Program Overview
This immersive program equips cross-functional professionals with the skills to apply Generative AI in real business scenarios across operations, decision-making, documentation, customer engagement, and process efficiency. Designed for non-technical managers, the course blends conceptual clarity with situational awareness, real industry examples, and hands-on simulations to help participants identify high-impact use cases and solve actual business problems using GenAI tools. By the end of the program, learners gain practical confidence in using GenAI for everyday tasks, insights, workflows, and decision support—responsibly, safely, and with measurable business impact.
Features
- Identify and prioritize high-value GenAI use cases within your business function
- Use GenAI tools to convert raw data and documents into actionable insights for faster decision-making
- Redesign everyday workflows with GenAI to improve efficiency, accuracy, and stakeholder experience
- Apply safe, responsible, and audit-ready GenAI practices to reduce risk and improve output reliability
Target audiences
- Supply Chain, Procurement & Operations professionals
- Finance, Risk & Compliance professionals
- HR, Talent, L&OD & People-Management professionals
- Sales, Marketing & Customer Experience professionals
- Projects, Quality, Strategy & General Management professionals
Curriculum
- 6 Sections
- 37 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- 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
Instructor
