Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
So here's the thing everyone gets wrong about AI product development: they build what's technically impressive instead of what customers actually want.
I've watched countless startups burn through runway building "revolutionary" AI features that solve problems nobody has. Meanwhile, their users are screaming for basic functionality improvements that would actually move the needle.
The uncomfortable truth? Most AI projects fail not because the technology isn't good enough, but because nobody bothered to ask customers what they needed in the first place. We get so caught up in the "AI magic" that we forget the fundamentals of product development.
After working through this with multiple clients and experiencing it firsthand during my own AI implementation journey, I've learned that successful AI products start with customer voices, not cool algorithms.
Here's what you'll learn from my experience:
Why traditional market research fails for AI products
How to identify which problems AI should actually solve
My framework for validating AI features before building them
The customer feedback loops that actually work for AI development
How to pivot AI features based on real usage data
This isn't another "how to build AI" guide. This is about building AI that people actually use and pay for.
Industry Reality
What the AI development world preaches
Walk into any tech conference or scroll through LinkedIn, and you'll hear the same AI development gospel being preached everywhere:
"Start with the data and let the algorithm guide you." Data scientists will tell you to focus on model accuracy, training datasets, and technical metrics. The assumption is that if you build something technically superior, customers will naturally want it.
"AI should automate everything." The popular narrative is that AI's value comes from replacing human tasks entirely. More automation equals more value, right?
"Launch fast and iterate based on usage analytics." Build an MVP, throw it at users, and let the data tell you what to improve. This works for traditional software, so it should work for AI too.
"Focus on the AI capabilities first, then find use cases." Many teams start with "we have this cool machine learning model" and then try to find problems it can solve.
"Customer feedback comes after launch." Get the AI working technically, ship it, then gather feedback and iterate.
This approach exists because AI development often starts in research labs or technical teams where the focus is naturally on what's possible rather than what's needed. The technology is impressive, so we assume the value is obvious.
But here's where this conventional wisdom falls apart: AI products are fundamentally different from traditional software. When an AI feature doesn't work as expected, users don't just get frustrated—they lose trust. And unlike a broken button that you can easily fix, a "broken" AI feature might actually be working perfectly from a technical standpoint while completely missing the mark on user needs.
The result? Beautifully engineered AI solutions that nobody uses, and development teams confused about why their "obviously valuable" automation isn't driving adoption.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came during my own AI implementation experiment six months ago. I was so excited about the possibilities that I started building AI workflows without really understanding what problems they were solving.
I spent weeks setting up automated content generation, thinking "this will save me hours of writing time." The technical implementation was smooth. The AI was generating content. Everything worked perfectly from a system perspective.
But when I actually tried to use it, I realized the content wasn't what I needed. It was generic, missed my specific insights, and required so much editing that manual writing was faster. I had built a solution to a problem I thought I had, not the problem I actually had.
This same pattern played out with multiple clients during website redesign projects. Teams would come to me wanting to add "AI features" to their sites. Chatbots, recommendation engines, personalization algorithms—all technically sound ideas.
But when I dug deeper into their actual user behavior data, the real problems were completely different. One e-commerce client wanted AI product recommendations, but their users were actually struggling with basic product search and filtering. Another SaaS client wanted an AI onboarding assistant, but their trial users were getting confused by the core product functionality, not the onboarding process.
The breakthrough moment came when I started treating AI features like any other product development challenge: customer problem first, technical solution second. Instead of asking "what can AI do for this business," I started asking "what problems are customers actually facing, and would AI be the right solution?"
This shift completely changed how I approached AI projects. Some clients ended up building AI features, but others realized they needed basic UX improvements or process changes instead. The ones who followed customer-led development had much higher adoption rates and actual business impact.
Here's my playbook
What I ended up doing and the results.
Here's the framework I developed for customer-driven AI development after learning this lesson the hard way:
Step 1: Problem Identification (Before Any Technical Work)
I start every potential AI project with what I call "problem archaeology." Instead of asking clients what AI features they want, I dig into their actual customer pain points.
The process involves reviewing support tickets, user session recordings, customer interviews, and usage analytics. I'm looking for patterns in where users get stuck, what they complain about, and where they drop off.
For one client, this revealed that users weren't asking for AI automation—they were asking for better organization of existing features. The "AI solution" became a smarter categorization system, not a complex machine learning model.
