AI model vs Decision model

Characteristics of AI Models:

• Functionality Focused on Precision: AI models formulate a function that represents the relationship between a single target and the remaining input data, aiming for precise predictions or classifications.

• Performance Measured by Statistical Metrics: They are typically evaluated based on statistical indicators like GINI, MSE, and AUC.

• Developed by Specialists: Modeling requires mathematical and programming expertise, so AI or data professionals are primarily responsible for development and operation.

Characteristics of Decision Models:

• Integrated Approach for Better Decisions: Decision models are designed to enhance business decision-making by integrating business KPIs, AI models, business rules, and expert judgments into a unified function.

• Evaluated Through Business KPIs: They can be assessed and managed using business KPIs, which serve as the criteria for business decisions.

• Collaborative Development: Because the input of domain experts is crucial in business operations, not only AI specialists but also field professionals can directly participate in modeling and monitoring tasks.

• Handles Multiple Decision Topics: While AI models can handle only a single prediction target, decision models can address multiple decision-making topics within a single model.

• Adaptable to Changing Environments: Since it's vital to adjust decisions according to the ever-changing business environment and situations, decision models must be capable of being appropriately modified in a timely manner.