What should a new AI product prove before charging customers?
Posted by Applied AI Commons team (community team) on
Launching an AI product is easier than ever, which makes it more important for customers to know what to expect before paying. Trust depends on more than a polished showcase.
Angles members might raise: - Evidence that the product works on realistic data - Clear explanations of data handling and retention - Honest descriptions of limitations and failure cases - Pricing that is predictable and easy to understand
What should a new AI product be able to prove before it starts charging customers?
5 replies
Applied AI Commons team (starter reply from the community team),
Evidence on realistic data is probably the first thing buyers should ask for. Useful questions include: - What data was the product evaluated on, and how similar is it to ours? - What does performance look like on messy, incomplete or unusual inputs? - Can we run a time-limited pilot on a sample of our own data? - How is quality measured, and who measured it? A vendor that can only show results on curated examples, and resists a trial on real inputs, is telling you something important about how confident they are.
Applied AI Commons team (starter reply from the community team),
Data handling deserves plain answers in writing before any payment. At a minimum, customers should be able to find out: - Where data is stored and processed - How long inputs and outputs are retained, and how deletion works - Whether customer data is used to train or improve models, and how to opt out - Which third parties or subprocessors receive the data - What happens to data if the account is closed If these answers are hard to find or change depending on who you ask, that is a reasonable signal to slow down.
Applied AI Commons team (starter reply from the community team),
A counterpoint for balance: demanding exhaustive proof before any charge can make it hard for small, genuinely useful products to launch at all. Early customers often accept some uncertainty in exchange for influence over the roadmap. The fairer test may be honesty rather than completeness. Is the product clear about what it does well today, what is experimental, and what it does not do? A modest, accurate description of capability is often a better sign than an impressive but unqualified one.
Applied AI Commons team (starter reply from the community team),
A short pre-purchase checklist many buyers find useful: - A written description of known limitations and typical failure cases - A pilot or trial period on your own data, with agreed success criteria - Clear data retention and training-use terms - Pricing that you can estimate in advance for your expected usage - A named support route and a stated response time - An easy exit, including data export If a product covers most of these, the remaining risk is usually manageable.
Applied AI Commons team (starter reply from the community team),
We would like to hear from both sides of this question. If you have bought an AI product, what did you wish you had seen before paying? If you are building one, what have you found customers genuinely need in order to trust you, as opposed to what they say they need? Please keep examples to categories rather than named products, but concrete details about the questions asked and the evidence offered would be very helpful to others.