Offered by Shailesh J. Mehta School of Management, IIT Bombay

This opening module orients leaders to the technical and organisational building blocks needed to unlock AI value at scale. Learners are grounded in the ACID (AI, Cloud, IoT, Data) technology landscape and modern data capabilities, then connect these foundations to practical AI/ML applications and the economics of adoption. By the end, learners can frame business-relevant use cases and assess organisational readiness for AI adoption. Topics Covered: • Introduction to ACID (AI, Cloud, IoT, Data) technologies with focus on AI • Data modernisation and data intelligence for business • AI, Machine Learning and Deep Learning for business • AI economics and maturity models

This module moves from principles to practice with GenAI. Learners will discuss how foundation models are adapted to enterprise contexts, how to design prompts and evaluation loops at scale, and what it takes to operationalise GenAI safely. We also introduce agentic systems and their implications for productivity, control, and risk. Topics Covered: • Foundations of Generative AI • Prompt engineering at scale • Implementing Generative AI in your organisation • Agentic AI and its implications for businesses

In this module, learners get an opportunity to translate technology into competitive advantage.This module equips them with an AI-era strategy toolkit with an understanding of various AI-led business models, strategic positioning and opportunities for differentiation, and then links it to building rapid proof of concepts (PoCs), value hypotheses, and evidence-based scale-up. Learners leave with a build-vs-buy playbook and integration patterns that reduce time-to-value while managing risk. Topics Covered: • AI-first digital transformation • Business models in the AI age • AI-era strategic positioning & dierentiation • Prototyping & rapid proof of concept, scoping and success gates • Integration patterns and scale; build-vs-buy playbook

This module focuses on execution: how to assemble cross-functional teams, govern work, and drive change so that AI initiatives stick. It emphasises operating rhythm, stakeholder alignment, and adoption tactics that turn pilots into lasting capabilities, while keeping human oversight and accountability central to delivery. Topics Covered: • Building & leading cross-functional AI teams • Agile delivery & change management for AI

In the final module, learners choose an industry specific track to apply the core toolkit to real constraints, data realities, regulations, and measures of value within their respective industries. Learners go through industry specific use cases , artefacts, and a final capstone project to build an implementation-ready playbook customised as per their specific industry requirements. Topics Covered: • Learners can choose from the following 2 tracks Track A: For professional from Banking, Financial Services, Insurance (BFSI), Fintech and similar backgrounds Track B: For professionals from Information Technology (IT), Computer Science, Technology, AI / ML and Data Science and similar backgrounds • Sector-specific topics and examples on: ◦ Data & analytics ◦ Use cases of AI/ML for specific business challenges ◦ Regulations & responsible AI ◦ Measuring performance and ROI for AI-led transformations