From Radar to Reasoning: Why Enterprise AI Finally Watches Your Business in Real Time

In 1935, the British Air Ministry asked physicist Robert Watson-Watt for a death ray capable of downing enemy aircraft. He couldn't build one, but his team developed something arguably more valuable. Within weeks, their radar was able to detect a bomber 8 miles out.

Radar was a conceptual coup as much as a technical one. Radio waves had been reflecting off objects since long before anyone thought to listen for them. What Watson-Watt built was the layer that made those echoes more legible than ever before, creating a picture a person could act on while it still mattered. Five years later, a small team in a control room could watch an entire sky at once - a worldview that changed the course of the Battle of Britain and ultimately World War II.

Today, many enterprises are living in their own "moment right before radar."

They are saturated with signal. The answer to almost any question they care about is somewhere in a stream of millions of data points an hour: error rates, pipeline movement, contact center telemetry, developer activity.

And still, the insight that could have prevented the outage, avoided the attrition, or saved the account tends to surface long after the moment to act has passed. What's missing is the layer that makes it legible.

AI's radar moment

That's the gap Salesforce AI Research Chief Scientist Silvio Savarese is targeting with what he calls "operational intelligence." It's AI that reasons over a business's live data continuously, the way radar operators still scan the skies, instead of waiting for a dashboard to summarize what already happened.

Two capabilities make this work. Deep Insights reasons over structured business data, traces problems back to their root cause, and recommends next steps. Moirai, a time-series forecasting model, predicts demand and flags anomalies with business context already built in - a product launch, a holiday season, a wave of contract renewals.

The results are already showing up inside Salesforce itself. Engineering 360, which tracks 90 billion records across dozens of internal systems, uses Deep Insights to spot patterns humans aren't built to pick out.

"Deep Insights gives Engineering 360 something no human would be able to accomplish at scale," said Salesforce EVP of Engineering Strategy Alex Hu, "unprecedented pattern recognition, correlation across datasets that would be impractical to do by hand, and the reasoning to explain what the data actually means."

Salesforce's own Contact Center and Voice organization is applying the same approach to forecasting, with SVP Gautam Vasudev describing how it's reshaping staffing and demand planning before problems hit the floor.

Savarese sees the model extending well beyond engineering and support into sales pipeline health, marketing campaign performance, and supply chain shortage detection. Basically, anywhere data volume has outpaced a human's ability to watch it.

Radar didn't replace human judgment in 1935. It gave commanders the lead time to use it. Nine decades on, Savarese's bet is that enterprise AI is about to do the same thing for every business function still living in the moment right before radar.

Go Deeper:

• Read the full story on the 360 Blog
• Mastering time series forecasting with Moirai
• How Salesforce Engineering moved to run on AI