IBM senior vice president Bob Picciano, left, joins the Weather Company chairman and chief executive David Kenny, right, at the IBM Insight Conference in Las Vegas. (IBM)

IBM Watson continues to find new career callings. This time it’s as a weather forecaster. With IBM acquiring the digital assets of the Weather Company for a reported $2.5 billion, it’s only a matter of time before the world’s most famous supercomputer starts coming up with new insights at the intersection of atmospheric science, computer science and big data.

The focus at IBM is not so much in getting Watson involved in making better weather forecasts, but in putting the world’s most famous supercomputer to work in mining epic amounts of data in order to help businesses come up with actionable insights about the weather on both a real-time and long-term basis.

As part of the deal, IBM Watson will have access to an enormous amount of data from The Weather Company — a cloud-based platform that handles 26 billion requests per day. It also includes data from three billion weather forecast reference points, 40 million mobile phones and 50,000 daily airline flights.

In fact, the bigger the data, the better. That’s because IBM is planning to package up all this data, customize it for specific industries and verticals, and make it available via the cloud.

Retailers, for example, may be interested in seasonal data, so that they know not to stock too many sweaters ahead of time if it’s going to be a warm winter. Insurance companies may be interested in data for specific geographic locations, so that they can predict potential liability for upcoming weather events. Airlines may be interested in a mix of real-time and historical data, in order to optimize fuel consumption and reduce weather-induced delays. Wall Street traders may want better weather forecasts to help them estimate the performance of specific weather-sensitive businesses.

How big of a market opportunity is this? According to IBM, it could be as big as $500 billion — that’s the amount that businesses lose each year due to weather unpredictability. By being able to have access to better weather prediction ability, companies hope to be able to cut those costs significantly.

The big question, then, is: Just how much better is IBM Watson going to be at forecasting the weather than today’s weather forecasters? After all, weather forecasting is a notoriously difficult business — just ask the meteorologists who appear on the Weather Channel.

The big challenge facing IBM Watson, says Richard Perez, professor at the State University of New York’s Atmospheric Research Center in Albany, is getting the inputs right: “Forecast techniques — whether they employ data mining, physical/empirical or stochastic models — are only as good as the inputs that are used to run them.”

Traditional modeling techniques, he told me via an e-mail conversation, are the least valuable “when atmospheric conditions are at their most unpredictable or inconsistent.” An initial condition in a weather forecasting model that’s off by just a little can lead to results that are off by quite a lot.

That’s where another aspect of the IBM-Weather Company deal starts to make sense. IBM has a massive $3 billion pot of money for extending IBM Watson to the Internet of Things. While IBM is not able to comment now on how IBM Watson will integrate previous weather research work at the company, it’s possible to speculate about where IBM is headed.

Imagine the entire Internet of Things being used to improve the inputs into the weather prediction model used by IBM Watson. Imagine planes, ships and trucks traversing the globe, picking up weather inputs. Imagine buildings with sensors picking up new data. That’s because better inputs mean better weather forecasts.

Sound crazy? Maybe. But AI researchers at Microsoft, for example, have found that by thinking of airplanes in the sky as sensors, and then applying some machine learning techniques, they could come up with much better forecasts of future wind speeds. And the more airline companies know about wind, the more they can optimize flight patterns and fuel consumption for airplanes.

With all this focus on business, it’s easy to lose sight of the enormous consumer applications of getting IBM Watson into the weather forecasting business. IBM Watson, quite simply, may change the way we think about weather.

Consider, for example, that one of the key selling points of the IBM-Weather Company deal was the acquisition of the mobile business, including the Weather Channel app, the fourth most used mobile app in the United States. If IBM Watson gets involved in improving the app’s predictive ability — and IBM won’t specifically confirm any details until after the deal officially closes in Q1 2016 – then it’s safe to say that IBM Watson will have a potential touch point into every one of our lives.

Getting weather forecasts from IBM Watson via an app implies that artificial intelligence would begin entering our daily lives in subtle ways. We’ll start to expect more from our weather forecasts. We won’t be happy with just knowing that it’s going to be sunny later in the day. We’ll ask IBM Watson very specific queries that are weather-related, to help us make sense of the world.

“I’m dubious that the big win is from increasing the accuracy of old fashioned weather forecasting,” says Andrew Moore, dean of the School of Computer Science at Carnegie Mellon University. “It’s in predicting how the rest of the world will be impacted by upcoming weather events, from airport delays to moods.”

The moods angle is an interesting one. One example that Moore told me about is Netflix. It could be the case, he noted, that “the choice of a Netflix movie is influenced by the weather during the day for some demographic of people.” This link between weather and moods could be an interesting angle for IBM Watson to pursue.

This hints at a future in which we think of the weather very differently than we do now. We’ll stop thinking of weather as a “liability” — as something that’s random and chaotic and often impedes us from doing what we want to do. Instead, we’ll see weather as an “asset” — as something that can help to make us better decisions about the everyday world around us.