Why we focus on decision intelligence

A parent opens a school budget because something needs explaining. Perhaps spending has risen, enrollment has changed, or a program is being cut. A table of figures helps only if the parent can tell which measures bear on the question.

We use “decision intelligence” for the process of making information useful at moments like this. The starting point is a person’s situation: what they are trying to decide, what they already know, and what is missing. That gives us a specific job to design for.

In NJ School Data, it means explaining a measure and its reporting year before inviting a comparison. For Hard Bargains, it means keeping the asking price attached to the machine, condition, and date observed. For Backfield, it means asking what has been demonstrated before interpreting what an AI announcement could mean for a newsroom.

The next step may be small. A parent might leave with a question for a board meeting. A buyer might decide to check a specification or wait for more evidence. Sometimes the most useful finding is that the available information cannot settle the matter.

The amount of explanation should fit the decision. A reversible purchase may need a quick comparison. A choice with lasting consequences may need several perspectives and time to consider them. Adding detail can help, but it can also make a simple task harder.

AI offers ways to respond to a person’s circumstances: accept a question in ordinary language, answer a follow-up, or reconsider the options when a constraint changes. Those possibilities do not remove the need for sources and clear limits. A person should be able to see what shaped an answer and correct a mistaken assumption.

We judge this approach by whether someone understands their options better after using the product. Producing more pages, charts, or answers does not establish that. Our public projects are where we are testing the connection.