Data & experimentation
Design intuition is necessary but no longer sufficient.
How AI is impacting this skill
AI makes querying data and reading analytics dramatically more accessible. Designers who pair qualitative insight with quantitative evidence move from contributors to decision-owners.
The shifts that matter
- Natural-language interfaces to analytics remove the SQL barrier.
- Experimentation infrastructure is increasingly self-serve.
- Designers are expected to define and own success metrics.
- AI features specifically require new metrics: trust, accuracy, escalation rate.
What to do about it
- Define success metrics on the next design problem you take on.
- Use AI to write SQL or analytics queries against your product data.
- Run one small A/B test end-to-end and document what you learned.
- Build a habit of pairing every shipped change with a metric to watch.
Tools to try
Behavioural analytics with AI-assisted querying.
AI-assisted SQL and notebooks for deeper analysis.
Self-serve experimentation infrastructure.
Next move
See how the rest of your skills stack up
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Other skills
Synthesis is cheap. Good questions and good evidence aren't.
Framing the right problem matters more than ever.
AI surfaces happy paths fast. Edge cases are still on you.
Taste and systems thinking are the durable edge.
Systems are the bridge between AI output and shipped product.
Decisions move faster. Alignment is the bottleneck.
The skill that compounds everything else.