Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
OK, so you've built an AI product and now you're trying to figure out if people actually want it. You've probably been told to "survey your users" about a hundred times by now. But here's the thing that nobody talks about: most AI product validation surveys are complete garbage.
I've watched countless founders create beautiful Typeform surveys asking users "Would you pay for this AI feature?" only to get overwhelmingly positive responses that translate to exactly zero paying customers. The problem isn't that people are lying - it's that they genuinely don't know how they'll behave until they're actually in the situation.
After working with AI startups and seeing this pattern repeat over and over, I've developed a different approach to validation surveys that actually predicts real user behavior. In this playbook, you'll learn:
Why traditional survey questions fail for AI products specifically
The behavioral-based survey framework that actually works
How to design questions that reveal true willingness to pay
The follow-up process that turns survey responses into real validation
When to skip surveys entirely and use different validation methods
Let me show you the approach that's helped several AI startups avoid building features nobody actually wants.
Market Research
What every AI founder has been told about validation
If you've spent any time in startup circles or read the usual product management advice, you've heard the standard validation playbook. It goes something like this:
Create a survey asking potential users about their pain points
Ask willingness-to-pay questions like "Would you pay $20/month for this?"
Get demographic information to build user personas
Ask feature prioritization questions using ranking or rating scales
Collect contact information for follow-up interviews
This conventional wisdom exists because it works reasonably well for traditional software products where users understand their workflows and can articulate their needs clearly. The methodology comes from decades of consumer research and product management frameworks that assume people know what they want.
But here's where it falls apart for AI products: people have no mental model for how AI should fit into their workflow. They can't accurately predict how they'll use something they've never experienced before. Ask someone "Would you use an AI assistant to write your emails?" and they might say yes because it sounds useful. But will they actually trust it enough to send those emails? Will they spend time training it? Will they pay for it when the free trial ends?
The gap between stated preference and actual behavior is enormous with AI products because the value proposition is often abstract until experienced firsthand. Traditional surveys optimize for collecting opinions, but opinions about AI capabilities are largely worthless for validation.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this the hard way while working with a B2B startup developing AI-powered workflow automation. The founders had spent weeks creating what they thought was the perfect validation survey. Beautiful design, smart logic flows, questions about pain points, willingness to pay, feature preferences - the works.
The results looked amazing. Over 200 responses, 73% saying they'd "definitely" or "probably" pay for the solution, high pain point scores across all their target use cases. The founders were convinced they had product-market fit and started building immediately.
Three months later? Zero paying customers.
What happened was classic: people loved the idea of AI automation but had no real understanding of what implementing it would actually require. The survey asked "How much time do you spend on repetitive tasks?" and people said "too much." It asked "Would you pay $50/month to automate 80% of that?" and people said "absolutely." But it never asked the hard questions.
When we dug deeper with actual user interviews, the real story emerged. Users were worried about data security. They didn't trust AI to handle customer-facing communications. They had complex approval processes that couldn't be automated. They used legacy systems that didn't integrate well. None of this came up in the survey because the questions were focused on the fantasy, not the reality.
That's when I realized traditional validation surveys for AI products are asking the wrong questions entirely. We needed an approach that revealed actual behavior patterns, not just stated preferences.
Here's my playbook
What I ended up doing and the results.
After that failed experiment, I developed what I call the Behavioral Validation Survey framework specifically for AI products. Instead of asking what people want, it reveals what people actually do and how they're likely to behave in real situations.
The core principle: Past behavior predicts future behavior better than stated intentions, especially for AI products where users can't visualize the actual experience.
Here's the step-by-step framework I now use:
Step 1: Current Behavior Mapping
Instead of asking "Do you want AI to help with X?" I ask "Walk me through exactly how you currently handle X." Get specific: What tools do they use? Who's involved in the process? What happens when something goes wrong? This reveals the actual workflow you're trying to improve.
Step 2: Pain Point Validation Through Stories
Rather than rating pain points on a scale, ask "Tell me about the last time X process went wrong. What happened? How did you fix it? What did it cost you?" Real stories reveal real stakes. If they can't think of a recent example, the pain isn't that significant.
Step 3: Investment History Questions
This is the key insight: "What have you already spent money or time trying to solve this problem?" People who've already invested in solutions are exponentially more likely to pay for yours. If they haven't spent anything, they probably won't start with your AI product.
Step 4: Decision-Making Process Discovery
Ask "Who else would need to approve purchasing a solution like this? What's their biggest concern likely to be?" This reveals the actual buying process, not just individual interest. Most AI products fail because individual users love them but can't get organizational buy-in.
Step 5: Competitive Alternative Analysis
"If you couldn't use our solution, what would you do instead?" Their answer tells you your real competition and how strong their motivation is. If their alternative is "we'd probably just keep doing it manually," you might not have strong enough value proposition.
Step 6: The Implementation Reality Check
"What would have to be true about your data/team/processes for an AI solution to work?" This surfaces implementation barriers before you build. Many AI products fail not because they don't work, but because customers can't actually implement them.
The magic happens in the follow-up. Instead of just collecting survey responses, I immediately schedule calls with anyone who gives detailed, specific answers. The survey doesn't validate your product - it identifies people who might actually use it.
Behavioral Questions
Focus on what people currently do, not what they think they want. Past behavior predicts future adoption.
Investment History
People who've already spent money trying to solve this problem are 10x more likely to pay for your solution.
Implementation Reality
Surface the practical barriers before building. Most AI products fail on implementation, not functionality.
Decision Mapping
Understand the actual buying process, not just individual interest. B2B AI needs organizational buy-in.
Using this behavioral approach instead of traditional validation surveys completely changed the results for the AI workflow automation startup. When we re-surveyed their market with behavioral questions, the picture was dramatically different.
Only 12% of respondents could describe a recent, specific incident where manual processes had caused significant problems. Of those, only 3% had already tried to invest in solutions. But here's what mattered: those 3% became paying customers within 60 days.
The behavioral survey revealed the real market was much smaller than the original survey suggested, but it was also much more qualified. Instead of building for the 73% who said they wanted AI automation, we focused on the 3% who had already proven they'd pay for solutions. Revenue followed immediately.
More importantly, the implementation questions revealed that successful customers needed specific data formats and approval processes. This insight shaped the product roadmap to focus on integration capabilities rather than AI sophistication - a pivot that made all the difference.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson from this experience: AI product validation isn't about proving people want your solution - it's about finding people who've already proven they need one.
Behavioral questions beat hypothetical ones - "What do you currently do?" trumps "What would you want?"
Investment history is the strongest predictor - People who've spent money on the problem will spend money on solutions
Implementation barriers kill AI products - Surface organizational and technical constraints early
Follow-up calls are mandatory - The survey identifies prospects, conversations validate the product
Smaller qualified markets beat larger unqualified ones - 3% who will buy beats 73% who might want
Decision-making processes vary wildly - Individual enthusiasm doesn't guarantee organizational adoption
Stories reveal stakes better than ratings - Specific examples show real pain points and urgency
If I were doing this again, I'd spend more time on the pre-survey research to better understand the industry's specific workflow patterns. The more context you have before writing questions, the more revealing the behavioral questions become.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups developing AI features:
Focus on existing customers who've requested automation
Survey current workflow pain points before building AI solutions
Validate integration requirements early in the survey process
For your Ecommerce store
For ecommerce businesses considering AI tools:
Survey customers about their actual shopping behavior, not preferences
Focus on operational processes where you've already invested in solutions
Test AI features with high-value customer segments first