Building Real-World AI Automation: 3 Practical Use Cases (With Code)

Skip the hype. Here’s how I use AI to automate business processes — from inboxes to agents to documents.

Introduction

AI has moved fast — but most companies still don’t know how to turn it into working automation. In this series, I walk through three practical use cases I’ve built using large language models (LLMs). These aren’t research projects or toy examples. They’re real workflows that save time, reduce manual work, and deliver value from day one. Each article includes working code, architecture sketches, and clear reasoning — so you can adapt the ideas to your own projects.

1. AI Secretary for Emails

📩 “How I Built an AI Secretary That Handles My Emails” Use case: Automatically extract meaning from incoming emails — like identifying invoices, extracting deadlines, or forwarding relevant content to Slack or Drive. Why it matters: Most email automation relies on brittle filters. This solution uses GPT-4 to understand the email, then trigger actions. Read the full article → https://medium.com/@data.ai.oliver/how-i-built-an-ai-secretary-that-handles-my-emails-dea1f4310dbb

2. Comparing Agent Frameworks

🤖 “LangChain vs CrewAI vs Autogen: Which Agent Framework Should You Use?” Use case: You want to build an AI agent that does more than just chat — something that coordinates tools, plans tasks, or interacts with other agents. Why it matters: Not all frameworks are created equal. This article shows how they differ — and when to use which one. Read the full article → https://medium.com/@data.ai.oliver/langchain-vs-crewai-vs-autogen-a-practical-guide-to-choosing-an-ai-agent-framework-a2d5de59b6c4

3. Automating Everyday Business Tasks

📊 “Automate Your Business with AI — 3 Use Cases You Can Start Today” Use cases included:

Invoice processing (from inbox to archive)
Sales lead triage
Contract intelligence

Why it matters: You don’t need a team of data scientists to start. These are lightweight, high-impact automations anyone can implement with the right setup. Read the full article → https://medium.com/@data.ai.oliver/automate-your-business-with-ai-3-use-cases-you-can-start-today-8ba3f1dc1634

Conclusion

You don’t need to build the next ChatGPT. You just need to identify repetitive workflows and plug in the right AI system. Whether you’re a freelancer, a startup, or an enterprise team: the value is there — if you focus on execution over theory.

Read the full article here: https://medium.com/@data.ai.oliver/building-real-world-ai-automation-3-practical-use-cases-with-code-b45bc75a2b1b