Decision intelligence, minus the category jargon
The phrase describes software that helps people decide better. That is broad enough to be useless, so the useful distinction is what the tool actually does to the decision.
The category · 6 min read
What is decision intelligence? An umbrella term for tools and practices that improve organisational decision-making, spanning analytics platforms, decision modelling, and more recently AI assistants. The distinction that matters is whether a tool supplies information, or works the reasoning.
Three quite different things travel under the label, and conflating them is why the category is hard to evaluate.
- Information tools. Dashboards, forecasting, data platforms. They tell you what is happening. They do nothing about how you reason from it, which is where most bad decisions are made.
- Modelling tools. Decision trees, scenario models, simulations. Genuinely useful when the decision is well-structured and the variables are known. Most executive decisions are neither.
- Reasoning tools. Anything that changes the process by which a person works a judgement call: the questions asked, the order, the record kept.
Most decisions do not fail for lack of data. They fail because nobody asked what would make the plan wrong.
Why the third category is the interesting one
The failures that hurt are rarely informational. They are framing failures: the option nobody listed, the assumption nobody named, the consequence nobody traced. More data does not fix any of those, and a dashboard cannot, because the problem is upstream of the numbers.
What to ask of anything in this category
- Does it change the process, or only the inputs?
- Does it produce something reviewable later, or does the output evaporate when you close the tab?
- Can it produce a conclusion you did not want? If it structurally cannot, it is a mirror.
- Does it get better with use, in a way you could demonstrate rather than assert?
Five Peers editorial note. Sources named in the text.