IT Department, Government of Tamil Nadu, he contributed to the design and implementation of advanced analytical solutions and data-driven policies, strengthening the state’s digital and governance capabilities.

Dr. Rajesh Natarajan combines strong academic credentials with deep industry experience, offering participants a unique blend of conceptual rigor and practical, real-world insight. In academia, Rajesh worked as an Assistant Professor with the IT & Systems Group of the Indian Institute of Management, Lucknow (IIML) and as a Visiting Associate Professor, Industrial Engineering Group, Mechanical Engineering Area of Universidad Carlos III de Madrid (UC3M), in Madrid, Spain. Rajesh has been an active researcher and has contributed 20+ articles to leading international technical conferences (IEEE ICDM, IEEE IRI, etc.)  and refereed journals.

Rajesh holds a Bachelor’s degree in Electronics Engineering (B.E. (Honours)) from the University of Bombay (Mumbai) and has completed the Fellow Program in Management (PhD in Management) from the Indian Institute of Management Bangalore (IIMB).

Certificate

AI for Business Managers - Certificate Program

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Program Schedule

Schedule: Two sessions of 75 minutes each every week on Sat and Sun between 7 pm and 8:15 pm.

The program starts on Sat, 12 Sep 2026 and would run for 12 weeks.  The program has a total of 24 sessions.

Delivery Mode: Live-Online through Zoom

Application and Enrollment Flow

To enroll and apply for the program, click on the button below –

Enroll and Pay

The payment and enrollment happen through our enrollment and learning management website – https://lms.labinmotion.com . We offer easy installment plans for all our programs. This can help plan your cash flows. We offer an additional 5% discount on group registrations for 3 or more people from a single organisation. Please email support@labinmotion.com for group registrations, and we will guide you through the enrollment process.

Labinmotion application flow

Contact For Queries

Parthasarathy S
Ph. +91 9110614400
Email: partha@labinmotion.com

 

Connecting the Dots

AI creates impact when supported by strategic leadership. The program on Strategic Management, Leadership and Emotional Intelligence  helps you assess AI opportunities and influence adoption.

To ensure AI initiatives drive real value, the program on Customer Centricity: Driving Growth Through Customer Value  aligns insights with customer needs and growth.

Successful AI adoption also depends on teams embracing change. The program on Creating and Leading High-Performing Teams supports collaboration and capability building in AI-driven environments.