AI Tools Usage & Sentiment Survey Template

An AI tools usage and sentiment survey is a structured questionnaire that captures which artificial intelligence tools respondents actively use, how frequently, how much they trust AI-generated outputs, and what they are willing to pay for AI features. It gives SaaS teams and researchers first-party adoption data rather than relying on vendor-reported growth statistics or analyst estimates.

Run it before a product roadmap cycle where AI features are under evaluation, ahead of a pricing or packaging decision for an AI tier, or when a competitor launches an AI-forward product and you need to understand how your audience perceives the category. It's also the right instrument before publishing a market report — primary data significantly strengthens the credibility of any AI adoption narrative.

Cover five areas: current tool usage and frequency, primary use cases by job function, trust in AI-generated outputs for different task types, perceived risk or concerns around data privacy, and willingness to pay for AI features at defined price points. Including a question on which tasks respondents would never delegate to AI surfaces the trust ceiling that most adoption surveys miss.

Use anchored price sensitivity questions rather than open-ended input — present three to four price points and ask respondents to rate each as too cheap, acceptable, or too expensive. Van Westendorp's Price Sensitivity Meter adapted to a 5–6 question sequence works well for SaaS contexts. Pair it with a feature-value ranking question so you can see which AI capabilities justify premium pricing in your specific market.

A general tech adoption survey measures tool awareness, usage frequency, and satisfaction across a broad category. An AI sentiment survey goes deeper on trust — asking how respondents feel about AI replacing tasks, whether they verify AI outputs, and how concerns about accuracy or bias affect their usage patterns. That trust dimension is what makes AI adoption research distinct and is the variable most predictive of long-term feature retention.