Experimentation in the AI Era: Lessons from the Trenches

What You’ll Learn

Three things you’ll be able to do after this session
One hour. No fluff. You’ll leave with a concrete playbook you can apply to the next GenAI feature your team ships.

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Why traditional experimentation breaks in AI products: See the exact structural failure mechanisms—user spillage, SUTVA violations, and rapidly evolving models—that silently invalidate standard testing frameworks.
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How to measure success in AI-powered experiences: Learn practical frameworks to design robust metrics and accurately track performance, even when treatments and feedback loops change dynamically.
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What the future of experimentation looks like: Discover how to clear the shipping bottleneck using adaptive systems, multi-armed bandits, AI-assisted experiment design, and autonomous optimization.

Why This Topic Matters

The traditional testing playbook has become the ultimate shipping bottleneck.

GenAI accelerates code development, but static A/B testing frameworks slow down deployment. To enable high-velocity product shipping in the AI era, engineering and data teams must overcome three fundamental shifts:

1. The Continuous Model-User Flywheel
AI experiences are highly dynamic. Model predictions constantly shape user behavior, and that user feedback simultaneously updates the model. Static control vs. treatment groups simply cannot track a moving target.

2. SUTVA Failures & Signal Leakage
AI-generated content and behavior models create unexpected network effects. Once data or treatments spill across user buckets, classic statistical independence is broken, leaving you with compromised insights.

3. The Need for Ultra-High Experimentation Velocity
Waiting weeks for a standard test run forces teams to ship blind or stall innovation. Moving toward continuous optimization, adaptive testing, and autonomous systems is no longer optional—it’s a requirement.

Teams that build adaptive, closed-loop learning systems scale their shipping velocity.

Who This Is For

💻

Data Scientists &
ML Engineers
Building and shipping GenAI features in production.

📊

Product Managers &
Analytics Leads
Responsible for measuring the true impact of AI feature launches.

🚀

Engineering Leaders &
Tech Managers
Overseeing AI integration and demanding reliable measurement practices.

✦ Some familiarity with A/B testing concepts is helpful. No prior GenAI experimentation experience required.

Baig Academy

Baig Academy

What You’ll Learn

Why This Topic Matters

Who This Is For

What You’ll Learn

Why This Topic Matters

Who This Is For

Let’s map your team’s AI fluency plan.