Your AI's biggest fear? The blank text box.
Everyone freezes at "How can I help you?" That empty text box with infinite possibilities is where most AI products die. Not because the AI is bad - because users have no idea what to ask.
Your stakeholders want AI that answers every business question. Your data can barely answer one question reliably.
Here's how to ship V1 anyway.
The narrow intelligence approach:
Don't build "Ask me anything about your business." Build "I can tell you which deals are most likely to close this quarter based on engagement patterns."
One question. Answered well. That's your V1.
How to know your data is ready for V1: The single-question test
Pick ONE question users desperately need answered. Can your data reliably answer that specific question with 70-80% accuracy? Ship that.
The "show your work" test
When AI gives an answer, can it explain reasoning using data points users already trust? If not, your data has gaps they'll notice immediately.
The rollback plan
Can users override AI recommendations without breaking their workflow? If AI failure = catastrophic failure, you're not ready.
The UI moves that save your limited V1:
Don't give them a blank text box. "How can I help you?" with infinite possibilities highlights how limited your AI actually is.
Show trending questions
Display the 3-5 questions your AI can actually answer well:
"Which deals are most likely to close this month?"
"Which customers haven't engaged in 30+ days?"
"What's our win rate by industry?"
Provide example prompts
"Try asking: Show me stalled deals over $50K" - trains users what's possible AND keeps them in your AI's narrow lane of competence.
Show what you can't answer (yet)
When users ask outside your scope: "I can't answer that yet, but I can tell you [related thing I'm trained on]." Honesty builds more trust than fake answers.
The V1 message:
"Our AI is really good at predicting deal closure probability. It uses engagement data, email responses, and meeting frequency. It will be wrong sometimes - here's how to verify."
Narrow scope. Clear data sources. Honest limitations.
Most companies never ship because they're waiting for AI that knows everything.
Meanwhile, competitors ship AI that knows one thing really well, learn from real usage, and iterate.
Perfect data is a myth. Smart scoping is a strategy.
What's the ONE question your data could answer well enough to ship today?