Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, I watched a startup founder proudly show me their "AI-powered user personas" - basically ChatGPT-generated profiles that read like everyone else's. "Meet Sarah, 32, busy marketing manager who loves efficiency." Sound familiar?
Here's the uncomfortable truth: most AI product personas are complete fiction. They're based on assumptions, competitor research, and wishful thinking rather than actual user behavior with intelligent systems.
After building personas for multiple AI product launches, I've learned that traditional persona development fails spectacularly when applied to AI products. Why? Because AI changes user behavior in unpredictable ways, and most founders are still thinking like they're building traditional software.
In this playbook, you'll discover:
Why conventional persona templates break down for AI products
My systematic approach to identifying AI-specific user behaviors
How to build SaaS personas that predict AI adoption patterns
The critical difference between AI early adopters and mainstream users
A validation framework that actually works for intelligent products
Industry Reality
What the startup world preaches about AI personas
Walk into any startup accelerator or read any product development blog, and you'll hear the same advice about AI product personas:
"Just add AI use cases to your existing personas" - as if AI is just another feature
"Focus on efficiency and automation benefits" - because that's what surveys say people want
"Target tech-savvy early adopters first" - the same advice for every new technology
"Use demographic and psychographic segmentation" - traditional B2B playbook applied to AI
"Survey users about their AI preferences" - because people definitely know what they want from technology they've never used
This conventional wisdom exists because it worked for traditional software. You could predict that "busy professionals want time-saving tools" and build accordingly. The market was more predictable, and user behavior followed established patterns.
But here's where it falls apart: AI fundamentally changes how people interact with software. A marketing manager who's never used AI-powered content generation doesn't know they'll spend 3x longer on prompt engineering than actual content creation. A sales rep can't predict they'll trust AI-generated insights less than manual research, even when the AI is more accurate.
The result? Startups build for imaginary users and wonder why their beautifully crafted AI features sit unused. Product-market fit becomes impossible when your personas are based on fiction rather than reality.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came when working with a B2B startup building an AI-powered customer support tool. We'd done everything "right" - detailed buyer personas, user journey mapping, feature prioritization based on persona needs. The personas looked professional, complete with stock photos and carefully crafted pain points.
The startup was targeting two main personas: "Overwhelmed Support Manager Sam" who needed to reduce ticket volume, and "Efficiency-Focused Operations Director Dana" who wanted to cut support costs. Both personas, according to our research, were "excited about AI automation" and "frustrated with current manual processes."
We launched the product with confidence. The results were... educational.
Sam turned out to be terrified of AI replacing his team, not excited about automation. Despite saying he wanted efficiency in surveys, he actively avoided features that might eliminate jobs. Dana, meanwhile, was enthusiastic about AI in principle but had zero patience for the setup required to make it work effectively.
Here's what really broke my brain: our highest-engagement users weren't support managers or operations directors at all. They were customer success managers using our tool for completely different purposes - analyzing customer feedback patterns to predict churn. This use case never appeared in any of our persona research.
That's when I realized we were building personas for a world that doesn't exist. AI doesn't just solve existing problems differently - it creates entirely new user behaviors and use cases. The traditional approach of interviewing people about their current workflows and adding "AI magic" was fundamentally flawed.
This experience forced me to completely rethink persona development for AI products. Instead of asking people what they wanted from AI, I needed to observe how they actually behaved when AI was introduced into their workflow.
Here's my playbook
What I ended up doing and the results.
After that humbling experience, I developed a completely different approach to building personas for AI products. Instead of starting with demographics and surveys, I start with behavioral observation and AI-specific interaction patterns.
Phase 1: AI Behavior Mapping
I begin by identifying how people actually interact with AI tools in the wild, not how they say they want to interact with them. This means:
Analyzing usage data from existing AI tools in the space
Observing user sessions with AI prototypes or competitors
Tracking prompt patterns and iteration behaviors
Measuring trust indicators (when users accept vs. modify AI outputs)
For that customer support startup, this revealed three distinct AI interaction patterns: Validators (people who fact-check everything), Delegators (people who want AI to handle entire workflows), and Collaborators (people who treat AI as a thinking partner).
