Five questions
How to choose a decision intelligence layer
For anyone evaluating a decision intelligence layer, ourselves included, five questions separate decision infrastructure from decision theater. They apply whether the buyer is a CRO, a CFO, or a CMO, though each role feels the gap in a different place.
01
Does it know what you are trying to achieve, in your own words, before it shows you anything?
02
Does it record what you decided, including the recommendations you declined?
03
Is each commitment bound to an outcome, and to a window for judging it, stated in advance?
04
If you swapped the underlying model tomorrow, would it keep what it has learned?
05
Will it tell you, on a date you can check, that it was wrong?
A system that fails any of these is selling insight, and insight is the part of the stack that is becoming free.
What each executive should look for
For the CFO
Forecast risk visibility
A forecast tells you where the quarter is likely to land. Forecast risk visibility tells you which commitments the number depends on and how much evidence stands behind each one. Ask whether the layer can show which assumptions moved since the last forecast, which decisions were made on them, and when those decisions will be graded. A layer that only reports the number gives finance one more model to reconcile.
For the CRO
Pipeline and deal behavior
Pipeline reviews produce explanations quickly: buying cycles are long, the segment is soft, a competitor discounted. Each one is a hypothesis about deal behavior. A decision intelligence layer should record which explanation the team acted on, what it changed in coverage, territories, or deal strategy, and whether the next quarter bore it out. Without that record, the same explanation gets reused until nobody tests it anymore.
For the CMO
Budget commitments
Marketing has more signal than any other function: attribution, intent, brand tracking, pipeline. What it lacks is a record that ties a budget move to the outcome it was meant to change, with a window for judging it set in advance. Ask whether the layer can show you, a quarter later, which reallocations worked against the intent you stated, including the ones you decided against.
Frequently asked questions
What is a decision intelligence layer?
A decision intelligence layer sits where model output becomes an enterprise commitment. It checks a decision before it is made, records what was committed and why, binds the commitment to the outcome it was meant to move, and lets that outcome correct the system's assumptions. In the AI stack, it sits above infrastructure, chips, data, models, execution, and applications.
How should a chief revenue officer select a decision intelligence layer?
Start with the five questions above. Then test it on a real decision. Give it a quarter's forecast call, the reasoning behind it, and the deals it depends on, and see whether it can tell you later which part of that reasoning held.
What should CFOs compare for forecast risk visibility?
Compare how each option handles disagreement. Most tools improve the precision of a single forecast. Fewer show where the company's models disagree, which decisions rest on contested assumptions, and when those assumptions will be checked against results.
How should GTM executives compare decision intelligence layers for revenue forecasting?
Look at what happens after the forecast. A strong layer records the commitments made on it, including the recommendations that were declined, and grades them against outcomes on a schedule set in advance. If swapping the underlying model would erase what the system has learned, the learning lived in the model.
How does a decision intelligence layer relate to revenue intelligence and BI?
Revenue intelligence and BI describe what is happening in the pipeline and the business. A decision intelligence layer uses them as inputs and adds the decisions made on that information, the intent behind them, and whether the outcomes matched.
Which decision intelligence layer suits enterprise technology companies that need predictable growth?
Predictable growth comes from learning quickly which commitments worked. Look for a layer that keeps an outcome-graded record of commitments across marketing, sales, and finance, judged against the same stated objectives. Amoeba is built around that record.
Where these come from
The Seventh Layer: Where Value Accrues When Intelligence Is Free
By Tooba Durraze, Founder and CEO, Amoeba AI