Generative AI for Everyone

Just enough AI fundamentals for Generative AI

Duration

1 Day

Level

Basic Level

Design and Tailor this course

As per your team needs

Overview

This one-day, hands-on course is designed to introduce professionals across all roles to the transformative potential of Generative and Agentic AI. Participants will explore how AI can improve productivity, enhance creativity, support decision-making, and be used responsibly—without needing a technical background. Through relatable examples, practical exercises, and real-world scenarios, learners will develop the confidence and skills to integrate AI into their daily work.

By the end of this course, participants will be able to:

  • Understand the fundamentals & potential of Generative AI across industries & roles
  • Apply generative thinking to improve productivity in daily work through task automation, idea generation, and information synthesis
  • Craft effective prompts to guide AI systems toward more useful & relevant outcomes
  • Explore the potential of autonomous AI agents for decision-making and innovation
  • Practice ethical and responsible use of AI, ensuring fairness, transparency, & accountability

Audience

This course is designed for everyone in the organization, including:

  • Frontline staff looking to automate repetitive tasks
  • Managers aiming to streamline team workflows
  • Executives and decision-makers exploring strategic innovation
  • HR, Finance, Marketing, IT, Operations, and other functional professionals
  • Non-technical professionals curious about AI’s everyday impact

Prerequisites

  • Basic Digital Literacy
  • Openness to Experimentation
  • Access to approved Generative AI tools
  • Curiosity about improving productivity and innovation using Gen AI

Curriculum

What is Generative AI vs. Traditional AI?

  • Clear definitions and key differences

  • Business impact within the insurance ecosystem

  • How Generative AI differs from rule-based and predictive AI systems

Anatomy of Modern AI Systems

  • Understanding Large Language Models (LLMs)

  • Natural Language Processing (NLP) fundamentals

  • Domain-trained and industry-adapted models

  • Application of these technologies within insurance workflows

Key AI Terminology Explained

  • Models

  • Training data

  • Inference

  • Hallucinations, with insurance-based examples

AI Output Types in Insurance

  • Text: Policy wording, endorsements, client communication

  • Image: Damage photo analysis and claims documentation

  • Code: Automation scripts and workflow enhancements

  • Audio: Call summaries and customer interaction insights

Cross-Industry vs. Insurance-Specific Use Cases

Cross-Industry Applications

  • Banking

  • Retail

  • Healthcare

  • Enterprise customer support

Insurance-Specific Applications

  • Risk scoring and premium pricing models

  • Policy document generation tailored to individual clients

  • Claims image summarization and fraud detection

AI as a “Thought Partner,” Not a Replacement

  • AI as an augmentation tool for decision-making

  • Human oversight in underwriting, claims, and compliance

  • Maintaining regulatory accountability

The 80–20 Mindset in Insurance AI

  • AI handles structured drafts and first-level outputs

    • Drafting policy terms

    • Generating claim summaries

  • Humans validate, refine, and approve final decisions

Understanding Workflows

  • Task Classification: Routine, Creative, Analytical, Collaborative

  • Mapping Workflows Where Generative AI Excels: Drafting, summarizing, brainstorming, reformatting

    • Underwriting: Generate risk summaries, calculate premium scenarios

    • Claims: Auto-extract accident data, draft claim reports, flag anomalies

    • Fraud Detection: Pattern analysis, anomaly scoring

    • Customer Service: Chatbots for FAQs, policy details, renewal reminders

Departmental Examples of Productivity Gains

  • HR: Crafting internal policy documents, drafting DEI statements

  • Marketing: Rapid headline testing, content calendar generation

  • Finance: Trend analysis summaries, explanation of financial terms for laypeople

  • Sales: Personalized message templates, competitor summaries

  • IT: Drafting SOPs, writing knowledge base articles

  • Legal/Compliance: Summarizing policy changes, drafting risk statements

  • Executives: Condensing meeting notes, strategic report skeletons

  • Ways to Work with Generative AI

  • Why Prompt Engineering Matters?

  • Prompting Basics

    • Anatomy of a Good Prompt

      • Instruction

      • Context

      • Example

    • Prompt Types

      • Informative

      • Creative

      • Summarizing

      • Converting

      • Transforming

  • Prompt Engineering Formats and Techniques

    • ReAct Prompting

    • Prompt Techniques

      • Few-Shot Prompting

      • Chain-of-Thought Prompting

      • Multi-Step Prompting

  • What is Agentic AI? Understanding AI agents that act, decide, and adapt
  • From assistants to collaborators: imagining intelligent task handlers
  • Creative thinking: Designing your ideal digital co-worker
  • Misinformation, bias, and “hallucinations” in AI systems
  • Fairness and inclusion: who is represented in AI outputs?
  • Copyright, transparency, and data use concerns

What is an Agent Type?

  • Zero-Shot ReAct → reasoning + acting without examples
  • Conversational Agent → context retention in dialogue
  • Multi-Function Agent → when multiple tools are available
  • OpenAI Function Calling Agent → structured tool invocation
  • Use Cases:
    • Zero-Shot for ad hoc queries
    • Conversational for chatbots
    • Multi-Function for data workflows
  • Hands-on: test the same query with different agent types and compare behavior

Let’s Build Your Growth Ecosystem.

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