Phase 2: The AI Comfort Spectrum
Traditional personas focus on job titles and company size. For AI products, I map users across an "AI Comfort Spectrum" instead:
AI Natives: Already using multiple AI tools, understand limitations
AI Curious: Interested but need significant hand-holding
AI Skeptics: Will use AI if it's invisible or provides undeniable value
AI Resisters: Actively avoid AI due to job security or ethical concerns
This spectrum cuts across traditional demographics. I've seen 25-year-old marketing coordinators who are AI Resisters and 55-year-old CFOs who are AI Natives. Job title tells you almost nothing about AI adoption behavior.
Phase 3: Workflow Integration Points
Instead of mapping generic "user journeys," I identify specific moments where AI can integrate into existing workflows without disruption. This involves:
Shadow sessions with target users in their actual work environment
Identifying "AI-ready moments" - tasks that are repetitive, data-heavy, or creative
Mapping decision points where users need external input or validation
Understanding handoff points between human and AI work
Phase 4: The Reality Test
The final step is what I call the "Reality Test" - building minimal prototypes and observing actual behavior rather than relying on stated preferences. This typically reveals that:
People use AI differently than they say they will
The most valuable use cases often aren't the obvious ones
Resistance points are different from what surveys suggest
Value perception changes dramatically after hands-on experience
For the customer support startup, this process revealed that our real persona wasn't "Support Manager" at all - it was "Data-Driven Customer Success Manager who uses AI to identify patterns in customer feedback." This insight completely changed our product roadmap and messaging, leading to much better market fit.
Behavioral Patterns
Focus on how users actually interact with AI tools rather than their stated preferences or job descriptions
Trust Indicators
Map when users accept AI outputs versus when they modify or reject them - this reveals comfort levels
Workflow Moments
Identify specific points where AI naturally fits into existing workflows without major disruption
Reality Testing
Build prototypes and observe actual behavior rather than relying on surveys about hypothetical AI usage
The behavioral observation approach led to dramatically better product-market fit. Instead of building for imaginary "AI-enthusiastic" users, we built for real behavioral patterns we could observe and measure.
The customer support startup pivoted based on these insights and saw 3x higher feature adoption when we targeted the actual behavioral personas rather than the demographic ones. More importantly, user retention improved significantly because we were solving problems people actually experienced with AI tools.
What surprised me most was how wrong our assumptions were about AI adoption. The people who claimed to be "excited about AI" in surveys were often the most resistant in practice, while skeptics became power users once they saw concrete value in their workflows.
This approach has now become my standard framework for any AI product launch. Instead of asking "Who would want AI?" I ask "Who already demonstrates compatible behavioral patterns?" The difference in accuracy is remarkable.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from developing personas for AI products:
Behavior trumps demographics every time. A person's AI interaction style matters more than their job title or company size.
AI creates new user categories that don't exist in traditional software. You can't just add AI features to existing personas.
Trust patterns are the most important indicator of AI product success. Map when people accept vs. reject AI outputs.
The highest-value users often aren't the obvious targets. Shadow usage reveals unexpected applications and user types.
AI resistance isn't always about technology comfort. Often it's about job security, creative control, or workflow disruption.
Prototype early and observe real behavior. What people say they'll do with AI differs drastically from what they actually do.
Integration points matter more than features. Find where AI naturally fits into existing workflows rather than forcing new processes.
What I'd do differently: Start with behavioral observation from day one instead of traditional market research. The time spent on surveys and demographic analysis would be better invested in building simple prototypes and watching real users interact with them.
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:
Map your users' current AI tool usage patterns
Identify "AI-ready moments" in existing workflows
Build behavioral personas, not demographic ones
Test AI integration points with minimal prototypes first
For your Ecommerce store
For ecommerce businesses integrating AI:
Observe how customers interact with recommendation engines
Map AI comfort levels across different customer segments
Focus on invisible AI that enhances rather than replaces human decision-making
Test personalization acceptance across different product categories