Local AI for Executives – Private, Secure, and Offline AI Analytics

Enable professionals with basic computer skills to install, configure, and use local LLMs AI agents for data analysis, document processing, and decision-making—without sending data to the internet.

• Enable executives to run AI locally without sending sensitive data to the cloud.
• Simplify LLM deployment for beginners (no coding required).
• Teach secure, offline AI workflows for document analysis, spreadsheets, and automation.
• Compare local vs. cloud AI – When to use each.
• Ensure data sovereignty for GDPR and corporate compliance.

Module 1: Why Local AI? Privacy, Security & Business Benefits

• Risks of cloud-based AI (data leaks, compliance issues)
• Advantages of local AI (GDPR-friendly, full control, no vendor lock-in)
• Use cases: Document analysis, internal data Q&A, report generation
• Demo: A local AI chatbot answering questions from an internal PDF

Module 2: Understanding Local LLMs – No Technical Jargon

• What is an LLM? (Simplified for non-tech users)
• Open-source vs. proprietary models (Mistral, Llama 3, Phi-3 vs. ChatGPT)
• Hardware requirements (Do you need a GPU? Can a laptop handle it?)
• Exercise: Checking your computer’s specs for AI compatibility

Module 3: Installing Your First Local AI – Step-by-Step Guide

• Tool of choice: Ollama (user-friendly local LLM manager)
• Alternative: LM Studio (for Windows/Mac with GUI)
• Hands-on: Installing & running a small LLM (e.g., Mistral 7B)
• Troubleshooting: Common installation issues & fixes

Module 4: Interacting with Your Local AI – Chat & Basic Prompts

• How to "talk" to your AI (basic prompting for business use)
• Asking for summaries, insights, and data extractions
• Workshop: Uploading a PDF (e.g., a financial report) and querying it

Module 5: Local AI for Data Analysis – Spreadsheets & Databases

• Using local AI to analyze CSV/Excel files (no Python needed)
• Tool: Pandas AI (local mode) or LibreChat (for structured queries)
• Exercise: Uploading sales data and asking, "What are the top 3 trends?"

Module 6: AI Agents for Automation – Simple Workflows

• What is an AI agent? (AutoGPT, BabyAGI simplified)
• Use case: Automating meeting notes, email sorting
• Demo: A local AI agent scanning a folder of docs and generating a summary

Module 7: Security & Maintenance – Keeping Your AI Safe

• Best practices for data isolation (firewalls, encrypted drives)
• Updating models & software
• When NOT to use local AI (limitations vs. cloud AI)

Module 8: Wrap-up, Certification & Next Steps

• Recap of key tools & workflows
• Q&A + Real-world troubleshooting
• Certification Award & Resource Kit (cheat sheets, recommended models)
• Feedback & Networking

• Privacy risks of cloud AI – Why local models are safer for sensitive data.
• Hardware requirements – What specs are needed to run AI locally.
• No-code local AI tools (Ollama, LM Studio, LibreChat).
• Use cases – Internal report generation, confidential document analysis.
• Security best practices – Keeping local AI models updated and isolated.

Methodology based on Active Learning: 50% minimum practice. Each theoretical point is systematically followed by examples and exercises.

• Participants will complete small, practical assignments after each module.
• Progress will be continuously monitored through quick tasks and feedback.
• There will be no heavy exams - just simple hands-on practice to build confidence.

Certificate of completion