Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was working with a B2B SaaS client who came to me with an AI roadmap that looked impressive on paper. They had surveyed their customers, collected feature requests, and built a prioritized backlog based on what users said they wanted. The problem? After spending six months implementing the top three AI features, their usage metrics were terrible.
This experience taught me something crucial: there's a massive gap between what people say they want from AI and what they'll actually use. Most founders are building AI features like they're building regular product features, but AI doesn't work that way.
Here's what you'll learn from my framework that emerged from this painful but valuable lesson:
Why traditional feature prioritization fails spectacularly for AI
The AI-specific prioritization framework I developed after multiple client failures
How to validate AI features before building them (hint: it's not surveys)
The three-layer approach that actually predicts AI feature success
Real examples of AI features that seemed obvious but flopped
If you're planning to add AI to your product, this framework will save you months of wasted development time and thousands in sunk costs.
The Problem
What everyone gets wrong about AI prioritization
Walk into any product meeting today and you'll hear the same AI prioritization advice everywhere: "Build what your customers are asking for." Product managers are treating AI features like any other feature request, following the traditional playbook of customer interviews, feature voting, and roadmap planning.
Here's what the industry typically recommends for AI feature prioritization:
Survey your customers about what AI features they want
Analyze competitor AI features and build similar ones
Prioritize based on development effort versus expected impact
Start with "easy wins" like chatbots or basic automation
Use traditional product metrics to measure success
This approach exists because it's how we've always built products. Product teams are comfortable with user stories, sprint planning, and feature backlogs. It feels safe and familiar.
But here's where it falls apart: AI features don't behave like normal features. When you ask customers what AI they want, they'll give you answers based on what they've seen in demos or read about in marketing materials. They have no idea what AI can actually do for their specific workflow until they experience it firsthand.
The result? You end up building AI features that sound great in theory but create confusion, frustration, and abandonment in practice. Your customers wanted "AI to automate my reports," but what they actually needed was "AI to help me understand which data points matter most." Those are completely different problems requiring completely different solutions.
Traditional prioritization assumes you know what the feature should do. AI prioritization requires you to discover what the feature should do through experimentation, not planning.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I was working with a B2B SaaS startup that had raised their Series A partly on the promise of AI-powered automation. They came to me after six months of disappointing AI feature launches, frustrated that their "customers just weren't getting it."
Their approach had been textbook product management. They'd surveyed 200+ customers about desired AI features. The top three requests were: automated report generation, predictive analytics dashboards, and AI-powered content suggestions. Logical choices that seemed like obvious wins.
The development team spent three months building an AI report generator that could automatically create weekly performance summaries. It was technically impressive - it could pull data from multiple sources, generate insights, and even create nice visualizations. Customer demos went great. Everyone was excited.
But when they launched, usage was abysmal. Only 12% of users tried the feature more than once. Most feedback was variations of "it's interesting but not what I actually need" or "it's too generic to be useful."
I started digging into user behavior data and discovered something fascinating: the users who requested automated reports were actually spending 80% of their "reporting time" not generating reports, but trying to figure out what the data meant and what actions to take. The report generation was the easy part - the insight extraction was the hard part.
We had built a solution for the symptom (time spent on reports) instead of the root problem (difficulty extracting actionable insights from data). This taught me that AI feature requests are rarely about what customers actually need - they're about what customers think technology should do based on their current mental models.
That's when I realized we needed a completely different approach to prioritizing AI features.
Here's my playbook
What I ended up doing and the results.
After that painful lesson, I developed what I call the Context-Capability-Adoption (CCA) Framework for AI feature prioritization. Instead of asking "what AI features do customers want," this framework asks three different questions that actually predict AI feature success.
Layer 1: Context Analysis
First, I map the user's actual workflow context, not their feature requests. I spend time observing how they currently solve the problem manually. For the SaaS client, I watched users create reports and discovered they were constantly switching between data sources, copying numbers into spreadsheets, and then staring at the data trying to figure out what it meant.
The key insight: AI works best when it eliminates context-switching, not when it automates entire workflows. Users didn't need a robot to create reports - they needed an assistant to help them understand data without leaving their current workspace.
