Your CEO Saw a Demo. Now Everyone Wants 'AI Features.' Here's How to Prioritize.
2026-02-04 · 10 min read · Janaina Maia
Every product team I talk to has the same problem right now. The CEO came back from a conference excited about AI agents. A customer asked for "AI-powered insights." Engineering wants to play with the latest model. Marketing wants an AI chatbot. The intern suggested an AI-generated mascot.
OK, I made up the last one. But you get the idea.
The problem isn't a lack of ideas. It's a lack of framework for evaluating them. Most teams default to "what's easiest to build" or "what the loudest person in the room wants." Neither optimizes for user value.
The AI Feature Value Matrix
I score every AI feature idea against four dimensions. Each scores 1-5, multiply them together. It makes prioritization debates way more productive — or at least louder for the right reasons.
Dimension 1: Task Frequency × Pain
How often do users do this task, and how much does it suck?
An AI feature that saves 2 minutes on a task done 50 times a day beats one that automates a monthly task. Every time. This seems obvious but I watch teams ignore it constantly.
Dimension 2: AI Readiness
Can current AI actually do this reliably? Not in a demo — in production, at scale, with messy real-world data.
The gap between demo and production is where AI feature dreams go to die. Score this honestly.
Dimension 3: Risk Tolerance
What happens when the AI gets it wrong?
- Auto-formatting? Who cares, easy to fix.
- Report drafts? Annoying but catchable.
- Engineering assessments? Now we're talking real consequences.
Low risk tolerance doesn't mean "don't build it." It means you need more guardrails — which affects scope and timeline.
Dimension 4: Differentiation
Does this set you apart, or is it table stakes? A chatbot scores 1 here. An AI that auto-classifies soil types from borehole data when nobody else can? That's a 5.
A Real Example
Say you're building a geotechnical analysis platform:
A) Auto-classify soil types from borehole data
Frequency×Pain: 5 | AI Readiness: 4 | Risk: 3 | Differentiation: 4
Score: 240 → Build this first
B) AI chatbot for customer support
Frequency×Pain: 2 | AI Readiness: 4 | Risk: 3 | Differentiation: 1
Score: 24 → Skip it
See how the chatbot scores lowest despite being the "easiest" to build? That's the framework working.
The Traps Everyone Falls Into
- The demo trap: Features that demo well aren't always features that work well in daily use
- The CEO trap: Building what the CEO saw at a conference instead of what users need
- The parity trap: Building AI features because competitors have them, without validating user need
- The capability trap: Building around what your team CAN do instead of what users need done
- The moonshot trap: Going all-in on the transformative feature while ignoring high-value incremental wins
I fall into the moonshot trap myself sometimes. Big ideas are more fun to design. But the teams that win aren't shipping the most AI features — they're shipping the right ones.
After You've Prioritized: Apply JTBD
Once you know WHAT to build, sharpen the definition with Jobs to Be Done:
- What job is the user hiring AI to do? (Not "classify soil" but "give me confidence my foundation design is based on accurate data, faster")
- What does success look like? Time saved, accuracy maintained, decisions improved.
- What would make them fire the AI? Too many errors? Too slow? Doesn't explain itself? This shapes your quality bar.
Next time someone comes to you with "we should add AI to [thing]," pull out this matrix. It won't make the conversation easier. But it'll make it more productive.