Generative AI for Business Transformation

Learning Objective: Use free AI tools to create marketing content, draft sales outreach, and evaluate outputs for brand alignment and quality.

Topics:

• Social posts, email sequences, taglines, ad copy — with before/after examples

• Customer persona building and trend research using AI

• Visual content: Canva AI and Microsoft Designer

• Sales use cases: proposals, cold outreach, objection-handling scripts

• AI-assisted CRM tools overview: HubSpot AI, Salesforce Einstein

• Brand voice and quality control

• Case Study: Coca-Cola's 'Create Real Magic' campaign

Skills:

• Multi-format content creation with free AI tools

• AI-assisted sales outreach and proposal writing

• Evaluating AI content for tone, accuracy, and brand fit

Hands-On:

• For a fictional product: create 3 social posts (LinkedIn, Instagram, X), a 2-email welcome sequence, a product description, and a cold outreach email; review all using a provided checklist

Learning Objective: Apply AI to streamline daily operations — documentation, reports, and SOPs — while knowing when human judgment must take over.

Topics:

• AI for drafting: meeting notes, action items, follow-up emails

• Generating SOPs, checklists, training materials

• Finance use cases: P&L summaries, variance commentary, board narratives

• When not to use AI: regulatory filings, legal documents, sensitive data

• Case Study: Bain & Company — AI across 18,000 employees

Skills:

• Creating operational documentation with AI

• Drafting executive summaries and board narratives

• Applying human-in-the-loop judgment for sensitive content

Hands-On:

• Using a provided 2–3 page report, prompt AI to: write a 5-sentence executive summary, list top 3 risks, generate 5 CEO questions, and draft a board update; compare against a model answer

Learning Objective: Draft HR documents and internal communications using AI while identifying bias and confidentiality risks in people-related use cases.

Topics:

• Inclusive job descriptions and interview scorecards

• Onboarding kits, FAQ generators, learning paths

• Townhall scripts, policy announcements, team newsletters

• Bias in AI hiring; what employee data must stay out of AI

• Case Study: Unilever — screening 1.8M applicants with AI

Skills:

• Drafting structured HR documents with AI

• Writing internal communications at scale

• Spotting bias and confidentiality risks in AI outputs

Hands-On:

• For a given role: create an inclusive JD, a 6-question interview guide, a rejection email, and a first-week onboarding checklist; evaluate each using a bias and tone rubric

Learning Objective: Understand RAG in plain language and use no-code tools like NotebookLM to ground AI on internal company documents.

Topics:

• Why public AI isn't enough for company-specific knowledge

• RAG explained simply: giving AI a reference library

• No-code grounding tools: ChatGPT file upload, Gemini + Google Workspace, Copilot + SharePoint

• Google NotebookLM: upload documents, AI answers only from them

• Data readiness checklist: organized, accurate, accessible, safe

• Case Study: Morgan Stanley — AI assistant on 100,000+ internal documents

Skills:

• Using NotebookLM, ChatGPT file upload, and Gemini for document Q&A

• Assessing organizational data readiness

• Explaining RAG to non-technical stakeholders

Hands-On:

• Upload a sample company handbook to NotebookLM or ChatGPT; ask 8 document-specific questions; score whether AI answered from the doc or hallucinated; log findings in an accuracy scorecard

Learning Objective: Identify, score, and prioritize GenAI opportunities using the impact-feasibility matrix and build a phased adoption roadmap for leadership.

Topics:

• Low-hanging fruit framework: repetitive, text-heavy, low-risk tasks

• Impact-feasibility matrix: business value vs. implementation complexity

• Build vs. Buy vs. Partner — for non-technical decision-makers

• Adoption phases: crawl → walk → run

• Case Study: TCS AI.Cloud — embedding GenAI into enterprise workflows

Skills:

• Scoring and prioritizing GenAI use cases

• Structuring a phased adoption roadmap

• Evaluating Build vs. Buy vs. Partner without technical background

Hands-On:

• For a fictional company profile: identify 8 use cases, score on impact-feasibility matrix, select top 3, draft a half-page leadership recommendation memo

Learning Objective: Identify GenAI risks, understand global and Indian AI regulations, and draft a practical Acceptable Use Policy for your organization.

Topics:

• Hallucinations, bias, data leaks, copyright — real incidents

• Risk categories: reputational, legal, operational, financial

• Regulatory overview: EU AI Act, NIST AI RMF, India's MeitY AI Guidelines

• Guardrails: Acceptable Use Policies, human-in-the-loop, approval workflows

• Case Study: Samsung data leak — confidential code shared with ChatGPT

Skills:

• Communicating GenAI risks to leadership

• Drafting an AI Acceptable Use Policy

• Applying AI regulations to business decisions

Hands-On:

• Draft an AI Usage Policy for a fictional 500-person company covering: approved tools, prohibited uses, data handling, oversight requirements, and incident reporting; benchmark against Microsoft and IBM policies

Learning Objective: Build a 1-page ROI-backed GenAI business case, design a 90-day pilot, and handle common leadership objections confidently.

Topics:

• Why AI projects fail — and how to avoid common traps

• Business case structure: problem → solution → benefit → cost → risk → timeline

• ROI measurement: time saved, cost reduced, quality improved + downloadable ROI Calculator

• Objection-handling playbook: security, job replacement, ROI questions

• Case Study: Walmart — function-level AI pilot before company-wide rollout

Skills:

• Building a quantified GenAI business case

• Designing a 90-day pilot with clear success criteria

• Responding to leadership objections with confidence

Hands-On:

• Using a provided template: define the problem, describe the solution, quantify ROI, estimate costs, list top 3 risks, propose a 90-day timeline; use AI to role-play a skeptical CFO and stress-test your pitch

Learning Objective: Understand emerging AI trends — agents, multimodal AI, and industry transformation — and build a 6-month personal upskilling plan.

Topics:

• AI Agents: software that acts, not just answers — Copilot Agents, Gemini Agents, ChatGPT Operator

• Multimodal AI and AI-native products — business implications

• Industry outlook: financial services, healthcare, education, retail, professional services

• Future of work: skills that matter — prompt literacy, critical thinking, AI judgment

• Case Study: Google DeepMind's AlphaFold — AI-driven scientific discovery

Skills:

• Explaining AI Agents and emerging trends to business audiences

• Assessing industry-level AI impact

• Building a personal AI upskilling roadmap

Hands-On:

• Self-assess AI skill level using a rubric; identify 3 development areas; list free resources for each; set monthly milestones for 6 months; use AI to refine the plan