{"id":36902,"date":"2018-11-14T17:44:38","date_gmt":"2018-11-14T22:44:38","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/salesmc2-salesforces-attempt-to-democratize-artificial-intelligence\/"},"modified":"2018-11-14T17:44:38","modified_gmt":"2018-11-14T22:44:38","slug":"salesmc2-salesforces-attempt-to-democratize-artificial-intelligence","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/salesmc2-salesforces-attempt-to-democratize-artificial-intelligence\/","title":{"rendered":"Sales=MC2: Salesforce\u2019s Attempt to Democratize Artificial Intelligence"},"content":{"rendered":"

Historically more of an art than a science, the act of selling goods and services has been reinvigorated by the advent of artificial intelligence [1]. Firms that have adapted their ways to incorporate new tools for customer engagement have reaped the benefits of increased market share and employee productivity. Those that have remained steadfast in their traditional ways, may now find themselves trying to play catchup. With the self-described mission of \u201cempower[ing] its customers to connect with their customers\u201d [2], Salesforce has been a leader in the pursuit of cutting-edge technologies addressing the digitalization of sales processes. The Company must continue to do so, if it is to maintain its foothold as the #1 provider of customer relationship management (CRM) software in the United States [3].<\/p>\n

An increase in the volume and types of sales metrics tracked in recent years coupled with advancements in machine learning technology have shaped the trajectory of Salesforce\u2019s product development. In 2016, the company launched Einstein<\/em>, a suite of customizable artificial intelligence tools designed to help its customers \u201cdiscover insights, predict outcomes, recommend actions and automate tasks\u201d as it sought to \u201cdemocratize artificial intelligence\u201d for its users [4]. Einstein <\/em>leverages machine learning in Salesforce\u2019s multi-tenant environment to generate tailored models for each of its enterprise users (please refer to Exhibit A) [5]. Today, the Company utilizes these models to power more than 1 billion artificial intelligence predictions each day [6].<\/p>\n

\"\"<\/a><\/p>\n

While Salesforce has touted testimonials from large enterprise accounts such as Arizona State University, Palo Alto Networks, Forrester and US Bank as proof of Einstein\u2019s <\/em>effectiveness [7], more objective reviews of the product have produced mixed results [8]. The Company has also failed to disclose meaningful statistics on user adoption and average return on investment for companies electing to adopt Einstein<\/em> products [9], bringing to question the readiness of the underlying technology for mass distribution. Nonetheless, Salesforce has forged ahead with its plans to build the world\u2019s \u201csmartest CRM system\u201d as underscored by its continued focus on product innovation and aggressive acquisition strategy. Since 2016, Salesforce has bought more than 15 businesses for an aggregate total of at least $12 billion [10]. Among these are Israeli-based Datarama, a startup specializing in artificial intelligence for marketers acquired in 2018 [11], and MetaMind, an artificial intelligence platform \u201cdesigned to predict outcomes for language, vision and database tasks\u201d acquired in 2016 [12]. The latter\u2019s founder, Dr. Richard Socher, was a visiting professor of natural language processing (NLP) at Stanford and is currently serving as Salesforce\u2019s Chief Scientist responsible leading research and development efforts for Einstein<\/em>.<\/p>\n

At its world-renowned Dreamforce Conference in September 2018, Dr. Socher introduced an innovative solution to one of sales professionals\u2019 most dreaded tasks: data entry. Einstein Voice<\/em>, as the new product is called, uses natural language understanding models to process voice notes and \u201cclassify [the] types of updates [that sales professionals] need to make to the various fields in Salesforce\u201d (please refer to Exhibit B) [13].<\/p>\n

\"\"<\/a><\/p>\n

Just a few weeks prior, Salesforce debuted an Einstein <\/em>integration for its popular business-to-business marketing platform, Pardot<\/em>, which gives users the ability to better assess prospects \u201cbased on indicators of purchase intent\u201d (please refer to Exhibit C) [14].<\/p>\n

\"\"<\/a><\/p>\n

Einstein<\/em>\u2019s ability to correctly predict customer behavior is highly reliant on the volume and quality of the underlying data that is fed into its algorithms. Herein lies one of Salesforce\u2019s core competencies and a primary benefit of increasing Einstein\u2019s<\/em> product mix and the volume of methods through which it can capture information. With operations dating back to 1999 and 150,000+ active customers [15], Salesforce has unique access to what is arguably the best pool of sales data from which to generate predictions. This, combined with Einstein\u2019s<\/em> seamless integration into existing users\u2019 dashboards, makes the competitive fight for category leader in the intelligent CRM space Salesforce\u2019s to lose.<\/p>\n

