Artificial Intelligence (AI) for Management

By Prof. Saji K Mathew | IIT Madras

Learners enrolled: 343

ABOUT THE COURSE:

Background

Artificial Intelligence (AI) agents co-existing with human agents generate unique opportunities and challenges for organizations. Recent architectural and algorithmic innovations have empowered AI with capabilities that exceed human capability in several areas. AI’s learning algorithms, trained with data selected by humans also reflect human bias. At the cusp of this transformation which affect individuals, organizations and societies, this course focuses on harnessing value for organizations from AI systems. While the course informs practicing managers on the business value of AI, it also helps them recognize potential biases and human- AI co-existence conflicts. The course also provides a view of emerging AI regulation in select geographies.

Learning outcomes

To recognize how AI as a technology can drive organizational goals
To analyze AI applications in select business functions and domains
To recognize AI risks from an organizational perspective
To develop organizational policy for adopting AI in select domains

INTENDED AUDIENCE: Students seeking applications of AI in business and management

PREREQUISITES: An under graduate having done courses on applied statistics, MIS, and programming

INDUSTRY SUPPORT: AI and innovation wing of all industry sectors, Analytics and data science industry, IT services industry, Manufacturing and services operations and marketing

Summary

Course Status :	Upcoming
Course Type :	Elective
Language for course content :	English
Duration :	12 weeks
Category :	Computer Science and Engineering
Credit Points :	3
Level :	Postgraduate
Start Date :	19 Jan 2026
End Date :	10 Apr 2026
Enrollment Ends :	26 Jan 2026
Exam Registration Ends :	13 Feb 2026
Exam Date :	17 Apr 2026 IST
NCrF Level	4.5 — 8.0

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1-2:

Foundations: The artificial neuron, neural networks, training, gradient descent to

deep nets, language and vision models, narrow, and general intelligence,

generative AI

Week 3-4:

AI, strategy and business models: Digital business strategy, enterprise to eco

systems, digital platforms, competition and smart connected products, business

models in AI

Week 5-6:

Business value of AI: AI-business value mechanism, value creation, AI for

business functions-Marketing, Operations, and HR; and business domains-

Manufacturing, and Healthcare

Week 7-8:

Algorithmic decision making: Machines for decisions, learning algorithms-decision support to decision-making, conversational agents and anthropomorphism, social structure, demographic disparity and prejudice, the spectrum of cognitive biases, social media and the echo chamber effects, the changing role of general managers; interpretability, explainability and decision stakes, accuracy vs interpretability, solutions and limitations

Week 9-10:

AI Risks: AI risk and sources, AI bias, types of bias, hallucination and jailbreaking, business-AI alignment; risk management-technological solutions: unlearning and forgetting, robustness checks, debiasing, data sharing and differential privacy

Week 11-12:

Responsible AI and regulation: Fairness and its categories, fair equality of opportunities, philosophy of policy, fairness and policy, model selection for fairness, Governance, and regulation, human values vs market-oriented regulation, AI innovation-regulation trade off, emerging regulations and compliance in select geographies

Books and references

Canals, J., & Heukamp, F. (Eds.) (2020). The future of management in an AI world, London: Palgrave Macmillan.
Van der Linden, S. (2023). Foolproof: Why misinformation infects our minds and how to build immunity. WW Norton & Company.
Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT press.
Stanford Business (2017). Deep technology applications in developing economies: Three vignettes. E-641.
Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management science, 65(7), 2966-2981.
Coeckelbergh, M. (2020). Artificial intelligence, responsibility attribution, and a relational justification of explainability. Science and engineering ethics, 26(4), 2051-2068.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.
Els, A. S. (2017). Artificial intelligence as a digital privacy protector. Harv. JL & Tech., 31, 217

Instructor bio

Prof. Saji K Mathew

IIT Madras

Prof. Saji K Mathew is currently a Professor at the Department of Management Studies, Indian Institute of Technology Madras, India. As a Fulbright Scholar, he did his post-doctoral research on offshore IT outsourcing at the Goizueta Business School of Emory University, Atlanta (USA). His current research focuses on behavioral cyber security, information privacy, misinformation and digital nudging. He has published research in leading IS journals while also making editorial contributions to some of them. He is a founding member of the Association for Information Systems India Chapter (INAIS) and the Accessibility Research Centre (the ARC) at IIT Madras.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.

The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).

Date and Time of Exams: April 17, 2026 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.

Registration url: Announcements will be made when the registration form is open for registrations.

The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.

Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

Please note that assignments encompass all types (including quizzes, programming tasks, and essay submissions) available in the specific week.

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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