Decision Intelligence Outlook in 2025

Major global research firms like Gartner and Forrester are evaluating Decision Intelligence as a technology with the potential to completely transform the market landscape. As the complexity of the business environment increases and changes accelerate, the importance of companies enhancing their decision-making capabilities is becoming ever more critical. Despite heightened expectations for AI in recent times, it hasn't been directly applied within business processes, leading to a growing trend in demand for connecting AI with business operations.

• Increased indirect demand for Decision Intelligence

  Companies that have encountered difficulties of AI implementation, are now seeking practical approaches to utilizing AI. Particularly, large enterprises with established digital or AI department have realized AI technology is only one part of the journey. While they strive not to fall behind in technical trends, such as LLM, Generative AI, their focus has shifted to actual business cases.

  
  

  The pressures for AI transformation are increasing, with higher management continually asks business teams to apply AI capabilities to their work. Inquiries and requests directly from business units have notably risen. Meanwhile, AI teams have learned that cooperation with business units is very challenging. From a business perspective, AI is part of the decision-making process, making it essential to address and structure business goals, problems, constraints - this is where decision intelligence comes into play.  Decision Intelligence is still in its emerging stages, resulting in fewer direct requests for it. However, the underlying demand is closely related to AI-based decision-making.

  
  

  Examples of indirect demand 

  
  

  1) Insurance

  - Underwriting Intelligence

  - Claim Intelligence

  - Actuary Intelligence

  - Reject Inference

  
  

  2) Banking

  - Credit Application Intelligence

  - Credit Behaviour Intelligence

  - Credit Limit Intelligence

  - Reject Inference 

  
  

  3) Retail, Manufacturing and Logistics

  - Replenishment Intelligence

  - Merchandise Intelligence

  - Demand Forecasting - Resource Planning

• Increased demand for explainability, simulation, optimization, monitoring & updating of decisions.

  There have been remarkable advancements in AI implementation in the market. However, since current AI techniques typically conclude at the narrow-contented modeling step, they do not integrate well with business decision-making process. As business decisioning process requires explainability, simulation, optimization and monitoring & updating capabilities, the demand for these techniques is increasing significantly. This demand also reflects the collaboration between humans and technology.

- Explainability: The explainability of an AI model differs from that of business decisions. While XAI (Explainable AI) involves statistical interpretation, business units require a deeper, multi-dimensional understanding of the business situation and predictive impacts.

- Simulation: Although an AI model can produce accurate outputs, business units do not use these outputs as-is. Because decisions can lead to unexpected side effects, it is crucial for business operations to understand the causal relationship between decisions (actions) and their effects and results.

- Optimization: As the complexity of the business environment increases, it can easily overwhelm human cognitive capabilities. While AI modeling typically supports single-target prediction, business decisions require consideration of multiple constraints and factors. This is where computational optimization becomes essential.

- Monitoring & updating: As the business environment rapidly changes, business units must monitor the business status and adjust decisions promptly. This involves more than just model effectiveness; it encompasses the entire business operation.

Requests are partially influenced by clients’ situations or business types, Additionally, while various tools exist for each of techniques, they have not been unified into a cohesive, continuous decision-making process. This disjointed approach has not only increased costs but also added difficulties to business decision-making.