Duration 3 days – 21 hrs

 

Overview

 

This business-driven AI & digital transformation program equips leaders and transformation teams with a practical understanding of AI, data, and emerging technologies—so they can make smarter technology decisions, identify high-impact opportunities, manage risk responsibly, and build realistic adoption roadmaps.

 

Objectives

 

• Make informed technology and investment decisions
• Identify high-impact AI opportunities across business functions
• Understand GenAI capabilities and limitations
• Apply responsible AI, governance, risk, and ethics principles
• Build a business-aligned AI adoption roadmap (pilot → scale → optimize)
• Confidently engage with technical teams and vendors

 

 

Target Audience

 

• CXOs, Executives, Directors, Senior Managers, and Department Heads
• Business Leaders driving digital transformation and innovation
• Digital Transformation / Innovation Teams
• Project Managers, Product Owners, and Business Analysts
• IT & Data Leaders / IT Professionals exploring AI and emerging tech integration
• Operations, HR, Finance, Sales, and Marketing Leaders using data-driven decision-making
• Risk, Compliance, Governance, and Cybersecurity Teams
• Beginners and non-technical professionals seeking structured AI awareness
• Professionals transitioning into AI-driven roles

 

 

 

Prerequisites

 

• No coding experience required
• Familiarity with basic business processes and KPIs is helpful
• Openness to workshops/group exercises and real business case discussions

 

Course Outline

 

Day 1 — Foundation: Transformation + Data + AI Fundamentals

Module 1: Digital Transformation & AI Landscape (Context Setting)

• Digital transformation definition and enterprise drivers
• Role of AI, data & emerging tech in modern organizations
• AI vs Automation vs Analytics
• Global trends shaping AI adoption
• How organizations derive value from AI
  Workshop Output
• “AI value vs hype” sorting exercise
• Opportunity map: Top business pain points AI can impact

Module 2: Data Foundations for AI & Decision Intelligence

• Data types & sources (structured vs unstructured)
• Data lifecycle: collect → store → process → analyze → act
• Data quality and business impact
• KPIs, dashboards & decision intelligence
• Predictive decision-making overview
  Workshop Output
• Data readiness mini-assessment for your organization/function
• “What data do we need?” checklist per use case

 

 

 

Module 3: AI Fundamentals (No Complexity, All Clarity)

• What AI is (and isn’t)
• Machine Learning simplified (supervised vs unsupervised)
• Deep learning overview
• Common enterprise AI use cases (forecasting, recommendations, anomaly detection, customer insights)
  Workshop Output
• Use case evaluation activity: AI-fit vs non-AI-fit problems

 

Day 2 — Execution Layer: GenAI + Emerging Tech + Governance

Module 4: Generative AI & Modern AI Tools (ChatGPT to Enterprise AI)

• What is Generative AI?
• LLMs explained in simple business terms
• Tools overview: ChatGPT, Copilot, Gemini, Claude
• Enterprise use cases: content generation, knowledge management, automation, decision support
• Prompting fundamentals
• Responsible and safe GenAI usage
  Workshop Output
• Prompt lab: role-based prompts (HR, Ops, Finance, Sales, Risk)
• “Safe GenAI usage” checklist for teams

 

Module 5: Emerging Technologies Ecosystem

• Cloud (SaaS, PaaS, IaaS)
• IoT + Edge computing
• Blockchain beyond crypto
• AR/VR + immersive tech
• Intelligent Automation (RPA + AI)
  Workshop Output
• “Tech-to-value mapping” exercise
• Where each tech fits in your enterprise operating model

 

Module 6: AI Governance, Risk & Ethics (Responsible AI Adoption)

• AI risks: bias, hallucination, privacy
• Ethical AI principles
• Compliance and data privacy considerations
• AI governance models
• Risk management for AI initiatives
• Responsible AI adoption framework
  Workshop Output
• Risk & controls checklist for AI projects
• Governance “must-haves” for pilots vs scaled deployments

 

Day 3 — Strategy & Roadmap: Prioritization + ROI + Capstone

Module 7: AI Adoption Strategy & Roadmap (Idea → Implementation)

• Identifying high-impact AI opportunities
• Use case prioritization framework: value, feasibility, data readiness, risk
• AI adoption lifecycle: Pilot → Scale → Optimize
• Measuring ROI and success metrics
• Common failure points and how to avoid them
  Workshop Output
• AI roadmap draft (6–12 months)
• Prioritized use case portfolio with quick wins vs strategic bets

 

Module 8: Capstone Workshop (Hands-On Group Output)

Activity
Teams will:

1. Identify a real business problem
2. Propose an AI/data-driven solution
3. Define business value, risks/constraints, implementation roadmap, and KPIs
   Final Output

• Business-ready AI use case + roadmap + success metrics
• Strategy presentation + peer/trainer feedback