February 14, 2026 • 8 min read
Everybody builds AI projects. Very few people use them. Here's how to change that.
The Problem With Most AI Projects
You've probably seen it: someone builds a cool AI chatbot, fine-tunes an LLM, or creates an AI-powered automation tool. They get 50 GitHub stars, write a medium post, and then... nothing. The project sits dormant.
The issue isn't the technology. It's that most AI projects solve the wrong problem: they solve a problem the builder found interesting, not a problem people actually need solved.
Let me show you how to flip this.
1. Start With A Real Problem (Not An AI Idea)
Wrong approach: "I want to build something with LLMs. What can I make?"
Right approach: "What problem do I experience every day that wastes my time?"
Ask yourself:
- What tasks do I repeat weekly?
- What takes me 2+ hours to do manually?
- What do I complain about to friends?
- What would my coworkers pay for?
Example: I noticed I spent 45 minutes every Friday compiling team updates into a weekly report. That's a real problem. An AI tool that scrapes messages, summarizes them, and formats them as a report? That has instant value.
Your task: Write down 3 problems you face. Pick one. This is your starting point.
2. Solve The Problem With Or Without AI First
Here's where people get it wrong: they force AI into the solution.
"I need to organize my notes better... I know! I'll build a neural network!"
No. A good spreadsheet might be all you need.
The principle: Use the simplest tool that solves the problem. AI is a tool, not a solution on its own.
- Problem: Manually sorting customer feedback. Simple solution: A spreadsheet with filters. AI upgrade: LLM categorizes feedback automatically.
- Problem: Code reviews take too long. Simple solution: A checklist. AI upgrade: ChatGPT analyzes code for common issues first.
- Problem: Writing repetitive emails. Simple solution: Email templates. AI upgrade: Fine-tuned model generates personalized versions.
Your task: For the problem you picked, write out the simplest solution first. Then think: where would AI actually make this better?
3. Build For The Specific Use Case, Not The General One
Don't build: "A general-purpose AI assistant"
Build: "An AI assistant for therapists to track session notes"
Don't build: "An AI code reviewer"
Build: "An AI code reviewer for React components that checks for accessibility issues"
Specific = useful. General = ignored.
Why? Because specific problems have specific metrics for success. A therapist knows exactly if your notes tool saves them time. A React developer knows if your reviewer caught bugs they missed.
Your task: Who is your end user in one sentence? What's their biggest pain point?
4. Make It Easy To Get Started (Seriously)
Your AI project is useless if nobody can figure out how to run it.
Common mistakes:
- Requires 5 environment variables to set up
- Needs 3 API keys from different services
- Has a 2-page README nobody will read
- Requires Python 3.8 but docs don't mention it
Make setup take less than 5 minutes.
Real example of good onboarding:
- Click one button to deploy
- Enter one API key
- Done
Tools that do this well: Vercel, Replit, Hugging Face Spaces. Your project should match that standard.
Your task: Write down the exact steps someone needs to use your project. If it's more than 5 steps, simplify.
5. Show It In Action (With Real Results)
Don't just describe your AI project. Show it working on a real problem.
Instead of: "This chatbot uses fine-tuned GPT to answer customer questions"
Say: "This chatbot reduced response time from 2 hours to 30 seconds. Here's the before/after transcript."
Content types that work:
- Side-by-side comparisons (manual vs. AI)
- Time/cost saved (e.g., "saves 10 hours/month")
- Case study with a real person using it
- Demo video showing the full workflow
- Metrics: accuracy %, speed improvement, error reduction
Numbers convince people. Stories convince people to care. Use both.
Your task: What metric proves your project works? How will you measure it?
6. Build In Public (Even If Your Project Isn't Perfect)
Share your progress before it's done. You'll get feedback earlier. You'll build an audience. You'll stay accountable.
Share at these stages:
- Stage 1: "I'm building X because Y problem"
- Stage 2: "Here's a rough demo"
- Stage 3: "It's working but needs polish"
- Stage 4: "It's live, here's the code"
Post on Twitter, write on Medium, share in relevant Discord servers. Get feedback. Adjust. Repeat.
Your task: Where will you announce your project? (Twitter, Product Hunt, Reddit, Discord, LinkedIn?)
7. Make Money or Create Impact (Pick One)
Your project doesn't have to be free. Your project doesn't have to be a startup.
Pick your goal:
- Goal: Impact → Make it open-source, free, easy to use
- Goal: Money → Build a paid product, SaaS, or consulting gig around it
- Goal: Portfolio → Make it impressive and well-documented for hiring
Your task: What's your goal for this project? Be honest with yourself.
Case Study: What Works
Successful AI Project Pattern:
- ✅ Solves one specific problem extremely well
- ✅ Takes less than 5 minutes to set up
- ✅ Shows clear before/after results
- ✅ Has open source code or accessible demo
- ✅ Built in public from the start
- ✅ Has clear business model (free, paid, or both)
Failed AI Project Pattern:
- ❌ Tries to solve everything
- ❌ Takes 30 minutes to configure
- ❌ No metrics or proof of impact
- ❌ Code is private or hard to access
- ❌ Announced once when finished
- ❌ Unclear if you can use it
Your Action Plan
This week:
- Identify one problem you face (see section 1)
- Write the simplest solution (see section 2)
- Define your specific user (see section 3)
Next week:
- Build a basic version
- Get it working on your own use case
- Announce it somewhere
Following week:
- Collect feedback
- Improve based on real usage
- Make it public
The Bottom Line
Most AI projects fail because they're built backwards. They start with technology instead of problems. They're made for people instead of by people who know the pain.
Flip it: Find a real problem. Solve it simply. Use AI where it actually helps. Make it easy to use. Show it works. Build in public. Done.
That's how you build AI projects people actually use.