Jev AI Will Make You Money – But Exactly How Much?

As I covered in my last article earlier this week, Jev and its competitors – a new breed of “judgement models” – have recently taken the AI world by storm. They provide the power to leverage modern foundation models for predictive AI projects. This includes the capability to perform zero-shot outcome prediction, eliminating a major barrier to entry for predictive AI: training a customized predictive model.

But most Jev users make a grave error: They measure only technical performance such as accuracy, rather than business performance. They fail to directly measure how good their Jev-based system is for their business.

AI accuracy tells you almost nothing about business value. Say you are predicting which customer messages convey an intention to cancel. 20% of the messages are positive (they do convey that negative signal) and 80% are negative. If an AI system predicts “no” for every customer, it achieves an 80% accuracy, even without correctly identifying a single at-risk customer.

Instead, measure the potential money you’d make by using the model.

Predictively Investing In Customers

Using a model is the only way it realizes value. Operationalize it, deploy it, launch it or put it to production. All different ways to say the same thing. You need to determine what the system will do when it receives a customer complaint such as:

“I am writing to report a significant problem with the centralized account management portal, which currently appears to be offline. This outage is blocking access to account settings, leading to substantial inconvenience. I have attempted to log in multiple times using different browsers and devices, but the issue persists.”

If this is for B2B, such as a payroll service, you could respond with a retention offer of, say, $250 off their subscription. That might pay off well, averaging $700 of net future earnings from customers correctly targeted and convinced to stick around.