AI for Business Transformation

Emerging Technologies

About Programme

Why this program now

AI is no longer a future initiative reserved for specialist teams. It is becoming central to how enterprises transform business processes, improve decision quality, increase operational efficiency, redesign workflows, and build new growth opportunities.

Senior leaders today are not only expected to understand AI, but also to decide where it matters, what is feasible, what should be prioritised, and how business value should be measured.

Most organisations are no longer asking whether AI matters. The real challenge is deciding where to apply it, how to evaluate, govern responsibly, and how to turn experimentation into measurable business outcomes.

Why this program works

Many AI programs either stay at a broad awareness level or become implementation-centric too quickly. That leaves senior leaders with fragmented understanding and limited ability to prioritize AI initiatives, make informed decisions, and drive adoption across teams. This program is structured to close that gap by helping participants:

Understand how AI, ML, and Generative AI enable business transformation

Identify high-impact use cases aligned to enterprise priorities

Ask relevant questions across technology, analytics, and business functions

Identify capability gaps, adoption barriers, and the functional support required for AI execution

Connect cutting-edge AI frameworks, such as LLMs, retrieval workflows, and agentic systems, to organisational needs

Apply learning to a business-relevant capstone project

Evaluate AI initiatives & solutions for feasibility, governance, risk, and ROI

Programme Content

AI Strategy, Enterprise Adoption, and Modern AI Stack

Data-driven decision-making and AI overview

Enterprise AI landscape and AI-first mindset

Generative AI impact and live demos

LLM Q&A, RAG systems, and AI agent workflows

Industry use cases and adoption challenges

Supervised and unsupervised machine learning

ML vs GenAI vs agents and agentic AI workflows

Expectation gaps and practical considerations

Data Foundation to Achieve AI Solution Architecture

Enterprise data availability and quality challenges

Translating business problems into data problems

Solution considerations and pretrained vs custom models

ML, time series models, and LLMs for forecasting

Retrieval and AI agents for forecasting

Quality and defect data management

Yield and cost control through AI

AI-driven quality monitoring and early defect identification

Process stability, rework reduction, and downtime reduction

Data-Driven Model and AI-Enabled Transformation

Operational waste and inefficiencies identification

AI-driven workflow redesign

Lean operations and maintenance integration

Human-AI interaction and collaboration

Risk and reliability assessment

Operational impact measurement

Cost savings quantification

Practical use of OpenAI-led workflows

How LLMs Reshape AI Solutions, Workflows, and Advantage

Customer segmentation and churn modeling

Classical ML techniques and clustering with XGBoost

LLM-powered personalization

LangChain orchestration and AI-driven customer workflows

Probability and logit models

Classical ML outputs as signals

LLM-driven action prioritization

Document loading, chunking, and preprocessing

Vector search basics and building RAG workflows

Foundations of Agentic AI Systems

LLMs as reasoning engines

Tools vs assistants vs agents

Agentic system architecture

Goals, memory, planning, and actions

Feedback and workflow execution

Guardrails and human oversight

Failure modes and vector databases

Agent Orchestration and Tool-Integrated AI

How tool calling works in practice

Designing controlled tool inputs and outputs

Building multi-step agent workflows

Agent output checking and validation

AI Evaluation, Reliability, and ROI

Evaluating AI output for accuracy and reliability

Improving retrieval and prompting strategies

Error handling and fallback mechanisms

Safety and guardrails

Real-world AI solution design

Copilots and agentic systems

AI-enabled workflows

Impact and ROI validation

Risk assessment and feasibility review

Programme Audience

This program is designed for experienced professionals and business leaders who are responsible for performance, execution, and transformation in their organisations.

CXOs and Vice Presidents: Senior executives driving organizational strategy and AI-led transformation.

Founders and Business Leaders: Entrepreneurs and leaders shaping business direction and growth through AI adoption.

Business Unit Heads and Department Heads: Leaders responsible for performance, execution, and transformation within their units.

Senior Management and Decision Makers: Professionals evaluating, prioritising, and governing AI initiatives across the enterprise.

Profit and Loss Owners: Leaders accountable for business outcomes and measuring ROI from AI investments.

