Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform with cutting-edge AI features. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Here's why: when I asked them which specific problems their AI features would solve for users, they couldn't give me a straight answer. They wanted AI recommendations, machine learning matching algorithms, and predictive analytics because "that's what modern platforms do." But they had no existing audience, no validated customer base, and no proof that anyone actually wanted their solution.
They were prioritizing AI features before validating if anyone cared about the underlying problem they claimed to solve.
This conversation sparked a deeper realization about AI feature prioritization that most startups get backwards in 2025. Instead of asking "What AI features should we build?" the real question should be "Which problems actually require AI to be solved effectively?"
After working with dozens of startups on AI integration strategies, here's what I've learned about prioritizing features that users actually value over impressive technology demonstrations.
In this playbook, you'll discover:
Why most AI feature roadmaps are built backwards
The validation framework I recommend before any AI development
How to identify which problems genuinely benefit from AI vs. good UX
A prioritization system that focuses on user value, not technical capability
Real examples of when to say no to AI features (even when you can build them)
Conventional Wisdom
What every startup thinks they need
Walk into any startup accelerator or browse Product Hunt, and you'll hear the same advice about AI features in 2025. The market is saturated with articles about "must-have AI capabilities" and "essential machine learning features for competitive advantage."
Here's what the industry typically recommends for AI feature prioritization:
Start with recommendation engines - Because personalization drives engagement
Add chatbots and conversational interfaces - AI customer support is the future
Implement predictive analytics - Users want to see future trends and insights
Build content generation features - Let AI write emails, summaries, and descriptions
Create smart automation workflows - Reduce manual work with intelligent systems
This conventional wisdom exists for good reasons. Successful AI implementations like Notion's writing assistant, Spotify's recommendations, and Grammarly's real-time corrections have proven that intelligent features can create genuine user value. Investors are funding AI-first companies, and users expect "smart" experiences.
The typical prioritization process looks like this: survey what competitors are building, identify trending AI capabilities, then figure out how to implement them in your product. Teams often start by asking "What AI features are possible with our data?" rather than "What problems do our users have that AI could uniquely solve?"
But here's where this approach fails in practice: it treats AI as a feature category rather than a problem-solving tool. The result? Products packed with impressive but irrelevant intelligent capabilities that users don't understand, don't trust, or simply don't need for their core workflow.
What you actually need is a framework for deciding when AI creates real value versus when it's just expensive technical theater.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The project I mentioned in the intro perfectly illustrates this backwards thinking. The founders came to me with a detailed specification: they wanted to build a platform for creative professionals with AI-powered matching between freelancers and clients.
Their feature wishlist was impressive: machine learning algorithms to analyze work portfolios, natural language processing to match project descriptions with freelancer skills, predictive analytics to forecast project success rates, and automated pricing suggestions based on market data.
On the surface, it seemed like a perfect use case for AI. Creative matching is subjective, there's tons of unstructured data, and personalization could genuinely improve outcomes. The technical implementation was definitely feasible - I'd built similar systems before.
But when I started digging into their user research, I discovered something crucial: they had no users to research. They'd built their entire feature prioritization around assumptions about what creative professionals needed, not actual problems they'd validated.
When I asked them to describe their target users' biggest pain points, they talked about "inefficient matching" and "lack of personalization." But they'd never actually spoken to freelancers or clients who had these problems. They were solving theoretical issues with expensive AI solutions.
That's when I realized why so many AI features fail: teams prioritize based on technical possibility rather than user necessity. They start with "We have data, so we can build recommendation engines" instead of "Our users waste 3 hours per week manually searching for relevant content."
I told them something that initially shocked them: "If you're truly testing market demand, your MVP should take one day to build—not three months."
Here's my playbook
What I ended up doing and the results.
Instead of helping them build an AI-powered platform, I recommended what I now call the Validation-First AI Prioritization framework. This approach flips the typical development process: validate demand manually before building intelligent solutions.
Here's the four-step process I walked them through:
Step 1: Manual Market Validation
Rather than building matching algorithms, I suggested they start manual outreach to both freelancers and potential clients. Create a simple landing page explaining the value proposition, then personally connect people through email introductions. This would test if the core problem - connecting creative professionals with relevant projects - actually existed.
Step 2: Process Documentation
While doing manual matching, document everything: what information do you need from both sides? What criteria actually matter for good matches? What questions come up repeatedly? This manual process reveals which parts genuinely benefit from AI versus good UX design.
Step 3: AI Opportunity Mapping
After processing 50+ manual matches, analyze which steps are most time-consuming, require pattern recognition across large datasets, or involve subjective decisions that could benefit from machine learning. Most importantly, identify which manual tasks users actually value versus which ones just feel like work to you.
Step 4: Minimum AI Implementation
Only after proving market demand and understanding the manual process should you consider AI features. Start with the single most painful bottleneck that genuinely requires intelligent automation. Often this is data processing, not user-facing features.
For the marketplace project, this framework revealed something interesting: the most valuable "AI" feature would have been simple text matching and filtering - basically enhanced search, not machine learning. The real user problems were trust, communication tools, and payment processing.
I've since applied this framework across multiple client projects, from SaaS startups building recommendation engines to e-commerce companies wanting personalization features. The consistent pattern: manual validation uncovers that most "AI problems" are actually workflow or UX problems in disguise.
Problem Validation
Test demand manually before building intelligent solutions
User Journey Mapping
Document which steps actually benefit from AI vs. good design
Minimum AI Surface
Start with one painful bottleneck, not comprehensive intelligence
Value Measurement
Track time saved and user satisfaction, not technical metrics
The results of this approach have been consistently positive across multiple projects. The marketplace founders I advised followed the manual validation process and discovered their assumptions were wrong - but in a good way. Creative professionals didn't need AI matching as much as they needed reliable payment processing and clear project scoping tools.
They pivoted to a simpler solution focused on trust and communication, launched in 2 weeks instead of 3 months, and started generating revenue immediately. Six months later, they had enough user data and validated demand to justify building intelligent features - but by then, they knew exactly which AI capabilities would drive real value.
This pattern has held across other projects: teams that validate manually first build more focused, successful AI features. They avoid the "everything needs to be smart" trap and instead create intelligence where it genuinely improves user outcomes.
The unexpected benefit? Manual validation often reveals that users' actual problems are simpler than you assume. Some challenges that seem perfect for AI - like content recommendations or automated categorization - turn out to work better with good search functionality and user-controlled filtering.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Validate demand before building intelligence - AI can't fix a problem no one has
Manual processes reveal true requirements - Do it by hand first to understand what actually needs automation
Start with user problems, not technical capabilities - "We have data" is not a reason to build ML features
Simple solutions often outperform complex ones - Enhanced search beats recommendation engines more often than you think
AI features should save time, not create new workflows - If users need training to benefit from your intelligence, you've prioritized wrong
Measure user value, not technical metrics - Accuracy scores matter less than time saved or tasks completed
Most "AI problems" are actually UX problems - Good information architecture often eliminates the need for machine learning
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Start with manual user research and workflow documentation
Identify time-consuming tasks that require pattern recognition
Build simple automation before complex AI features
Measure user time saved, not technical performance metrics
Focus on workflow enhancement over feature complexity
For your Ecommerce store
Validate personalization manually before building recommendation engines
Test content categorization with simple rule-based systems first
Focus on conversion optimization over engagement metrics
Start with existing customer behavior data, not external sources
Prioritize inventory and pricing intelligence over content generation