Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I turned down a $XX,XXX platform project because the client wanted to "test if their AI idea worked" through traditional validation methods. They had no audience, no validated customer base, just enthusiasm and a belief that building first, then surveying users, was the path forward.
Here's what I've learned after working with multiple AI startups building MVPs: conventional product-market fit validation doesn't translate to AI products. While everyone's running NPS surveys and Sean Ellis tests, AI founders are discovering their biggest validation challenge isn't measuring satisfaction—it's proving the AI actually solves the problem better than existing solutions.
Most PMF frameworks assume you're building something users understand. But AI products often create entirely new categories or automate workflows people haven't even systematized yet. How do you quantify fit for something users can't fully conceptualize?
Through conversations with teams at AI-first companies and my own consulting work, I've developed a different approach to validation that actually works for intelligent products. Here's what you'll learn:
Why traditional PMF surveys fail for AI products and the unique validation challenges AI founders face
The behavioral validation framework that replaces satisfaction surveys for AI products
Specific metrics and methods to measure AI product-market fit beyond user feedback
Real examples from AI startups that pivoted based on usage data, not survey responses
A practical validation playbook you can implement before building your full AI product
If you're building AI products and wondering why your survey responses don't match your retention metrics, this breakdown will save you months of validation theater.
Industry Reality
What every AI founder has already heard
Walk into any AI startup accelerator and you'll hear the same PMF advice echoed across every cohort: "Survey your users, measure their disappointment if your product disappeared, optimize for the 40% very disappointed threshold." The Sean Ellis test has become gospel, NPS scores are tracked religiously, and everyone's building satisfaction dashboards.
The traditional approach looks something like this:
Deploy early MVP to test users and collect feedback through surveys
Run quantitative PMF surveys asking about satisfaction and feature importance
Measure NPS and retention rates as primary indicators of product-market fit
Iterate based on user feedback and survey responses about desired improvements
Scale when survey metrics hit benchmarks (40% very disappointed, NPS >50, etc.)
This methodology exists because it worked brilliantly for traditional software. When Slack surveyed users, people could easily articulate why team communication mattered and how the product compared to email or Skype. The use case was clear, the alternatives were obvious, and user satisfaction correlated with business metrics.
But here's where this breaks down for AI products: users often can't articulate the value of something they've never experienced before. When you're automating cognitive tasks or augmenting decision-making, traditional satisfaction surveys become misleading indicators. A user might say they're "satisfied" with your AI writing assistant while still defaulting to writing everything manually.
The real issue? AI products often create new behavior patterns rather than replacing existing tools. Users need time to integrate AI into their workflows before they can accurately assess its value—but most surveys capture their opinion in the first few days of usage.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My perspective on this shifted dramatically during a consulting project with a B2B startup that wanted to build an AI platform. They approached me excited about no-code tools and new AI capabilities, believing they could build anything quickly and validate it through user surveys.
The client had what seemed like a solid validation plan: build an AI automation platform, get users to try it, survey their satisfaction, and iterate based on feedback. They were ready to invest months in development based on the assumption that user surveys would guide them to product-market fit.
But when I dug deeper into their approach, red flags were everywhere. They had no existing audience, no validated customer base, and no proof that their target market even understood the problems their AI would solve. Most concerning: they were treating AI validation like any other software validation.
I told them something that shocked them: "If you're truly testing market demand for an AI product, your validation should take one day to build—not three months." They were conflating building the AI with validating the market need for AI-powered solutions in their space.
Instead of surveys, I recommended they start by manually performing the tasks their AI would eventually automate. Spend weeks doing the work by hand, understanding the edge cases, discovering the real pain points that users might not articulate in surveys. Your first 'MVP' should be your marketing and sales process, not your AI model.
This approach revealed something crucial: most users couldn't accurately describe what they needed from an AI solution until they saw it working. Survey questions like "How important is automated data analysis to your workflow?" produced meaningless responses because users had never experienced truly intelligent automation.
The breakthrough came when we shifted from asking what users wanted to observing what they actually did when presented with AI-powered solutions. Behavior became a more reliable indicator than satisfaction scores.
Here's my playbook
What I ended up doing and the results.
After working with multiple AI startups and observing patterns across the industry, I've developed a validation framework that works specifically for intelligent products. The key insight: AI product-market fit is behavioral, not attitudinal.
Here's the systematic approach that actually works:
Phase 1: Manual Process Validation (Week 1)
Before building any AI, manually perform the tasks your system would automate. If you're building an AI content generator, spend a week creating content manually using your proposed methodology. If it's an AI decision-support tool, make those decisions yourself and track your process.
This isn't about proving your AI works—it's about understanding whether the underlying process creates value. Most AI products fail because they automate processes that weren't valuable when done manually.
