Decision management refers to the process of designing, building, and managing automated decision-making systems that support or replace human decision-making in organizations. It integrates business rules, predictive analytics, and decision modeling to streamline and automate operational decisions. These systems combine business rules and potentially machine learning to automate routine business decisions and are typically embedded in business operations where large volumes of routine decisions are made, such as fraud detection, customer service routing, and claims processing.

Decision management differs from decision support systems in that its primary focus is on automating operational decisions, rather than solely providing information to assist human decision-makers. It incorporates technologies designed for real-time decision-making with minimal human intervention.

The roots of decision management can be traced back to the expert systems and management science/operations research practices developed in the mid-20th century. These early systems aimed to replicate human reasoning using predefined logic. As technology advanced, decision management evolved to incorporate data-driven analytics and visual analytics tools. For instance, the Decision Exploration Lab introduced visual analytics solutions to help understand and refine decision logic, streamlining business decision-making. This historical context helps place current decision management strategies within their evolutionary framework.

Decision management was described in 2005 as an "emerging important discipline, due to an increasing need to automate high-volume decisions across the enterprise and to impart precision, consistency, and agility in the decision-making process". Decision management is implemented "via the use of rule-based systems and analytic models for enabling high-volume, automated decision making".

Organizations seek to improve the value created through each decision by deploying software solutions (generally developed using BRMS and predictive analytics technology) that better manage the tradeoffs between precision or accuracy, consistency, agility, speed or decision latency, and cost of decision-making within organizations. The concept of decision yield, for instance, focuses on all five key attributes of decision-making: more targeted decisions (precision); in the same way, over and over again (consistency); while being able to adapt "on-the-fly" (business agility) while reducing cost and improving speed, is an overall metric for how well an organization is making a particular decision.

Organizations are adopting decision management technology and approaches because they need a higher return from previous infrastructure investments, are dealing with increasing business decision complexity, face competitive pressure for more sophisticated decisions and because increasingly short windows of competitive advantage means that the speed of business is outpacing speed of information technology to react.

Other terms used include "intelligent process automation" (where decision management is combined with business process management).

There are a number of different approaches used to apply decision management principles. In general, these follow three steps:

1. Decision identification and decision modeling using either open standards such as Decision Model and Notation or proprietary approaches such as The Decision Model
2. Development of a system or service (often called a Decision Service) that automates all or part of the decision
3. Ongoing monitoring and management of the decision to keep the business rules and predictive analytics or machine learning models used up to date

Decision management often involves the use of A/B testing and experimentation as well.

• Business rules
• Predictive analytics
• Machine Learning
• Decision making software
• Decision engineering