Decision Log: Building AI Products Without Illusion

Context

In Q4 2024, the team faced a critical decision: should we add “AI-powered” features to stay competitive, or should we focus on solving real problems first?

The Dilemma

Option A: AI-First Approach

  • Add ChatGPT integration to every feature
  • Market as “AI-powered platform”
  • Risk: Building solutions looking for problems

Option B: Problem-First Approach

  • Identify real user pain points
  • Use AI only where it demonstrably helps
  • Risk: Appearing “behind” competitors

What We Decided

We chose Option B with a twist: validate AI use cases through small experiments first.

Key Principles

  1. No AI for AI’s sake - Every AI feature must solve a measurable problem
  2. Start small - Prototype with 10 users before scaling
  3. Measure ruthlessly - Track actual usage, not vanity metrics

The Results (3 months later)

What worked:

  • AI-assisted code review: 40% faster PR reviews
  • Automated test generation: 60% test coverage increase
  • Natural language search: 3x more feature discovery

What didn’t work:

  • AI chatbot for support: Users preferred direct docs
  • Auto-generated release notes: Too generic, no one read them
  • Smart notifications: Created more noise than value

Lessons Learned

1. Users don’t care about “AI”

They care about getting their job done faster. Don’t lead with the technology.

2. The boring problems are the best ones

AI works best on tedious, repetitive tasks. Not on creative or strategic decisions.

3. Human-in-the-loop always wins

Pure automation fails. AI as a copilot succeeds.

Framework for Future Decisions

Before adding any AI feature, ask:

  1. What specific task becomes 2x easier? (not “more intelligent”)
  2. Can we measure success in user behavior? (not just feedback)
  3. What happens when the AI is wrong? (failure modes matter)

Retrospective Thoughts

If I could redo this decision:

  • I’d start even smaller - 5 users, not 10
  • I’d time-box experiments - 2 weeks max, then kill or scale
  • I’d document failures publicly - We learned more from what didn’t work

Decision Date: January 15, 2025 Decision Makers: Product Team, Engineering Lead Status: Validated (3-month review completed) Next Review: April 2025