​The Decade Of Dashboards Is Over: Executives Need Adaptive AI Plus Decision Intelligence

Greg Genung is CEO and founder of Snowfire AI, a pioneer in AI signals and decision intelligence technology.

Here’s a quarterly forecast call I’ve watched happen, in one form or another, at more companies than I can count. The CRO comes in with a committed number. The pipeline dashboard is green, and the CRM shows the top deals in late stage. Three weeks later, two of those deals slip to next quarter, and the company misses its number.

Afterward, everyone finds the signals. One of those accounts had a sudden jump in support tickets. At the other, the champion had stopped logging into the product. Finance had heard about a procurement freeze during a renewal conversation. Every one of those facts sat in a system the company already owned and paid for. None of them reached the decision.

That isn’t a data problem. It’s a decision problem, and it’s the clearest sign I know that the decade of dashboards is over.

Dashboards only answer yesterday’s question.

For the past 10 years, we built reporting estates around one idea: Put the data in front of smart people, and they’ll make better calls. That worked when the questions stayed the same and things moved slowly.

Neither is true anymore. A dashboard is a static answer to a question someone asked months ago. It shows only what it was designed to show, from the system it was built for. The forecast dashboard doesn’t know about the support spike. The security console doesn’t know a renewal is at risk. Each view is accurate, and each one is incomplete.

So executives end up connecting the pieces in their heads, in meetings, under time pressure. That’s where decisions break.

Your estate is an asset, not a liability.

CIOs usually find this part a relief: You don’t have to rip out what you’ve built.

What actually changes from static to adaptive?

The difference comes down to this: A static answer is right once. An adaptive answer stays right as conditions change.

What does this look like in practice?

Here’s a hypothetical example: