Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Six months ago, I was having the same conversation with every startup founder: "We need AI in our product, but we don't know what to build."
Sound familiar? You're not alone. After watching countless companies burn through budgets building AI features that users ignored, I realized most teams are approaching this backwards. They're asking "What can AI do?" instead of "What problems do our customers actually need solved?"
The wake-up call came when a SaaS client spent three months building an AI recommendation engine that nobody used. Meanwhile, their customers were screaming for better search functionality - a much simpler problem that AI could actually solve well.
Here's what I learned from working with over a dozen companies trying to integrate AI: the ones that succeed don't start with the technology. They start with customer pain points and work backwards to find where AI actually makes sense.
In this playbook, you'll discover:
Why most AI roadmaps fail before they even launch
My 4-step framework for validating AI features before you build them
Real examples of customer-driven AI implementations that actually worked
How to spot the difference between AI hype and genuine customer need
The metrics that matter when measuring AI feature success
Ready to stop building AI features in a vacuum? Let's dive into how AI implementation actually works when you put customers first.
Industry Reality
The AI-First Trap Every Company Falls Into
Walk into any startup office today and you'll hear the same refrain: "We need to be AI-native" or "Our competitors are using AI, so we need it too." The pressure is real, and it's creating some seriously misguided product decisions.
Here's what the industry typically tells you about AI roadmap planning:
Start with the technology: "What can GPT-4 or Claude do for our product?"
Look at competitors: "Company X added AI chat, so we need one too"
Think big first: "Let's build an AI that can automate everything"
Focus on the wow factor: "Users will be impressed by our AI capabilities"
Build and iterate: "We'll figure out product-market fit after launch"
This approach exists because AI vendors, consultants, and even well-meaning advisors are pushing a technology-first narrative. The messaging is seductive: "AI is the future, get on board now or get left behind."
VCs are asking about AI strategies in every pitch meeting. Customers are asking "Do you have AI?" without really knowing what they want it to do. The pressure creates a perfect storm of feature-building without purpose.
But here's where this conventional wisdom falls short: AI is not a strategy, it's a tool. And like any tool, it's only valuable when it solves a real problem that customers care about solving.
Most companies end up with expensive AI features that look impressive in demos but don't drive meaningful engagement or retention. They're solving problems that don't exist while ignoring the real pain points their customers face daily.
What if there was a different way? What if you could build AI features that customers actually requested, used, and paid more for?
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The moment that changed everything happened during a product review meeting with a B2B SaaS client. We'd spent three months building what we thought was a game-changing AI feature - a smart recommendation engine that would suggest optimal workflows based on user behavior.
The demo was flawless. The AI was sophisticated. The interface was beautiful. And when we launched it to their 5,000 active users, the usage rate was 0.3%.
Meanwhile, their support team was fielding dozens of tickets daily about users struggling to find specific documents and data within their platform. The search functionality was basic, often returning irrelevant results, and users were frustrated.
That's when it hit me: we'd built an AI solution for a problem customers didn't have while ignoring a massive pain point they complained about every single day.
This wasn't an isolated incident. I started seeing the same pattern across multiple clients:
A fintech startup built an AI investment advisor that nobody used because customers wanted better expense tracking
An e-commerce platform created an AI product description generator while customers begged for smarter inventory management
A marketing SaaS added AI content creation when users were actually struggling with basic campaign analytics
The problem wasn't the quality of our AI implementations - they were technically solid. The problem was we were building solutions to problems that existed in our heads, not in our customers' daily workflows.
This realization forced me to completely rethink how I approached AI roadmap planning. Instead of starting with "What cool AI stuff can we build?" I needed to start with "What problems are our customers actually struggling with that AI might help solve?"
The shift in mindset was simple, but the impact was massive.
Here's my playbook
What I ended up doing and the results.
After that painful lesson, I developed a systematic approach that puts customer needs at the center of every AI decision. Here's exactly how I now help companies build AI roadmaps that actually drive business results:
Step 1: The Customer Pain Audit
Before touching any AI technology, I spend 2-3 weeks diving deep into customer feedback. This isn't just reading support tickets - it's a comprehensive investigation:
Analyze support ticket themes over the past 6 months
Review customer churn exit interviews
Examine feature request patterns in your backlog
Conduct 10-15 customer interviews about their biggest daily frustrations
Survey your sales team about objections they hear most often
The goal is to create a ranked list of genuine customer pain points, not assumed ones.
Step 2: The AI Suitability Filter
Once I have the pain points, I run them through a simple filter to identify which problems AI can realistically solve well:
Good AI candidates: Pattern recognition, large data processing, personalization at scale, natural language processing, predictive analysis
Poor AI candidates: Simple automation, basic logic, creative strategy, emotional intelligence, complex decision-making requiring context
For the SaaS client I mentioned, their search problem was a perfect AI candidate - it involved processing large amounts of unstructured data and understanding user intent.
Step 3: The Minimum Viable AI Test
Instead of building full features, I create quick experiments to validate whether AI actually solves the problem better than existing solutions. This might be:
A prototype using existing AI APIs (OpenAI, Claude, etc.)
Manual simulation of what the AI would do
A/B testing AI-enhanced vs. traditional approaches on a small user segment
For the search problem, we built a simple prototype using OpenAI's embedding API to improve document search. We tested it with 50 power users for two weeks.
Step 4: Metrics That Actually Matter
Finally, I track success metrics that tie directly to customer value, not just AI performance:
Problem resolution rate (Did this actually solve the customer pain?)
Feature adoption rate (Are people actually using it?)
Support ticket reduction (Did it reduce the original complaints?)
Customer satisfaction scores for the specific workflow
Revenue impact through retention or upsells
This approach completely flips the traditional AI development process. Instead of building impressive technology and hoping customers find it useful, you're building solutions to real problems that happen to use AI.
Pain Point Research
Map customer complaints to identify where AI can genuinely help solve daily frustrations and workflow bottlenecks
Validation Framework
Test AI solutions with minimal viable experiments before committing to full development cycles
Success Metrics
Track problem resolution and customer satisfaction rather than just technical AI performance indicators
Implementation Strategy
Build AI features as solutions to existing problems rather than impressive technology showcases
The results speak for themselves. When we rebuilt that search feature using AI-powered semantic understanding, adoption jumped to 73% within the first month. More importantly, support tickets related to "can't find" issues dropped by 65%.
But the real validation came from customer feedback. Instead of crickets, we got messages like: "Finally! This is exactly what we needed" and "This saves me 20 minutes every day."
The timeline looked like this:
Week 1-3: Customer pain audit and interview process
Week 4-5: AI suitability analysis and prototype development
Week 6-7: Testing with 50 beta users
Week 8-12: Full implementation and rollout
Month 3: 73% adoption rate, 65% reduction in related support tickets
The unexpected outcome? This customer-driven approach actually made the AI implementation easier and cheaper. We had clear requirements, obvious success metrics, and excited beta testers who provided valuable feedback.
Contrast this with the original recommendation engine that took twice as long to build, had unclear success criteria, and ultimately provided zero customer value.
Since then, I've applied this framework to help over a dozen companies align their AI roadmaps with customer needs. The success rate is dramatically higher when you start with problems instead of solutions.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from aligning AI roadmaps with real customer needs:
Customer complaints are your AI roadmap: The most successful AI features I've built solved problems customers were already complaining about
Boring AI often wins: Flashy AI demos impress investors, but boring solutions to daily frustrations drive adoption
Start small, think big: Prove AI value on one specific problem before expanding to adjacent use cases
Manual simulation beats expensive prototypes: You can often test AI concepts manually before writing a single line of code
AI suitability isn't obvious: Not every customer problem needs an AI solution - sometimes simple automation works better
Success metrics must tie to customer value: Technical AI metrics are meaningless if customers aren't happier
Speed matters more than sophistication: A simple AI solution that ships quickly beats a sophisticated one that takes months
What I'd do differently: I wish I had started with customer interviews from day one. Too much time was wasted building solutions to assumed problems instead of validated ones.
This approach works best when you have direct access to customers and clear feedback channels. It's harder to implement if you're building horizontal tools or platforms where customer needs vary widely.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to align AI with customer needs:
Start with your support ticket analysis - common complaints = AI opportunities
Interview 10+ customers about daily workflow frustrations before building anything
Test AI concepts with existing APIs before custom development
For your Ecommerce store
For e-commerce stores implementing customer-focused AI:
Focus on search, recommendations, and customer service pain points first
Analyze customer journey drop-off points where AI could reduce friction
Test personalization features with small customer segments initially