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Salesforce introduces Einstein — an AI layer for all its clouds

The company is going beyond its previous integrations of computer intelligence, making embedded predictive and other insights a standard part of its platform.

Barry Levine on September 19, 2016 at 2:46 pm

Einstein made his name, in part, by demonstrating a new understanding of the relationship between matter and energy.

Today, he is lending his name to Salesforce’s newest effort to understand the relationship between customers and your business.

The company is announcing a layer of artificial intelligence (AI) that is branded with the name of the world’s most famous smart person, in an effort to embed a new level of smartness into all of the company’s clouds — Sales, Marketing, Internet of Things, Community, Service, Commerce and Analytics.

Additionally, a Salesforce Research group is being launched, charged with developing new deep learning, natural language processing and computer vision applications for the company’s products.

Of course, this is not Salesforce’s first instance of employing computer intelligence in its services. Last November, for instance, it added Predictive Scores and Predictive Audiences to its Marketing Cloud.

The difference, Vice President of Product, Data and Analytics Leslie Fine told me via email, is that Einstein will bring such intelligence to the entire platform, making “Salesforce the world’s smartest CRM platform and [making] every customer interaction smarter, personalized and more productive, across sales, service, marketing, IT and more.”

The difference between Salesforce-with-Einstein and other marketing platforms — some of which have their own built-in or added-on intelligence — is automated intelligence, Gartner VP Ruth Salaam told me.

Salaam noted that Adobe, as one example, has also invested heavily in advanced analytics and intelligence in its Marketing Cloud. But, she added, there are several potential advantages in the Salesforce platform, moving forward.

‘Insight everywhere’

First, the intelligence is embedded throughout the platform, not focused on a few specific areas. “Insight everywhere,” she called it.

Second, the AI models are dynamic and continuously updated, compared to many AI models that require more or less manual maintenance. That means models and data are always working, always being tuned up, and thus potentially more relevant.

As one example of the new capabilities of dynamic AI, she pointed to Salesforce’s recent acquisition of BeyondCore. That AI marketing provider claims its data analysis tech is so smart, it generates the questions as well as the answers.

And the third potential advantage, she said, is an intelligent agent like Siri or Alexa featured as part of the platform. This might remain embedded and out of sight, but it raises the possibility of a generalized intelligence that goes beyond the individual functions.

Salesforce’s Fine noted that “no other company today is delivering real, comprehensive AI across all facets of the customer experience including sales, service, marketing and more.”

“And because Einstein is an integral part of the Salesforce Platform,” she said, “customers don’t need to do anything to start using it or seeing the benefits. It just works.”

Forrester analyst Brandon Purcell emailed me that Einstein will provide two kinds of insights.

The first, he said, “consists of localized insights based upon Einstein learning from individual companies’ data sets. In many ways, this is similar to what other predictive marketing vendors such as Infer and Radius are offering, and is an extension of the Marketing Cloud’s existing personalization capabilities.”

‘The real game-changer’

But, he added, the “real game-changer will emerge from Salesforce’s ability to glean globalized insights from all the data in the Salesforce cloud. Einstein will basically be attending Harvard while competitive offerings are stuck in grade school. If they’re able to do this and package it effectively, it will be extremely difficult for anyone else to catch up.”

Salesforce outlined some of the specifics that are included in this initial incarnation of the Einstein layer, including several — such as Predictive Scores and Predictive Audiences in the Marketing Cloud — that were already existing:

Sales Cloud: Predictive Lead Scoring; Opportunity Insights with alerts on deal status; Automated Activity Capture that compares email and calendar activity with Salesforce customer records to make predictions.

sales-cloud-einstein-opportunity-insights

Marketing Cloud: Predictive Scoring about users’ likelihood to engage with specific emails; Predictive Audiences to build custom audience segments based on predicted behaviors; Automated Send-time Optimization to suggest best times to deliver messages.
Commerce Cloud: Product Recommendations; Predictive Sort to order personalized search results based on engagement likelihood; Commerce Insights to inform retailers about the best approaches to store planning.
Community Cloud: Recommended Experts, Articles, and Topics; Automated Service Escalation; Newsfeed Insights to highlight the predicted most relevant content.
Analytics Cloud: Predictive Wave Apps for discovering business process patterns; Smart Data Discovery; Automated Analytics & Storytelling to suggest insights for users.
Service Cloud: Recommended Case Classification to automatically route cases to the right agent for faster resolution; Recommended Responses will send suggested responses to agents; Predictive Close Times will estimate time needed for issue resolution.

service-cloud-einstein-automated-case-classification-recommended-responses

IoT Cloud: Predictive Device Scoring for incoming data; Recommended Best Next Actions “for service processes and marketing journeys;” Automated IoT Rules Optimization for the management of data.

The Einstein layer will also allow developers to readily create apps that include AI capabilities in objects, page layouts or workflows. This includes Predictive Vision and Sentiment Services so that developers can utilize automated image classifications and sentiment analysis of text, and Predictive IO in Heroku Private Spaces for creating custom predictive models.

About The Author

Barry Levine

Barry Levine covers marketing technology for Third Door Media. Previously, he covered this space as a Senior Writer for VentureBeat, and he has written about these and other tech subjects for such publications as CMSWire and NewsFactor. He founded and led the web site/unit at PBS station Thirteen/WNET; worked as an online Senior Producer/writer for Viacom; created a successful interactive game, PLAY IT BY EAR: The First CD Game; founded and led an independent film showcase, CENTER SCREEN, based at Harvard and M.I.T.; and served over five years as a consultant to the M.I.T. Media Lab. You can find him at LinkedIn, and on Twitter at xBarryLevine.