However, democratizing access to artificial intelligence requires developing a compelling enough product to warrant widespread user adoption, not just a category leading product. If Salesforce is to achieve this lofty goal, it must remain hyper focused on its customers and its people as foundations for future product development decisions. A strong relationship with its customers would enable honest feedback on existing Einstein <\/em>products while allowing the Company to remain up to speed on shifting pain points. As one of the premier recruiters for top technical talent, it is crucial for Salesforce to foster a culture that enables the professional development of its data scientists and continue monitoring the startup landscape for potential acquisitions that could bolster its research capabilities. With customer input guiding product decisions and industry leading talent executing iterations, Salesforce should be able to democratize access to artificial intelligence. The question still remains: how likely and how quickly is this to happen? Einstein\u2019s <\/em>guess is as good as mine.<\/p>\n

[798 words]<\/p>\n

    \n
  1. Victor Antonio, \u201cHow AI Is Changing Sales,\u201d 性视界 Business Review<\/em>, July 30, 2018, [https:\/\/hbr.org\/2018\/07\/how-ai-is-changing-sales], accessed November 2018.<\/li>\n
  2. Salesforce, 2017 Annual Report, p. 7, [https:\/\/s1.q4cdn.com\/454432842\/files\/doc_financials\/2017\/Salesforce-FY-2017-Annual-Report.PDF], accessed November 2018<\/li>\n
  3. \u201cSalesforce Named #1 CRM Provider For Fourth Consecutive Year,\u201d press release, May 18, 2017, PR Newswire, [https:\/\/www.prnewswire.com\/news-releases\/salesforce-named-1-crm-provider-for-fourth-consecutive-year-300460380.html], accessed November 2018.<\/li>\n
  4. Madlen Nicolaus, \u201cWelcome Salesforce Einstein: AI for the World’s Smartest CRM,\u201d Salesforce Blog, Salesforce, September 19, 2016, [https:\/\/www.salesforce.com\/uk\/blog\/2016\/09\/welcome-salesforce-einstein-ai-for-the-worlds-smartest-crm.html], accessed November 2018.<\/li>\n
  5. Salesforce. \u201cSalesforce Einstein: Machine Learning In A Multi-Tenant Environment,\u201d Salesforce. [https:\/\/www.salesforce.com\/video\/307349\/], accessed November 2018.<\/li>\n
  6. Blair Hanley Frank, \u201cSalesforce Einstein now powers over 1 billion AI predictions per day,\u201d Venture Beat, February 28, 2018, [https:\/\/venturebeat.com\/2018\/02\/28\/salesforce-einstein-now-powers-over-1-billion-ai-predictions-per-day\/], accessed November 2018.<\/li>\n
  7. Salesforce, \u201cCustomer Spotlights,\u201d [https:\/\/www.salesforce.com\/products\/einstein-analytics\/customer-stories\/], accessed November 2018.<\/li>\n
  8. Eugene Kim, \u201cSalesforce’s big new product ‘Einstein’ receives mixed reviews despite all the hype,\u201d Business Insider, <\/em>October 10, 2016, [https:\/\/www.businessinsider.com\/salesforce-einstein-mixed-reviews-despite-hype-2016-10], <\/strong>accessed November 2018.<\/li>\n
  9. \u201cCorporate Innovator: Meghann York, Senior Director of Product Marketing at Salesforce,\u201d interview by Steven Loeb, Vator, November 7, 2018, https:\/\/vator.tv\/news\/2018-11-07-corporate-innovator-meghann-york-senior-director-of-product-marketing-at-salesforce.<\/li>\n
  10. Source: Salesforce Acquisitions, Capital IQ, Inc., a division of Standard & Poor\u2019s<\/li>\n
  11. Joseph Tsidulko, \u201cSalesforce Beefs Up AI Capabilities With Datorama Buy,\u201d CRN, July 16, 2018, [https:\/\/www.crn.com\/news\/cloud\/300106601\/salesforce-beefs-up-ai-capabilities-with-datorama-buy.htm], accessed November 2018.<\/li>\n
  12. Connie Loizos, \u201cSalesforce acquires MetaMind,\u201d TechCrunch, <\/em>April 4, 2016, [https:\/\/techcrunch.com\/2016\/04\/04\/saleforce-acquires-metamind\/], accessed November 2018.<\/li>\n
  13. Michael Machado, \u201cIntroducing New Einstein Voice \u2013 You Talk, AI Listens,\u201d Salesforce Blog, Salesforce, September 20, 2018, [https:\/\/www.salesforce.com\/blog\/2018\/09\/introducing-einstein-voice.html], accessed November 2018.<\/li>\n
  14. Barry Levine, \u201cSalesforce adds process improvements to Sales Cloud, Einstein AI to Pardot,\u201d MarTech, <\/em>September 6, 2018, [https:\/\/martechtoday.com\/salesforce-adds-process-improvements-to-sales-cloud-einstein-ai-to-pardot-223771], <\/strong>accessed November 2018.<\/li>\n
  15. Salesforce, 2017 Annual Report, p. 4, [https:\/\/s1.q4cdn.com\/454432842\/files\/doc_financials\/2017\/Salesforce-FY-2017-Annual-Report.PDF], accessed November 2018<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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