Migrate from Decision management to Decisioning migrate-to-decisioning

Custom metrics: Ability to use custom optimization metrics for AI models. This provides reporting interoperability with Customer Journey Analytics, standardizes reporting across both platforms, and improves data consistency and reliability. The seamless integration provides a clearer view of performance metrics and adds new capabilities such as creating simple metrics, publishing audiences, asking ad-hoc questions using Insight Builder, and scheduling reports.

Lift measurement: Ability to visualize explore vs exploit traffic in AI models. This enables marketers and data scientists to quantify how AI exploration improves long-term model performance and discoverability of new winning offers. Transparency into traffic allocation builds trust in AI decisions and empowers teams to optimize for both learning and performance over time. Learn more

AI formula builder: Ability to apply AI model score outputs to existing formula capabilities. This empowers marketers to seamlessly combine AI outputs with deterministic rules and weights for more nuanced optimization strategies, increasing control and flexibility while still leveraging machine-learning intelligence. Learn more

Sandbox copy: Ability to copy over objects between sandboxes (e.g., Dev to Prod). This simplifies deployment and testing workflows by enabling seamless migration of decision logic, offers, and configuration objects between environments, reducing setup time and minimizing human error. Learn more

Schema-based item catalog management: Ability to define and manage decision items directly to schema-linked datasets, enabling dynamic updates and simplified governance. This streamlines catalog management by syncing decision items with underlying data sources, ensuring content accuracy, enabling faster updates, and supporting governance at scale. Learn more

Location-agnostic decisioning: Ability to make decision logic reusable across placements/locations, decoupling decision selection from delivery. This promotes reusability and efficiency by allowing a single decision model to power multiple placements or surfaces (e.g., web, app, email), centralizing logic and accelerating cross-channel personalization efforts. Learn more

Reusable content fragments: Ability to define JSON or HTML content blocks (e.g., titles, headers, footers, CTAs) once and reference them within multiple offer objects. This streamlines content authoring and governance by allowing shared components to be centrally managed and automatically updated across offers. Learn more

Channel decisioning: Ability to use decision logic to determine the best channel for engagement (e.g., email vs. push vs. web), rather than just the best offer within a single channel. This enhances customer experience by optimizing where a message is delivered, not just what is delivered.

Message optimization: Ability to use AI or rule-based approaches to optimize message content for each profile, improving engagement and conversion outcomes. This enables marketers to tailor tone, imagery, and layout dynamically based on audience attributes and performance data.

Journey path optimization: Ability to determine which journey path a profile should follow, based on experimental results, real-time context, rules, and/or propensity to convert. This allows teams to intelligently route profiles through the optimal journey branch, ensuring the right cadence and content for each user.

Journey decisioning: Ability to arbitrate between multiple journeys when a profile qualifies for more than one, ensuring the most valuable or relevant journey is selected. This prevents message conflicts and over-messaging by ranking and selecting the highest-priority journey for each profile.

Policy enforcement: Business user empowerment to use features like Data Usage Labeling & Enforcement (DULE) and Consent within Decisioning, enabling privacy shield protection across the decisioning workflow. This ensures decisions automatically respect data usage policies and customer consent preferences.

Native messaging channel support: Integrated messaging and decisioning within a single framework across multiple channels (Code-based experience and Email currently available, other channels coming in H1 2026). Intuitive UI support allows users to insert decisioning components directly in message authoring workflows.

Experience Platform dataset lookup: Ability to upload and reference Adobe Experience Platform datasets directly within offer selection rules, ranking, and personalized offer content. This expands flexibility for personalization and targeting by allowing decision logic to use dynamic external data sources. Learn more

Scalability & performance: Architectural enhancement that moves decision computation from the hub to the edge, significantly reducing latency and improving throughput for high-traffic use cases.