✨ Why is Decision Intelligence needed?
Historically, decision-making has been central to industries like insurance banking government health financialservices and logistics.
Yet most organizations still struggle to make decisions well.
💥 Because decision-making problems are often misunderstood.
Therefore, intelligence is mistaken for AI. Decisions are reduced to data.
🔸The data problem
Despite popular belief, data is not the center of decision-making. On average, 60% to 73% of enterprise data is never used for analytics. even when data is used 78% of the time is for to justify te already-made decisions.
🔸The noise problem
Humans are inconsistent judges. As Daniel Kahneman said, where there is judgment, there is noise. The same decision, made by different people, produces different outcomes due to bias, mood, and narrow-window thinking. That variability cannot be reduced by the AI or more data.
🔸The algorithm problem
An algorithm-centric approach to ai ml llm does not solve business problems. Many models never reach production. Others solve the wrong problem or learn from historically bad human judgments.
🔸The silo problem
Decision-making is multi-step, multi-dimensional and continuous. Siloed systems, data, processes, and technologies increase complexity and fragmentation. Eventually lead to decision drift i.e. the disconnected decision experience.
So, what's the answer?
• Decisions must be modeled explicitly.
• They should be hierarchical and multi-step.
• They should use composite intelligence combining rules machinelearning optimization.
• They should consider the influences of the previously made decisions i.e. Continuous Decision Model (CDM)
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Published January 19th, 2026 at 10:30 am
