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Learning Objective: Understand what Generative AI is, identify the right tools for business tasks, and separate real value from hype.
Topics:
What is Generative AI? — no-jargon explanation
GenAI vs. Google Search, Excel, and traditional software
Tools landscape: ChatGPT, Gemini, Copilot, Claude, Perplexity
What GenAI can and cannot do — strengths vs. limitations
Case Study: Infosys Topaz — enterprise GenAI rollout
Skills:
Explaining GenAI to any business audience
Matching tools to professional tasks
Evaluating GenAI claims critically
Hands-On:
Complete 5 guided tasks in ChatGPT or Gemini: explain a concept, summarize text, draft an email, build a comparison table, fact-check an AI answer; fill in a reflection template
Learning Objective: Write structured prompts using RACE, CO-STAR, and Chain-of-Thought frameworks to produce accurate, high-quality business outputs.
Topics:
Why prompt quality determines output quality
RACE framework: Role, Action, Context, Expectation
CO-STAR framework: Context, Objective, Style, Tone, Audience, Response
Chain-of-Thought prompting for analysis and decisions
Iterative prompting: refining outputs step-by-step
Spotting and fact-checking hallucinations
Case Study: McKinsey's Lilli — structured prompting for consulting outputs
Skills:
Applying RACE and CO-STAR to business scenarios
Using Chain-of-Thought for multi-step analysis
Detecting and correcting hallucinated AI outputs
Hands-On:
5 prompt exercises: status update email, SWOT analysis (CO-STAR), article summary, vendor rejection note, Chain-of-Thought consulting scenario; iterate each prompt once
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
This is a 10-module, self-paced certification course that teaches non-technical professionals how to use AI tools like ChatGPT, Google Gemini, Microsoft Copilot, and Google NotebookLM to get real work done across marketing, HR, operations, strategy, and more.
By the end of the course, you'll be able to write structured prompts, create business content using free AI tools, ground AI on your company's documents, build a GenAI business case with ROI, draft an AI governance policy, and complete a peer-reviewed AI Adoption Blueprint.
AI fluency is now a baseline expectation across most professional roles. Roles listing AI skills pay significantly more on average than equivalent roles without them. This course gives you the practical skills to use AI at work from day one — not theory, not code, just real-world application.
This course is for business managers, marketing and sales professionals, HR teams, strategy consultants, entrepreneurs, MBA students, and C-suite decision-makers who want to use AI effectively — without needing a technical background.
Absolutely. If you're already using ChatGPT casually, this course will sharpen how you use it: structured prompting frameworks, grounding AI on your company's documents, building business cases for AI initiatives, and navigating governance risks are skills most casual users have never been exposed to.
No prior knowledge of AI, data science, or programming is required.
This course is built specifically for non-technical professionals. Every lab uses free, no-code tools — ChatGPT, Gemini, NotebookLM, and Canva AI. Zero setup required.
The course is 11 hours of structured content across 10 modules. Since it's self-paced, most learners finish in 2–3 weeks by spending 3–4 hours per week.
RAG stands for Retrieval-Augmented Generation — it's how you make AI answer questions using your documents instead of general internet knowledge. In Module 6, you'll learn to do this without any coding using Google NotebookLM and ChatGPT's file upload feature. It's one of the most practical skills in the course.
Yes. Edureka's Generative AI for Business Professionals Certificate is awarded after you complete all modules, submit your Capstone Blueprint, and finish the peer review. The Certificate ID is verifiable at edureka.co/verify.
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