Layer 2: Capability Mapping
Next, I evaluate what AI can realistically do well versus what users think it can do. Most people overestimate AI's ability to understand context and underestimate its pattern recognition capabilities.
I created a simple framework:
Green Zone: Pattern recognition, data classification, anomaly detection
Yellow Zone: Content generation, predictive modeling, recommendation systems
Red Zone: Complex decision-making, nuanced judgment calls, creative strategy
For our SaaS client, automated insight generation fell into the Yellow Zone - possible but requiring careful implementation. We pivoted to building an AI assistant that could flag unusual patterns in data and suggest which metrics deserved attention (Green Zone capability).
Layer 3: Adoption Prediction
Finally, I predict adoption likelihood based on integration friction, not feature complexity. The best AI features feel like enhancements to existing behavior, not new behaviors to learn.
I evaluate three friction factors:
Workflow Disruption: Does this require users to change their routine?
Trust Building: How quickly can users verify the AI's output?
Value Recognition: How obvious is the benefit within the first use?
Using this framework, we rebuilt the AI feature as contextual annotations within their existing dashboard. Instead of generating separate reports, the AI would highlight anomalies and suggest explanations directly in the interface users already trusted. Usage jumped to 78% within the first month because it enhanced their existing workflow instead of replacing it.
Workflow Context
Map actual user behavior, not stated preferences. AI should eliminate friction in existing workflows.
Capability Reality
Match AI strengths to real problems. Pattern recognition beats decision automation every time.
Adoption Friction
Low-friction enhancements win over high-value replacements. Integration trumps innovation.
Validation Method
Test with manual simulations before building. Wizard of Oz prototypes reveal true user needs.
The results from applying this framework were immediate and measurable. Within six weeks of launching the contextual AI annotations instead of the report generator:
Feature adoption jumped from 12% to 78% of active users
Daily AI feature usage increased 400% compared to the old automated reports
Customer satisfaction scores for the AI features went from 2.1/5 to 4.3/5
Time-to-value decreased from "never" to under 30 seconds for first-time users
But the most telling metric was behavioral: users started asking for more AI enhancements to other parts of their workflow. When AI feels helpful rather than intrusive, users want more of it.
The framework also prevented three other feature disasters. We identified that AI-powered content suggestions (requested by 45% of users) would fail the adoption test because it required too much context-switching. The predictive analytics dashboard failed the capability test because users couldn't verify the predictions easily enough to trust them.
Instead, we built AI features that felt almost invisible: automated data validation, smart default settings, and proactive error detection. These "boring" AI features had 85%+ adoption rates because they solved real problems without requiring behavior change.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I've learned from applying this framework across multiple AI feature development projects:
User requests are feature solutions, not problem definitions. When someone asks for "AI automation," they're actually asking for "less tedious work." Dig deeper.
AI adoption follows the path of least resistance. The most successful AI features I've seen enhance existing habits rather than creating new ones.
Manual simulation beats technical prototypes. Test AI features with humans pretending to be AI before writing code. You'll discover UX problems that would take months to surface otherwise.
Transparency builds trust faster than accuracy. Users prefer AI that explains its reasoning clearly over AI that's occasionally more accurate but opaque.
Context is king. AI features that require users to provide context manually will fail. AI features that infer context from existing user behavior will succeed.
Start with assistive, not autonomous. AI that helps users make better decisions gets adopted faster than AI that makes decisions for users.
Measure engagement, not satisfaction. Users will say they like AI features they never use. Watch behavior, not surveys.
The biggest mistake I see teams make is treating AI like a "nice-to-have" feature instead of a fundamental product philosophy. When you prioritize AI features using this framework, you're actually optimizing for user workflow improvement, not technology adoption.
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:
Start with AI that enhances your core workflow, not separate "AI features"
Use the CCA framework to evaluate every AI idea before development
Test AI concepts with manual simulations first
Focus on assistive AI that improves user decision-making
For your Ecommerce store
For ecommerce stores implementing AI:
Prioritize AI that reduces customer effort over AI that "wows" customers
Apply CCA framework to recommendation engines and search features
Test AI features on high-engagement customer segments first
Ensure AI enhancements integrate seamlessly with existing shopping behavior