Module 05: AI Strategy Within Business and Transformation Strategy

• Role of AI within business and digital transformation strategy

• Aligning AI initiatives with business priorities and value creation

• Components of AI strategy: people, process, platforms, data, and governance

• Organisational models for AI adoption

• Evaluating operating model trade-offs

• Blueprint for enterprise AI transformation and operating model design

Module 06: AI Strategy and Business Case Development

• Frameworks for identifying and prioritising AI opportunities

• Build vs. buy vs. configure decision frameworks

• Structuring AI business cases: cost, ROI, risk, and strategic value

• Common failure patterns in AI strategy and mitigation approaches

Module 07: Data Strategy for AI-Ready Organisations

• Data quality as a predictor of AI outcomes

• Data readiness audits across structured, unstructured, and real-time environments

• Data governance, ownership, privacy, and regulatory implications

• Building a data-driven organisational culture

Module 08: How AI Works–A Business Mental Model

• Machine learning (ML), deep learning, and foundation models for business leaders

• How AI models learn and common failure modes

• Understanding the AI value stack

• Core AI capability categories: prediction, classification, generation, reasoning and action

Module 09: AI-Augmented Decision-Making

• Strategic versus operational decision-making with AI

• Predictive analytics for demand, churn, revenue, and risk

• Reducing cognitive bias through AI-assisted decision support

• Knowing when not to rely on model outputs

Module 10: AI in Core Business Operations

• Finance: FP&A automation, anomaly detection, and real-time reporting

• Supply chain: forecasting, disruption sensing, and inventory optimisation

• HR analytics: workforce planning, attrition prediction, and skill-gap mapping

• Measuring and communicating AI-driven ROI

Module 11: AI-Powered Business Intelligence

• Evolution from dashboards to AI-driven recommendation systems

• AI-assisted business intelligence tools and applications

• Designing insight-to-action workflows

• Institutionalising a data-driven decision culture

Module 12: Generative AI–Foundations, Capabilities and Business Impact

• What differentiates generative AI from previous AI paradigms

• Large Language Models (LLMs) and multimodal AI across text, image, voice, and video

• Enterprise applications and workflow integration

• Practical limitations: hallucinations, context windows, and bias

• Impact of generative AI on knowledge work and business transformation

Module 13: GenAI for Executive and Functional Transformation

• AI-augmented research, synthesis, writing, and decision support

• Executive briefings, reports, and strategic communication using Generative AI

• Functional applications across marketing, sales, HR, finance, and legal

• Enterprise knowledge systems and RAG concepts

• Redesigning workflows in the age of Generative AI

Module 14: Understanding Agentic AI Systems and Business Applications

• Foundations of agentic AI

• The autonomy spectrum: assisted to autonomous systems

• Anatomy of an AI agent

• Multi-agent architectures and business applications

Module 15: Agentic AI–Applied Business Use Cases and Risk

• Finance: FP&A automation, anomaly detection, and real-time reporting

• Supply chain: forecasting, disruption sensing, and inventory optimisation

• HR analytics: workforce planning, attrition prediction, and skill-gap mapping

• Measuring and communicating AI-driven ROI

Module 16: AI Risk Management and Governance

• Enterprise AI risk taxonomy

• Regulatory and compliance landscape

• Governance framework design

• Accountability structures for AI systems

Module 17: Ethics, Fairness and Human Accountability

• Algorithmic bias and mitigation strategies

• Designing accountability for AI-driven decisions

• Human capabilities in AI-augmented organisations

• Communicating AI decisions to stakeholders

Module 18: Designing an AI Transformation Programme

• Transformation vision, road map, governance, talent, and culture

• Change management for AI adoption

• Building internal AI capabilities

• AI transformation case studies from leading Indian enterprises

Module 19: Sustaining Competitive Advantage with AI

• AI as a strategic moat

• Future outlook: reasoning models, physical AI, and AI-to-AI economies

• Designing the AI-native organisation

• Building a 90-day leadership action plan

Capstone

• AI Transformation Blueprint