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As artificial intelligence tools become a mainstay in offerings from Salesforce, IBM and other major vendors, focus is turning to how enterprises can use AI functionality to better understand their customers.
The recent announcement that IBM will share its Watson AI platform with Salesforce has grabbed the cloud AI market's attention in a big way. And while it's certainly big industry news, it's also packed with intriguing possibilities for applications like customer sentiment analysis.
At the risk of oversimplification, the Einstein platform is an AI extension of existing Salesforce CRM functionality. It benefits from access to oceans of customer behavioral data as well as Salesforce's deep experience in descriptive analytics. Watson, on the other hand, is computationally intensive, a machine learning environment that offers predictive analytics as a cloud service.
In analytics parlance, Einstein is descriptive, Watson is predictive and their combined powers are prescriptive, resulting in recommended courses of action, something that neither system is great at doing on its own.
So what outcomes can this combination produce?
Salesforce's expertise is information about customers, while Einstein inherently lacks a strong base in direct customer-related problem-solving. But a great many types of problems can indirectly affect customers, and that's where the alliance begins to make a lot of sense.
Watson contains vast amounts of structured and unstructured data from a wide range of industries, such as the financial sector, healthcare, supply chain management and retail -- and that data can be leveraged to generate customer sentiment analysis that could be far more focused than what Salesforce's Intelligent Customer Success Platform can accomplish on its own.
In a Fortune interview, IBM president and CEO Ginni Rometty suggested that Watson's great breadth in problem-solving could be applied to real-world, real-time scenarios as services to customers. She imagined an insurance company leveraging Watson's weather prediction expertise to warn its customers in a particular area of impending severe weather and hopefully pre-empt damage and subsequent claims.
This sort of pattern finding, at which Watson excels, could generate new layers of insight into environmental and social factors influencing customer behavior, resulting in better targeted advertising, geographically and more.
AI normalization in B2B and logistics
Supply chain operations, which juggle environmental and geographic factors in logistics that end up serving customers at several levels, have also been prime targets of AI-driven analytics. Artificial intelligence and analytics are a pattern-finding dream with challenges that boggle the mind and win-win rewards for all participating businesses and customers in the chain. Watson's cumulative breadth of predictive expertise is ideal for applications in supply chain logistics.
What has been lacking is a clear point of entry, which a merger with the Salesforce platform can provide. Most B2B entities in any chain have sales teams, and thousands already use Salesforce to some degree. B2B participants in supply chains, which have long sought an easier path to analytics success, can view the Watson-Einstein merger as a kind of one-stop shop.
That makes proliferation of predictive analytics implementation as well as improved customer sentiment analysis capabilities almost inevitable. And Watson's deep inroads into the financial market only sweeten the potential of the merger for B2B.
How it will work
The first step in the alliance is the eventual uptake of the Salesforce Service Cloud within IBM later in 2017, making it the computing giant's sales and marketing platform. That's a publicity win for Salesforce, but it also creates a real-world proving ground for the merged technologies: Both companies will learn firsthand how to best leverage their hybrid child -- and those lessons will trickle down to the 5,000 customers the two companies already share.
Watson and Einstein services will still be sold separately; there will be no package deal. Yet customers will benefit from shared, API-enabled mutual access. Subscribe to both platforms, and the predictive power of their interfaced features and shared data becomes readily available and applicable to a customer's particular problems.
According to USA Today, IBM's Rometty is hopeful of more than just synergetic services. She is forecasting rapid growth in the market during the next few years, pointing out that decision support systems in business thus far have a spotty record. "Studies show that of all the decisions we make, a third are OK, a third are suboptimal, and a third are not correct," she told USA Today. "AI can help with that."
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