Curriculum
- 6 Sections
- 37 Lessons
- 1 Day
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- Understanding GenAI in Business Operations7
- 1.1What exactly is GenAI? (LLMs, multimodal models, agents, copilots)
- 1.2Key terms: prompt engineering, embeddings, vector search, knowledge grounding, RAG pipelines, fine-tuning vs. orchestration, agent workflows, model hallucination, guardrailsWhere organisations are failing: data silos, weak documentation culture, unstructured knowledge.
- 1.3GenAI vs. Traditional Automation (RPA, BPA, BPM)
- 1.4Where organisations are failing: data silos, weak documentation culture, unstructured knowledge
- 1.5Where GenAI fits in the digital ops stack
- 1.6Example: How top companies (manufacturing, pharma, BFSI, FMCG, IT services) use GenAI in day-to-day operations
- 1.7Exercise: Identify repetitive or knowledge-heavy tasks in your function → map which ones are GenAI-automatable
- GenAI for Decision-Support & Insight Generation5
- 2.1Autonomous reasoning, chain-of-thought, tool calling.
- 2.2Data-to-insight flows: classification, summarization, scenario modeling
- 2.3Situational Awareness: When to rely on GenAI vs. when not to (risk, accuracy logic), Using GenAI to correct human bias and blind spots
- 2.4Real-life Use Cases: Daily MIS, business reviews (MBR/QBR), performance KPIs, Automated competitor intelligence, Ops forecasting and what-if analysis using prompt chaining
- 2.5Live Prompt Lab: Convert a raw Excel/Word/PDF dump into a decision-ready brief
- GenAI for Process Optimization & Operational Efficiency8
- 3.1Process mining + GenAI
- 3.2Intelligent workflow orchestration (agents + APIs)
- 3.3Knowledge automation (SOP extraction, policy interpretation)
- 3.4Situational Awareness: Where processes typically break (handoffs, versioning, exceptions), Human-in-loop designs for safe automation
- 3.5Real Example: Customer operations: automated case routing, email drafting, SLA triage
- 3.6Supply Chain: demand signals, root-cause analysis for OTIF failures
- 3.7Examples: Finance- automated reconciliations, spend analysis, HR: JD-to-candidate matching, policy Q&A agents
- 3.8Exercise: Redesign a broken process from participants’ functions using a GenAI-enabled workflow canvas
- GenAI for Documentation, Knowledge Management & Compliance6
- 4.1Enterprise Knowledge Graphs
- 4.2Retrieval-Augmented Generation (RAG) for compliance-heavy teams
- 4.3Content generation vs. content validation
- 4.4Situational Awareness: Risk of hallucinations and outdated documents, managing confidential/PII/regulated data
- 4.5Real Examples: Policy interpretation bots for procurement, finance, HR, Auto-generated SOPs, KRAs, contracts, audit trails
- 4.6Exercise: PProvide a real policy/SOP → Create a GenAI-ready knowledge brief
- GenAI for Customer, Sales & Stakeholder Engagement6
- 5.1Conversational AI vs. Assistive AI vs. Autonomous Agents
- 5.2Sentiment models, customer intent detection
- 5.3Situational Awareness: When to automate customer interactions vs. when to escalate to humans
- 5.4Real Examples Sales: auto-proposals, lead qualification, pricing Q&A agents, marketing: segmentation, personalized content
- 5.5Customer service: 24/7 intelligent triage, escalation prediction
- 5.6Persona-based Prompting Drill: Craft prompts for customer/stakeholder personas (CXO, manager, consumer)
- Risk, Governance, & Responsible AI5
- 6.1AI governance frameworks (EU AI Act, ISO 42001, model risk frameworks)
- 6.2Accuracy, privacy, bias, explainability
- 6.3Situational Awareness: Red flags for non-tech users, how to check GenAI output reliability
- 6.4Real Examples: Case studies where AI errors caused operational failures, secure enterprise-grade usage patterns
- 6.5Exercise: Audit a GenAI output for accuracy, bias, compliance & actionability