Challenges in implementing decision intelligence

As Decision Intelligence brings about fundamental changes to business decision-making processes, there are obstacles to its implementation. Here are the three top challenges in this market.

• Fragmented Decision-making processes within a company

  Enterprises have partially adopted rule-based, data-driven, and AI-driven decision-making processes. Although some processes are fully automated, human experts are typically responsible for final decisions or monitoring the processes. Due to the fragmentation of these processes, it is structurally impossible to simulate, optimize, and monitor decisions effectively.

  
  

• Fragmented Decision-making structure within a company

  Decisions across departments are interrelated. When individual departments analyze and make their own decisions, these decisions are collected to form comprehensive, company-wide decisions. This task is typically managed by roles such as higher management, the SCM (supply chain management) department, and cross-organizational committees. Despite the high demand for a centralized decision-making structure, decisions from individual business units remain fragmented.

  
  

• Following the AI hype

  The AI market often chases fads. While primary technologies are largely driven by big tech companies, the needs of traditional industries differ from these technological targets. Sectors such as finance, manufacturing, retail, and logistics require transformation of their existing business processes. AI is a crucial component of this transformation, but it is not the entire solution. For successful AI transformation, it is more important to focus on how to leverage appropriate technologies to meet business goals and objectives rather than on what the latest technology can do.