Building an AI MVP without validation is the most expensive way to learn your idea does not work. This guide helps you validate before you build.
Step 1: Validate the problem exists. Talk to 15-20 potential users. Ask about their current workflow and pain points. Do not pitch your solution — listen. If you cannot find people who have this problem, stop here.
Step 2: Validate that AI adds value. Can the problem be solved without AI? If yes, why would users prefer an AI solution? Be honest about whether AI is genuinely better or just trendy. AI adds value when it handles unstructured data, personalizes at scale, or automates expert judgment.
Step 3: Test technical feasibility. Build a quick proof-of-concept with the AI model you plan to use. Test it against 50-100 real examples from your domain. If the model cannot handle your use case at acceptable quality, you need to adjust your approach.
Step 4: Estimate unit economics. Calculate cost per AI inference, expected usage per customer, and target price point. If AI costs exceed what customers will pay, the business model does not work. Find this out before building.
Step 5: Create a landing page and measure demand. Build a simple landing page describing your AI product. Drive traffic and measure conversion (signups, waitlist, demo requests). Aim for 5%+ conversion from qualified traffic.
Step 6: Run a concierge MVP. Before building the full AI system, manually deliver the value your AI product promises. Use AI tools like ChatGPT to assist, but handle the process yourself. This validates that users actually want the output, regardless of how it is produced.
At SpeedMVPs, validation is built into our scoping process. We help teams validate technical feasibility and market fit before committing to a full build.
