Why AI is Replacing One-Off A/B Testing With Always-On Optimization

Traditional A/B testing was built for a world where marketers had time to run one test, wait for results, and apply a winner. That world is gone. Neha Mittal, CEO and Co-founder of Just AI, makes the case that decisioning — dynamic, continuous optimization powered by AI — is replacing the legacy test-and-learn cycle. This session covers the foundational shift happening in how the most AI-forward marketing teams think about experimentation, with real examples of what always-on optimization looks like in practice. What you'll learn: - Why one-off A/B tests are a structural limitation, not just a speed problem - How agentic AI enables continuous optimization across messaging, timing, and content simultaneously - What the shift from creative decisions to decisioning infrastructure looks like for marketing teams - Real examples from AI-forward teams that have moved beyond legacy testing frameworks - How to start building toward always-on optimization without throwing out your current testing program