Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was brought in to consult for a B2B SaaS that had built an impressive AI-powered platform. Their metrics told a frustrating story that's becoming all too common in the AI space: lots of signups, zero engagement, and almost no conversions to paid plans.
The marketing team was celebrating their "success" with aggressive CTAs and paid ads driving signup numbers up. But I knew we were optimizing for the wrong thing. Most users were trying the product for exactly one day, then vanishing into thin air.
This experience taught me something counterintuitive: when you're building AI products, traditional onboarding wisdom doesn't just fail—it actively hurts your product-market fit discovery process. The solution? Sometimes the best onboarding strategy is to prevent the wrong people from signing up in the first place.
Here's what you'll learn from this real client case:
Why AI products need fundamentally different onboarding than traditional SaaS
How adding friction actually improved our trial-to-paid conversion rate by 340%
The "lovable MVP" framework I developed for AI product validation
Why cold traffic and AI products are a toxic combination
My step-by-step process for qualifying AI product users before they even see your interface
Most founders are treating their AI products like traditional software when they're actually selling something completely different. Let me show you what actually works in the AI era.
Industry Reality
What every AI founder has already heard
Walk into any startup accelerator or browse through YC's latest batch, and you'll hear the same onboarding advice repeated like gospel: "Reduce friction! Simplify your forms! Get users to their first 'aha moment' as fast as possible!"
The traditional SaaS onboarding playbook looks like this:
Minimize signup friction - Ask for just name and email
Progressive onboarding - Show features gradually
Quick wins first - Get users to experience value immediately
Guided tutorials - Walk them through your interface
Engagement tracking - Monitor feature adoption
This advice exists because it works beautifully for traditional software. If you're building a project management tool or a CRM, getting someone into your product quickly makes sense. They can immediately see folders, create tasks, and understand the value proposition.
But AI products are fundamentally different. You're not selling a tool—you're selling an outcome. Users don't care about your model architecture or training data. They care about whether your AI can actually solve their specific problem better than their current solution.
The conventional wisdom fails because AI products require context, data, and often significant behavior change to show their true value. When you optimize for quick signups, you get a flood of curious tire-kickers who bounce after seeing your interface instead of experiencing your AI's capabilities.
Every AI founder follows this playbook, then wonders why their product-market fit signals are so noisy. Time for a different approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client came to me with what seemed like a classic growth problem. They'd built an AI-powered analytics platform that could genuinely revolutionize how B2B companies understood their customer data. The technology was solid, the results were impressive, but their onboarding was broken.
Here's what their funnel looked like:
1,000+ weekly signups from paid ads and content marketing
90% bounce rate after the first session
2% trial-to-paid conversion (industry average is 15-20%)
Massive support load from confused users
The classic symptoms of AI product-market fit issues. But here's what made this case interesting: the 2% who did convert were absolutely obsessed with the product. Net Promoter Scores in the 80s, multiple referrals, expansion revenue—classic product-market fit signals.
My first instinct was to improve the post-signup experience. We built an interactive product tour, simplified the UX, added contextual tips. The engagement improved slightly, but nothing dramatic. The core problem remained untouched.
That's when I realized we were treating symptoms, not the disease. The issue wasn't that good prospects couldn't figure out the product—it was that 98% of signups weren't good prospects in the first place.
The AI required specific types of data, technical knowledge, and a particular business model to show value. But our "frictionless" onboarding was letting anyone with an email address create an account. We were drowning the few qualified prospects in a sea of confused browsers.
Most AI founders face this exact problem but attack it from the wrong end. They try to make their complex product simpler instead of making their audience more qualified.
Here's my playbook
What I ended up doing and the results.
I proposed something that made my client initially uncomfortable: make signup significantly harder. Instead of optimizing for conversion rate, we'd optimize for conversion quality.
