In his recent cover story interview with Forbes, Salesforce CEO Marc Benioff unveiled a sneak peek at the company’s most recent project: Salesforce Einstein. Benioff noted via Twitter that this project is the first comprehensive artificial intelligence (AI) platform for CRM.
“If this is not the next big thing, I don’t know what is,” said Benioff in his interview with Forbes. “We are going to catch our competitors by surprise.”
The new project will integrate AI capabilities into a majority of Salesforce’s current offerings, according to Forbes. This will include predictive recommendations designed to enhance the users marketing, sales and service initiatives. While Salesforce confirmed that everything in the Forbes story was accurate, they declined to comment further on the project. The project can explain the number of acquisitions Salesforce has made in the analytics/AI market since the beginning of the year. Most recently, Salesforce acquired BeyondCore, an enterprise analytics startup, for an undisclosed fee. Other notable acquisitions include:
- PredictionIO, an open source machine learning server for creating predictive features, in February 2016;
- MetaMind, an automated image recognition software powered by artificial intelligence, in April 2016; and
- Implisit, a predictive analytics solution for enhancing sales performance, in May 2016.
Benioff will be spotlighting Salesforce Einstein at the company’s annual Dreamforce conference, which takes place in October 2016.
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Industry expert Justin Gray, Founder and CEO of LeadMD, noted that this attention speaks to the potential and validity of software that learns from the behavior and data of its users.
The biggest barrier to this taking place is not technology, however. It’s data. Gray added that this can be attributed to the minimal, high-level data being gathered on a company’s website.
“Whether that is sales activity data, buyer data, firmographic data or all the way into psychological information on how our reps think and how buyers perceive and process information, across the board, data is lacking,” said Gray. “We must solve the data problem — find more accurate and evergreen data — in order for AI to produce reliable results.”