Decision Workbench

View, explore, and act on data-driven recommendations

Decision Workbench is an intuitive interface within Aera Decision Cloud™ that delivers prioritized and personalized recommendations for each user within your organization. As Aera Skills™ process data, apply intelligence, and identify opportunities, Decision Workbench delivers Aera’s recommendations to users in real time. Users can easily understand each recommendation’s context, evaluate trade-offs with powerful analytics, and take immediate action.

Decision Workbench also keeps a complete record of all the decisions made – that is, whether each recommendation was accepted, modified, or rejected – and stores that information in the Decision Data Model™. Capturing information about every decision allows relevant Aera Skills to learn and improve continuously over time, while also revealing opportunities to automate decisions for increased agility.

Decision-Workbench v001


  • Easily explainable recommendations: Each recommendation provides the best course of action from among available options analyzed by AI, allowing users to accept, modify, or reject the recommendation as circumstances demand. This explainability increases users’ trust in Decision Intelligence and leads to increased adoption.
  • View each recommendation’s projected impact: Each recommendation includes a summary of projected impact in terms of revenue, cost, and units – with the option to view a detailed analysis of how this impact is calculated. This transparency allows users to explore options, evaluate impacts, and weigh trade-offs before making a decision.
  • Rapid execution: Once a decision is made, Aera can write changes back into your transactional systems of record in order to execute the decision at the moment of maximum impact.
  • Continuous learning and improvement: Decision Workbench captures users’ end-to-end activity including the steps that were completed while rejecting or accepting recommendations. Aera continuously learns from this audit trail in order to improve recommendations over time.


  • Sort and prioritize recommendations by a variety of facets, including importance, financial impact, and urgency.
  • User-configurable interface can be personalized by role, responsibility, and user preferences.
  • Prescriptive, time-sensitive, and contextual recommendations present the impact of decisions and guidance for users
  • Transparent “glass box” AI shows you how each recommendation is calculated, from the predictive analytics to the machine learning algorithms used for forecasting.

See Aera in action.

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