Here's the complete qualification system I implemented:
Phase 1: Pre-Signup Qualification
Before anyone could even see a signup form, they had to complete a 3-minute qualification survey:
Company size and revenue range
Current analytics tools and team structure
Specific use case they wanted to solve
Timeline for implementation
Technical requirements and data sources
Phase 2: Conditional Access
Based on their answers, users got one of three paths:
Immediate Access - Perfect fit profiles (about 15% of applicants)
Educational Track - Close fit but need preparation (30%)
Alternative Resources - Not ready, redirected to content library (55%)
Phase 3: Contextual Onboarding
For qualified users, we created three different onboarding flows based on their use case:
Customer Segmentation Track - Sample data loaded, specific KPIs highlighted
Churn Prediction Track - Different interface, relevant case studies
Revenue Analytics Track - Custom dashboard, financial metrics focus
Phase 4: Progressive Value Delivery
Instead of showing all features, we revealed capabilities based on usage patterns and data quality:
Day 1-3: Basic insights with their actual data
Day 4-7: Predictive features unlocked
Week 2: Advanced customization and integrations
Week 3: Team collaboration and reporting features
The key insight: AI products need time and context to show their value. By pre-qualifying users and customizing their journey, we ensured they'd stick around long enough to experience the "wow" moment that only AI can deliver.
Qualification System
Multi-step qualification survey that filters prospects based on company size business model and technical readiness ensuring only qualified users access the AI platform.
Progressive Onboarding
Context-aware onboarding flows that adapt based on user's specific use case and data maturity rather than generic feature tours.
Conditional Access
Smart routing system that directs different user types to appropriate experiences - immediate access educational content or alternative resources.
Value Sequencing
Time-based feature unlocking that reveals AI capabilities progressively as users generate data and demonstrate engagement with core functionality.
The results were dramatic and counterintuitive:
Quantitative Results:
Signups dropped 60% (from 1,000 to 400 weekly)
Trial-to-paid conversion increased 340% (from 2% to 8.8%)
Support tickets decreased 70% despite higher engagement
Average session time increased 450%
Feature adoption rates improved 200-300% across all core features
Qualitative Changes:
More importantly, the quality of user feedback transformed. Instead of "I don't understand what this does" we started getting "This insight changed how we think about our customers" and "Can we integrate this with our existing workflow?"
The client initially panicked when signup numbers dropped, but monthly recurring revenue actually increased 180% within three months because we were converting the right people instead of confusing the wrong ones.
What surprised me most was the impact on product development. With clearer signals from qualified users, the team could prioritize features that actually moved the needle instead of chasing vanity metrics from unqualified signups.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experiment taught me five crucial lessons about AI product onboarding that apply way beyond this single case:
AI products are services, not tools. Traditional software shows its value immediately. AI products need time, data, and context to demonstrate their capabilities.
Qualification beats conversion optimization. Getting the right 100 users is infinitely more valuable than getting the wrong 1,000.
Context determines success. The same AI product can seem magical or useless depending on the user's situation and expectations.
Progressive disclosure works differently for AI. Instead of revealing features, reveal intelligence and insights as users provide more context.
Cold traffic and AI don't mix. AI products need warm, educated prospects who understand the problem space and have realistic expectations.
Support load is a leading indicator. If your AI product generates lots of confused questions, you have an audience problem, not a product problem.
The best onboarding happens before signup. Education and qualification should occur in your marketing and content, not in your product interface.
The biggest mindset shift: stop optimizing for departmental KPIs and start optimizing for the entire customer journey. Marketing shouldn't just generate signups—they should generate qualified, educated prospects who are ready to experience what AI can actually do.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI features:
Add qualification questions before AI feature access
Create separate onboarding flows for AI vs traditional features
Use progressive disclosure based on data quality not time
Track AI-specific engagement metrics beyond clicks
For your Ecommerce store
For ecommerce businesses implementing AI tools:
Qualify vendors based on your data maturity level
Demand customized onboarding for your specific use case
Insist on progressive implementation not all-at-once rollouts
Measure business outcomes not AI feature adoption rates