This article originally appeared on Real Money on March 6, 2017.

Given how much several tech giants throw around the terms "artificial intelligence," "machine learning" and "deep learning," it's easy to assume that they're all trying to do similar things. And in some cases, the companies are indeed using AI to tackle problems that have much in common, particularly when it comes to making sense out of real-life sounds, images and dialogue.

But a closer look shows that tech giants are at times not only tackling very different challenges, but employing different AI techniques to do so. IBM (IBM - Get Report) , which has reportedly set a goal of obtaining $10 billion in annual revenue related to its Watson AI platform by 2023, is a particular outlier -- not seen as a leader when it comes to certain large-scale, consumer-focused AI solutions, but very much one when it comes to AI offerings that require a measure of industry-specific expertise.

Big Blue's just-announced deal with cloud CRM software leader (CRM - Get Report) showcases some of this expertise. The companies plan to pair Watson's ability to make sense out of data unique to fields such as health care, retail and financial services with Salesforce's Einstein AI platform -- the platform relies on the data taken in by Salesforce apps -- to deliver industry-specific insights, predictions and recommendations that can be useful for customer interactions.

One such example given by the companies: By having Salesforce's Marketing Cloud apps plug into Watson programming interfaces (APIs), weather, local shopping and retail industry data analyzed by Watson could be paired with customer-specific shopping data analyzed by Einstein to "send highly personalized and localized email campaigns to shoppers."

IBM also plans to provide companies with tools for integrating data from their on-premise and cloud apps with Salesforce's apps, and a solution from its Weather Company unit (acquired in 2015) that will let companies pull in weather data that can be used by Salesforce apps. One cited example: An insurance company using weather forecast data with Salesforce's Service Cloud (customer support software) to alert customers who could be affected severe weather events.

Also as part of the agreement, IBM plans a company-wide deployment of Service Cloud. Given IBM did $80 billion in sales last year, it's safe to assume that this is one of Salesforce's larger Service Cloud deals.

Though the Salesforce tie-up is one of the more prominent Watson partnerships IBM has announced, it's far from the only one involving a big-name company.

Other notable partners include BMW (which uses Watson to analyze driving data), Medtronic (MDT - Get Report) (it uses Watson to analyze data from medical devices for diabetes research), Cisco Systems (CSCO - Get Report)  (offers joint solutions with IBM to analyze industrial IoT data and provides insights to users of Cisco's collaboration software), Twitter (TWTR - Get Report) (Watson helps businesses analyze tweets to understand user sentiment and behavior) and General Motors (GM - Get Report)  (Watson works with GM's OnStar telematics platform to provide personalized content and ads).

In some ways, IBM is clearly casting a very broad net with its AI efforts, one that might only be matched by Alphabet/Google (GOOGL - Get Report) . The company is likely able to do this in part because many of its AI projects are less resource-intensive than some of the top consumer-focused projects at other tech giants.

Many Watson projects seem to involve running algorithms against specific datasets to classify and uncover patterns for different pieces of data. Since the algorithms get smarter/more accurate on their own as the amount of data they're run against grows, they're considered to belong to a branch of AI known as machine learning.

A lot of the big AI projects at IBM's peers involve an advanced subset of machine learning known as deep learning. Here, algorithms -- often run against giant datasets -- attempt to mimic the behavior of neurons in a human brain to understand sounds, images and text as humans do. Deep learning projects, which often make heavy use of Nvidia (NVDA - Get Report) GPUs, underpin many of the efforts by Google, Facebook, Microsoft and others to offer things such as voice assistant, photo-tagging and autonomous driving solutions to millions of consumers.

IBM is investing in deep learning as well. But many of its Watson offerings appear to rely on other, less demanding types of machine learning. That, along with IBM's considerable industry-specific expertise, allows it to develop and run AI algorithms for a wide variety of enterprise use cases.

There are some parallels here with IBM's 2014 deal with Apple  (AAPL - Get Report) to develop iOS apps for verticals such as banking, retail and travel. Apple certainly isn't lacking in app development skills, but can't match IBM in providing the kind of domain-level expertise needed to create software for specific types of Global 2000 companies. Salesforce, which has invested heavily in its CRM-focused AI solutions, likely reached a similar conclusion about Watson's AI strengths.

There is some risk that Google, Microsoft and possibly others could encroach more on Watson's turf in the future. Google, in particular, has talked about how many of its AI advances in areas such as image-analysis and natural language-processing are applicable to numerous Google projects. But for now, Watson is in a unique position as AI investments surge within the tech sector and beyond.

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