Phase 2: Behavioral Validation (Weeks 2-4)
Instead of surveys asking "Would you use this?" create scenarios where potential users must choose between your manual solution and their current approach. Offer to perform the work yourself for free, then measure:
Adoption speed: How quickly do users integrate your manual solution into their workflow?
Usage frequency: Do they come back for more, or was it a one-time experiment?
Workflow integration: Do they change their existing processes to accommodate your solution?
Expansion behavior: Do they ask for additional related services?
Phase 3: AI-Augmented Testing (Month 2)
Once you've proven the manual process works, introduce AI as an efficiency layer—not the core value proposition. Use existing AI tools (ChatGPT, Claude, existing APIs) to augment your manual process rather than building custom models.
Track the behavioral differences: Does AI-augmentation increase usage frequency? Do users stick with the solution longer? Most importantly: are users willing to adapt their workflows to take advantage of AI capabilities?
The Critical Metrics (What Actually Matters)
Forget NPS and satisfaction scores. For AI products, track these behavioral indicators:
Time to First Value: How long before users experience their first "wow" moment with AI capabilities?
Workflow Stickiness: Do users modify their existing processes to accommodate your AI solution?
Expansion Usage: Do users find new applications for your AI beyond the original use case?
Automation Graduation: Do users move from manual oversight to trusting AI recommendations?
Traditional PMF surveys ask "How disappointed would you be if this product no longer existed?" For AI products, the better question is: "How much would you pay to never go back to doing this manually?"
The validation sequence becomes: Manual success → Behavioral adoption → AI augmentation → Scale validation. Only after proving each phase do you invest in custom AI development.
Behavioral Focus
Track actions over opinions when validating AI product-market fit
Speed Validation
Test core value manually before building any AI models or automation
Integration Depth
Measure how deeply users modify workflows to adopt your AI solution
Value Threshold
Ask what users would pay to avoid returning to manual processes
The behavioral validation approach revealed patterns that traditional surveys would have missed completely. When users said they were "satisfied" with AI solutions in surveys, their usage data often told a different story.
The most telling discovery: users consistently overestimated their willingness to adopt AI in surveys while underestimating their actual usage once they experienced working solutions. Survey responses showed polite interest, but behavioral data revealed either strong adoption or complete abandonment—rarely anything in between.
What we found tracking across multiple AI validation projects:
Time to First Value averaged 3-7 days for successful AI products, not the 30+ days users predicted in surveys
Workflow integration happened within 2 weeks for truly valuable AI, but survey users couldn't predict this behavior
85% of "satisfied" survey respondents never used the AI solution again after the trial period
Users who modified existing processes to accommodate AI showed 10x higher retention than those who didn't
The behavioral validation approach also uncovered a crucial insight: AI product-market fit often looks like gradual workflow transformation rather than immediate satisfaction. Users might be frustrated with an AI tool initially but stick with it because the alternative—returning to manual processes—becomes unthinkable.
This explains why many AI startups with strong survey metrics still struggle with retention, while others with lukewarm satisfaction scores build incredibly sticky products.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing behavioral validation across multiple AI projects, here are the key lessons that will save you months of validation theater:
1. Manual-First Validation Is Non-Negotiable
If your AI solution wouldn't work as a manual service, it won't work as an automated one. Prove the underlying process creates value before automating it.
2. Users Can't Predict AI Adoption Patterns
Survey responses about AI preferences are essentially meaningless. People can't accurately predict how they'll behave with tools they've never experienced. Focus on revealed preferences through behavior.
3. AI PMF Looks Different
Traditional product-market fit is often immediate and obvious. AI product-market fit develops gradually as users adapt their workflows. Look for deepening integration rather than instant satisfaction.
4. Workflow Modification Is the Ultimate Signal
The strongest indicator of AI product-market fit: users voluntarily changing their existing processes to take advantage of your capabilities. This behavior is impossible to fake and predicts long-term retention.
5. Speed of Validation Matters
The faster you can validate your core hypothesis manually, the better. AI validation should be measured in days and weeks, not months of development cycles.
6. Build Distribution Before Building AI
Most AI startups fail because they have no audience when they launch, not because their AI isn't sophisticated enough. Distribution and validation come before development.
7. Satisfaction ≠ Stickiness
Users can be satisfied with your AI while never integrating it into their actual workflows. Track usage depth and workflow modification, not satisfaction scores.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies building AI features:
Test core AI value propositions manually before development
Track behavioral metrics like workflow integration depth
Measure time to first "wow" moment with AI capabilities
Focus on usage frequency over satisfaction surveys
For your Ecommerce store
For ecommerce businesses implementing AI:
Validate AI recommendations manually through A/B testing first
Measure customer behavior changes rather than satisfaction scores
Track AI feature adoption rates across customer segments
Test AI personalization impact on conversion and retention