Building AI tools that users actually want

After launching six AI-powered tools in the past year—from Privy AI's privacy policy analyzer to our upcoming NDI Capture tool—we've learned that building AI tools users actually want requires a fundamentally different approach than traditional software development. Here's what we wish we'd known when we started. ## The AI Tool Paradox The biggest challenge with AI tools isn't the AI—it's everything else. We've seen countless technically impressive AI demos that nobody uses because they: - Solve problems people didn't know they had - Require too much setup or learning - Produce outputs that need extensive editing - Feel like magic tricks instead of practical tools The most successful AI tools we've built feel less like "AI" and more like superpowers. ## Lesson 1: Start with the Job, Not the Technology ### What We Used to Do "We have access to GPT-4, what could we build with it?" ### What We Do Now "What job are people struggling to do that AI could help with?" **Privy AI Example:** Instead of "let's build a document analyzer," we started with "people spend hours reading privacy policies they'll never understand—how can AI make this instant and clear?" The result? 94% user satisfaction because we solved a real pain point, not just showcased AI capabilities. ## Lesson 2: The Output Is Only Half the Product Great AI tools aren't just about generating good outputs—they're about making those outputs immediately useful. ### Bad AI Tool Flow: 1. User inputs data 2. AI processes and returns raw output 3. User figures out what to do with it ### Good AI Tool Flow: 1. User inputs data 2. AI processes and returns structured, actionable output 3. User can immediately use or export the result **NDI Audio Recorder Example:** We don't just capture audio streams—we provide frame-accurate timestamps, multiple export formats, and real-time monitoring so broadcast professionals can drop our output directly into their workflows. ## What Actually Makes AI Tools Successful After analyzing our hits and misses, successful AI tools share these characteristics: ### 1. They Replace, Don't Augment Good AI tools completely replace a tedious task. They don't just "help" with it. ### 2. They Work with Existing Workflows People won't change their entire process for your tool. Your tool needs to fit into their existing process. ### 3. They Have Clear Success Metrics Users need to know if the AI did a good job. Vague outputs kill adoption. ### 4. They Get Better with Use Whether through fine-tuning, better prompts, or user feedback, the tool should improve over time. ## Building Your Own AI Tool: A Checklist Before you start building, ask: - [ ] Does this solve a problem people actively complain about? - [ ] Can you build a working prototype in under a week? - [ ] Will the output be immediately usable without editing? - [ ] Does it fit into existing workflows? - [ ] Can you measure success objectively? - [ ] Would you use this tool weekly if someone else built it? If you can't answer "yes" to all of these, reconsider the approach. --- *Interested in collaborating on an AI tool for your industry? [Get in touch](/contact)—we're always looking for domain experts who want to explore what's possible.*