Our take on AI for experimentation

Statsig outlines an AI strategy for its experimentation platform centered on trustworthy, high-impact assistance rather than adding AI broadly for speed alone. The company sees AI as a way to reduce friction in self-service experimentation by consolidating data, connecting signals, explaining findings, and freeing data scientists from routine analysis, while acknowledging technical users’ concerns about hallucinations and unverifiable reasoning. Its approach emphasizes grounding AI outputs in real data with guardrails and transparent evidence, providing conservative and customizable controls over autonomous actions, and making AI optional so it augments rather than disrupts established workflows. Statsig intends to prioritize AI for more time-consuming tasks such as generating code, analyzing results, prioritizing test ideas, and answering program-level questions across many experiments, with the broader goal of helping teams improve both the efficiency and quality of their experimentation.

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