Senior Managers and Transformation Leaders: Driving change management and leading AI-powered business transformation programmes.

Consultants and Client Partners: Advising clients on AI strategy, feasibility, and enterprise-wide adoption.

Functional Leaders: Across operations, supply chain, procurement, quality, service, sales, marketing, customer success, and product development.

Enterprise AI landscape: from predictive analytics to Generative AI

Building an AI-ready organization and executive leadership mindset

Practical GenAI capabilities with live enterprise walkthroughs

Knowledge retrieval (RAG) and intelligent agent workflows in business

Overcoming organizational adoption hurdles: culture, change management, and governance

Understanding the continuum: Classical ML vs. Generative AI vs. Autonomous Agents

Avoiding common executive expectation gaps and pilot-to-production pitfalls

Enterprise data readiness, quality benchmarks, and governance standards

Translating strategic business priorities into measurable AI requirements

Strategic architecture: Pretrained commercial models vs. private custom models

AI-driven forecasting for demand planning, revenue projections, and inventory

Yield optimization, process stability, and cost reduction through AI

Automated defect identification, quality control, and downtime mitigation

Establishing reliable ground-truth datasets across departmental ERPs and CRMs

Identifying operational inefficiencies and high-friction process bottlenecks

Redesigning business workflows with intelligent AI assistance

Lean operations and predictive maintenance integration for asset reliability

Human-in-the-loop collaboration: empowering functional teams with AI tools

Operational risk assessment and reliability management

Quantifying productivity impact, cycle-time reduction, and cost savings

Practical deployment of modern enterprise workflow platforms

Customer retention modeling, churn reduction, and lifetime value optimization

Combining predictive analytics with Generative AI for personalized customer engagement

Enterprise document intelligence: extracting actionable insights from contracts, reports, and manuals

Enterprise knowledge retrieval (RAG): connecting LLMs securely to proprietary company data

Converting analytical data signals into prioritized executive decisions

Designing domain copilots to support cross-functional teams

Data privacy, intellectual property protection, and security safeguards for enterprise LLMs

The evolution from passive AI chatbots to goal-driven autonomous agents

Core architecture: goal planning, contextual memory, and autonomous task execution

Designing multi-agent systems for complex cross-functional business processes

Establishing enterprise guardrails, output validation, and executive oversight

Identifying system failure modes, drift, and ensuring operational predictability

Strategic use cases for autonomous agentic workflows across enterprise functions

How AI agents interact safely with enterprise software, APIs, and business databases

Designing controlled workflows with verified inputs, structured outputs, and audit logs

Multi-step process automation across departmental silos

Quality assurance, hallucination prevention, and accuracy verification protocols

Monitoring agent reliability and maintaining continuous operational stability

Evaluating AI output quality, reliability, and enterprise-grade accuracy

Improving retrieval strategies and optimizing enterprise prompting frameworks

Error mitigation, fallback procedures, and operational safety guardrails

Building a defensible business case and ROI scorecard for board and CFO review

Scaling successful AI proof-of-concepts into organization-wide production systems

Capital budgeting, total cost of ownership (TCO), and long-term value realization

Programme Benefits

Evaluate AI in their Business Context Identify where AI initiatives fit business goals, operating priorities, and transformation agendas.

Understand How Modern AI Systems Create Value Understand how AI, ML, and Generative AI systems support decision-making and transform business operations.

Identify High-Impact Use Cases Spot high-value use cases and workflows across functions and industries.

Assess Make, Buy, or Partner Choices Assess AI solution choices across make, buy, or partner models.

Lead Better Cross-Functional Conversations Ask relevant questions of data science, technology, and functional teams.

Strengthen AI Readiness Judgment Strengthen understanding of data, governance, and AI readiness.

Use No-Code and Low-Code Tools More Effectively Use no-code and low-code AI tools more effectively for faster adoption.

Evaluate Risk, Reliability, and ROI Assess feasibility, reliability, governance, risk, and ROI for effective investment decisions.

Apply Learning to Real Business Problems Apply learning to a capstone project linked to a real business challenge.

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Prashansa Uttam

Programme Advisor

+91 9403890085contact@echomind.oneMon – Fri, 9am – 5pm IST

IIM Bangalore

Executive Education Office

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