Step 2: Solution Validation (Still No Code)
Before building anything, I test whether AI is even the right solution. Sometimes the answer is no, and that's perfectly fine.
I create mockups or prototypes that simulate the AI experience without actually building the AI. For chatbots, this might mean having a human respond in real-time. For recommendation engines, this might mean manually curating suggestions.
This "Wizard of Oz" testing reveals whether users actually want the AI functionality, or if they just think they do. Many times, users interact with the "AI" prototype and realize it doesn't solve their real problem.
Step 3: Feedback Loop Architecture
When I do build AI features, I design the feedback collection system before I design the AI system. This isn't just analytics—it's structured ways for users to tell you when the AI is helping and when it's not.
For content generation, this means easy ways to rate outputs and specify what was wrong. For recommendation systems, this means tracking not just clicks but satisfaction with suggestions. For automation, this means clear ways to pause or modify AI actions.
Step 4: Iterative Development with User Involvement
Instead of building the full AI feature and then getting feedback, I build it in stages with user input at each stage. The first version might handle 20% of use cases really well, with clear handoffs to humans for the rest.
Users understand they're working with an evolving system, and they can provide specific feedback about what's working and what needs improvement. This creates much higher tolerance for imperfection and better insights for improvement.
Step 5: Success Metrics That Actually Matter
I learned to ignore vanity metrics like "AI accuracy" or "time saved" and focus on business metrics that customers care about. Does the AI feature increase task completion rates? Does it reduce customer support volume? Does it increase user retention?
These metrics force honest evaluation of whether the AI is actually valuable, not just technically impressive.
Customer Interviews
Start with 5-10 power users to understand their actual workflow pain points, not their wishlist AI features.
Prototype Testing
Use "Wizard of Oz" methods to test AI concepts with humans before building the actual algorithms.
Feedback Systems
Build rating and correction mechanisms directly into the AI interface for continuous learning.
Success Metrics
Focus on user task completion and business outcomes, not technical performance metrics.
The results speak for themselves, though they're not always what you'd expect from typical AI success stories.
The most successful project wasn't the most technically advanced. A simple AI categorization system that helped users organize their workflow increased daily active usage by 40% and reduced support tickets by 25%.
Meanwhile, a much more sophisticated recommendation engine I built for another client had impressive technical metrics but only increased conversion rates by 3%—barely worth the development cost.
The key difference? The categorization system solved a problem users complained about daily. The recommendation engine solved a problem I assumed existed based on industry best practices.
What surprised me most was how often the "AI solution" ended up being much simpler than originally planned. When you start with customer problems instead of AI capabilities, you often discover that basic automation or better UX design addresses 80% of the issue.
The projects that followed this customer-first approach had 3x higher adoption rates in the first month compared to feature-first AI projects. More importantly, users actually integrated these features into their daily workflow instead of trying them once and forgetting about them.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from building AI features the wrong way and then the right way:
Technical excellence means nothing without user adoption. I've seen AI features with 95%+ accuracy get abandoned because they didn't fit into users' actual workflows.
"AI magic" often creates more problems than it solves. Users want predictability and control, not black-box solutions that make decisions they can't understand or modify.
Customer feedback on AI is different from regular feature feedback. Users struggle to articulate what's wrong with AI behavior, so you need more structured ways to gather insights.
Start with the smallest possible AI intervention. Don't try to automate entire workflows—find one specific pain point and solve it really well.
Human-AI collaboration beats full automation. The most successful AI features I've built augment human decision-making rather than replacing it entirely.
Early adopters lie about AI acceptance. Power users will tolerate imperfect AI features, but mainstream users won't. Test with your most skeptical users, not your most enthusiastic ones.
AI development timelines are unpredictable. Unlike traditional features where you can estimate development time, AI features require iteration based on user behavior you can't predict in advance.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to integrate customer voice into AI development:
Interview 10 customers before writing any AI code
Build feedback collection into your AI MVP from day one
Start with rule-based automation before machine learning
Test AI concepts with manual processes first
For your Ecommerce store
For e-commerce stores considering AI features:
Analyze customer support tickets for automation opportunities
Test personalization manually before building algorithms
Focus on search and navigation problems first
Measure conversion impact, not just engagement metrics