Artificial intelligence is becoming a mainstream technology. You can find elements of it in, for example, Apple’s Siri, Amazon’s product recommendations and Facebook’s ability to identify photos in your feed.
But AI is less ubiquitous in the business world—something Salesforce set out to change a few years ago when it began developing Salesforce Einstein.
“For most business people, AI has been too complex and out of reach,” said John Ball, general manager of Einstein, in a briefing with reporters. “You have to collect and integrate a lot of data, convert it to a specific machine format and hire scarce data scientists to work on it and have an infrastructure that’s secure and scalable.
“Even if you have all that, the last mile where a lot of AI projects get tripped up is you have to be able to surface the insights in the context of your business applications—that’s just too hard for the vast majority of companies out there,” Ball said.
With the goal to “democratize AI,” Salesforce says it’s been able to integrate the benefits of AI across its cloud-based CRM applications so it’s just another built-in feature that leverages customer data for insights and predictive analysis, without the need for customers to hire their own data scientists to develop similar features.
The AI features are embedded in Sales Cloud Einstein, Marketing Cloud Einstein, Commerce Cloud Einstein, Service Cloud Einstein and Community Cloud Einstein—updates to Salesforce applications that will be previewed at the company’s annual Dreamforce conference next month.
Ball said the features will be part of the regular updates of the Salesforce platform the company releases three times a year. Some features will be included for free, while others will be available at an extra cost. The first new Einstein features are slated to be available in October 2016 as part of the Winter ’17 release.
In the briefing Salesforce emphasized the fact that the Einstein AI capabilities will cut across every Salesforce cloud application and leverage all available data, from customer contact information to activity in its Chatter news feed, email, calendar, ecommerce, social data streams and even data from devices connected to the internet of things (IoT).
A demonstration showed how Salesforce Einstein could help a customer selling sporting goods identify potential customers for soccer gear simply by entering an image of a soccer ball as the filter, without any text, to gain the ability to market soccer apparel and equipment only those customers who had a soccer ball image in their Twitter feed.
Salesforce pros already can use Sales Cloud to identify leads and prospects, but Einstein aims to greatly reduce the time and effort required.
For example, a feature called Predictive Lead Scoring will automatically analyze all data related to leads—including standard and custom fields, activity data from sales reps and behavioral activity from prospects—to generate a predictive score for each lead, giving the sales rep a better handle on which prospects are more likely to buy.
Going forward, the machine learning behind Einstein enables the models it creates to continually learn from signals such as lead source, industry, job title, web clicks and emails to improve the predictive score for every lead.
A feature called Opportunity Insights will analyze CRM data combined with customer interactions such as inbound emails from prospects to identify buying signals earlier in the sales process, sending alerts when a deal is trending up or down, and recommending next steps to increase the sales rep’s ability to close a deal.
Automated Activity Capture will analyze every email and calendar appointment to deliver predictions, automatically associating and logging new email messages and appointments with the relevant Salesforce records and eliminating the time sales reps spend on manual data entry.
“Einstein’s models will be customized automatically for every single customer and learn, self-tune and get smarter with every interaction and additional piece of data,” the company said.
The news comes at a time of increased interest and efforts to use AI to enhance business applications. There are, for example, a number of apps that use AI to make it easier to schedule and attend meetings.
In August, Apple acquired Turi for $200 million, a company developing so-called deep learning technology, while Google and Microsoft have long had research efforts in the areas of deep or machine learning that aims to provide actionable insights from mountains of data.