Drive Real Value: Adding AI Decisioning to Your Customer Engagement
Published on October 01, 2025/Last edited on October 01, 2025/4 min read
Published on October 01, 2025/Last edited on October 01, 2025/4 min read
AI decisioning helps brands go beyond lifecycle marketing campaigns based on segmentation and business rules to achieve 1:1 personalization at scale while optimizing any business KPI.
But there’s one important way to get even more value out of this AI capability. That’s by integrating AI decisioning directly to your customer engagement platform, rather than only integrating it to your data warehouse or CDP.
I’ve seen some brands focus on the integration between the AI decisioning layer and their data layer (data warehouse or CDP). However, my experience as a practitioner (co-founder of AI decisioning company OfferFit, acquired by Braze, and lead data scientist at BCG before that) taught me that a tight integration with the Customer Engagement Platform is much more important for increasing the quality of AI decisioning. It improves data quality, allows direct control of real-time channel activation, and eliminates a frequent source of silent errors.
AI decisioning relies on a type of AI called reinforcement learning. A reinforcement learning agent learns from its environment – it iteratively chooses from a set of actions, receives some reward in response, and then chooses another action based on what it’s learned. As the agent learns from the rewards it receives, it updates its policy for selecting future actions with the goal of maximizing the reward achieved over time.
In a marketing context, the “actions” are choices like channel, message, or time of send. The “reward” comes from the outcome the marketer is trying to achieve–for a renewal campaign, for example, might be the incremental revenue per user generated from a new contract.
The agent learns from customer behavior: from what customers open, click on, or purchase. As the agent learns what works for a particular kind of customer, it will then do more of the things that work and less of the things that don’t for similar customers.
Reinforcement learning agents are powerful, but they are very sensitive to the reward “signal” they receive. Just like a scientist conducting an experiment, the agent needs to know what worked. Any errors in the customer event data, or any missing data, could mistrain the agent and lead to catastrophic dips in performance.
Put another way, the AI agent’s decisions are only as good as its data. As the saying goes “garbage in, garbage out.” When the AI decisioning agent has direct access to customer events (opens, landing page actions, clicks, conversions, etc.), it eliminates a significant source of errors. Conversely, without a direct integration with the Customer Engagement Platform, failures in ETL pipelines and resultant data errors are a constant threat.
When the AI decisioning system is integrated with the engagement platform, activation of the agent’s decisions (e.g., “send template 137 with discount code 21 via email at 3:40pm”) can be done directly at the specific time and via the specific channel chosen by the agent. In the absence of a tight integration between the AI decisioning agent and the platform actually doing the activation integration, latency is introduced. Even more importantly, some intended activations can fail silently.
If the AI decisioning system isn’t directly integrated with the customer engagement platform executing the recommendations, a downstream guardrail can block a recommendation from being executed without immediate visibility to the AI decisioning agent. This is a particularly difficult form of error to diagnose without full, low-level access by the AI decisioning system to the activation logs of the engagement platform. If decisions fail to execute without the AI agent realizing it, an erroneous non-conversion would be attributed to the decision even though the message was in fact never sent. This would bias the data feeding back to the AI decisioning agent, confusing the agent and degrading performance.
Connecting your AI decisioning layer and your customer engagement platform minimizes this kind of noise and these kinds of errors in the data, which is crucial for complex AI decisioning systems like BrazeAI Decisioning Studio to work as designed.
LATAM Airlines turned to Braze to automate the process of choosing the best individual follow-up for potential customers who search for flights but didn’t end up completing the transaction. The goal was to achieve more conversions on their email program. By leveraging BrazeAI Decisioning Studio, marketers realized a 45% uplift, leading to an additional $10 million in bookings.
Visit the BrazeAI Decisioning Studio™ page to learn more about AI decisioning for the enterprise.
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