AI, Data, and Emerging Technologies provides practical knowledge of artificial intelligence, data-driven decision-making, automation, cloud computing, IoT, blockchain, and other innovations transforming modern businesses and industries.
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:
- Identify a real business problem
- Propose an AI/data-driven solution
- Define business value, risks/constraints, implementation roadmap, and KPIs
Final Output
- Business-ready AI use case + roadmap + success metrics
- Strategy presentation + peer/trainer feedback
