Put AI decisioning behind every marketing move

AI decisioning uses reinforcement learning agents, AI agents that experiment to optimize which choices — such as message, offer, channel, timing, day, and frequency — are most effective for each customer.

Unlike marketing methods based on static A/B testing and business rules, AI decisioning agents adapt in real time. They continuously experiment and autonomously learn from each interaction, optimizing campaigns dynamically instead of requiring manual rules or fixed test windows.

No, AI decisioning agents make 1:1 decisions at the individual customer level, using all available zero and first-party data to choose relevant experiences for each individual.

AI decisioning can optimize the most important KPIs for your business, including revenue, profit, and customer lifetime value (CLV). It can be used to improve outcomes like cross-selling, cart completion, contract renewals, and to reduce churn.

Marketers maintain control by setting custom guardrails and goals. You decide what content, offers, or actions are available for the AI agents to test. The AI then experiments within those boundaries, ensuring outputs stay aligned with your brand and compliance needs.

BrazeAI Decisioning Studio™ supports cross-channel engagement, including email, push notifications, SMS, in-app messages, and more. The agents automatically select the best channel mix for each individual based on past behaviors and performance.

No, BrazeAI Decisioning Studio™ can integrate into any modern martech stack as a decisioning layer.