This gap exists, Salesforce executives asserted, because too many companies are struggling to overcome data-management and data-science technical hurdles, so they have yet to put AI into production at scale. Salesforce’s alternative is Einstein and Einstein Analytics, both of which offer prebuilt, CRM-embedded predictions and recommendations along with the ability to build AI apps and answer company-specific business questions without coding or data science expertise. There’s no question that AI is of interest to organizations and that the Einstein and Einstein Analytics promise of pre-built capabilities and declarative, no-code/low-code app and model development is compelling. Yet Salesforce executives frankly acknowledged that Einstein and Einstein Analytics have only scratched the surface of potential adoption, with Sales Cloud use cases leading the way. Thus, what we heard about during this year’s Einstein and Einstein Analytics keynotes was more out-of-the-box features, more built-in platform capabilities and prebuilt industry apps, more proven use cases, and more training and support options. Here’s my rundown and take on 15 announcements.

Einstein Steps Up on Voice and Recommendations

The two themes emerging around Salesforce Einstein at Dreamforce where voice and recommendations, with new capabilities introduced or announced for the platform and specific clouds. Here’s the rundown:

MyPOV on the Einstein announcements

Voice was clearly the highlight of the Einstein keynote, and the Einstein smart speaker demo (whether real or not) gave it a novel, anthropomorphic twist. Nonetheless, I expect that the vast majority of Einstein Voice interactions will take place on smart phones through Salesforce mobile apps. One surprise announcement that deserves mention was the offering of 1 free prediction (beginning in February) for every organization with an enterprise license to Einstein Prediction Builder. The results of this prediction can be exposed to all licensed users within the organization. This is clearly a “try it, you’ll like it” offer meant to promote adoption. In fact, many Salesforce customers are entitled to included Einstein capabilities or bundled licenses but are not yet using them. The impediment is often not knowing where to start. Salesforce’s growing Trailhead community and educational offerings, such as the new “Einstein’s Guide to AI Use Cases,” are geared to demystifying AI and promoting adoption. But for some organizations, licensing costs are a concern and the reason for hesitation. My advice is to let business value be your guide. Take advantage of any built-in capabilities and bundled licenses to prove initial business value and see where it takes you.  

Einstein Analytics Blends BI and AI

It’s early days for the Salesforce acquisition of Tableau Software, so the simple explanation of the company’s analytics portfolio presented at Dreamforce (and a week earlier at the Tableau Conference) is that Einstein Analytics is for CRM-embedded analytics while Tableau is for Enterprise-Wide Analytics (while last year’s Datorama acquisition is for marketing intelligence). In their keynote, Einstein Analytics execs noted that the embedding platform provides a single stack for BI and machine learning that focuses on optimizing for business outcomes rather than exploring data in hopes of finding relevant insights. Here a rundown on the biggest Einstein Analytics announcements.

MyPOV on the Einstein Analytics announcements

Like the Einstein team, Einstein Analytics leaders pointed to multiple training and support assets, including Trailhead, a new Learning Map and upcoming Adoption and Learning Academies as ways to promote adoption. Here, too, I think cost is also an impediment for some firms, with the $150 per-user, per-month fee for Einstein Analytics Plus being an impediment, particularly when organizations have existing investments in BI and analytics tools and data management infrastructure. The new mix-and-match, $75 per-use, per-month Einstein Predictions SKU and Direct Data offerings are clearly designed to overcome these barriers and jump start adoption. The bet is that a taste of predictive insight embedded directly into Salesforce apps will win teams over to the combined BI and ML Einstein Analytics platform.

MyPOV on Dreamforce and the Salesforce Economy

Dreamforce is turning into two events in one. The larger stage highlighted Salesforce as the exemplar of what Marc Benioff has called “the new capitalism” in opinion pieces published in The New York Times and elsewhere. This was the side of the event that featured talk of values, sustainability goals, and corporate responsibility – not to mention all the celebrities and sessions on diversity, equality, and climate change. Not that I don’t identify with many of these values or believe they’re not genuine, but I suspect another motivation for highlighting these topics at Dreamforce is to attract employees. Salesforce is now the largest employer in San Francisco, according to Benioff, but it has fierce competition for workers. Studies show that younger workers, in particular, want to know that their careers have a higher purpose and that they identify with the values of their employers. Further, many customers are also choosing to do business with companies that focus on more than just profits. Salesforce is certainly setting itself apart in a Silicon Valley culture that’s too often focused on funding rounds and market valuations.   The second, more traditional aspect of Dreamforce is the product-, technology- and innovation-focused CRM event, which is the part I was there to see. The Einstein and Einstein Analytics keynotes delivered plenty of announcements tweaks to packaging and pricing. Both presentations were also laced with real-world examples and validations featuring, or even presented by, customers including State Street Global Advisors, PWC, Schneider Electric and Indeed.com. There clearly is a Salesforce economy and Dreamforce has become more than just an annual gathering for customers and partners building on the platform of CRM.  

Analytics For Applications: Three Next-Generation OptionsSalesforce to Acquire Tableau: Why Now and What’s the Path Forward?Market Overview - Augmented Analytics: How Smart Features Are Changing Business Intelligence