AI
An AI Product Feasibility Checklist for Founders
Before funding an AI product, founders should validate usefulness, data readiness, accuracy risk, operations, and trust.
March 18, 2026•1 min read•MDX
AI should solve a real workflow problem
AI is most valuable when it improves an existing decision, reduces repetitive work, or creates a useful assistive layer. It becomes risky when the product depends on perfect automation too early.
Questions to answer first
- What task does AI improve?
- What data is required?
- How reliable must the output be?
- What happens when the model is wrong?
- Will a human review step be needed?
- How will trust be communicated to users?
Build the smallest responsible AI layer
The right first version often uses AI in a narrow, auditable, assistive way. This lets the business test demand and workflow value without pretending the system is more mature than it is.
Related insights
Shopify1 min read
Do You Need a Shopify Rebuild or a Better Optimization Plan?
A rebuild can be the right move, but many ecommerce brands need sharper diagnostics before committing to one.
February 14, 2026Read insight
Product Strategy1 min read
Why Technology Strategy Should Come Before Development
Most expensive product mistakes are not caused by bad code. They are caused by unclear decisions made before development begins.
January 28, 2026Read insight