The Blog

  • May 9, 2019
  • Disruption plus

    There’s a chicken and egg issue with digital disruption. Making decisions based on numbers instead of gut instinct is recognized to be a superior approach in many situations, but before you can get to decision-making, people have to be able to use things like AI and machine learning. Humans are not naturals when it comes to numbers; thinking back to high school algebra is all it takes to convince most of us.

    Humans are really good at things like relationships and reading faces. So there should be a natural association between providing crunched numbers to customer-facing employees and their use. But before you can expect employees to take on thinking with numbers more than they ever have, it’s got to be dead solid easy to crunch the numbers and deliver their meaning. For much of the AI universe so far that crunching and delivery has been focused on things involving a next best algorithm. Next best offer in sales perhaps, or next best service solution in customer service. But there’s a lot more we can do.

    Salesforce delivers Einstein analytics for a broader audience

    Today, Salesforce announced four new products based on Einstein, its analytics engine, that are designed to spread analytics to more parts of an organization and to enable more types of employees to work with the tools. All introductions support clicks or code thus enabling admins and developers to access functionality according to their skill levels. Briefly the introductions include,

    1. Einstein Translation which enables admins and developers to set up automatic language translation. If a user enters data in a different language the system instantly converts to that language. There was no statement, however, about how many languages are initially supported. The product is in pilot so look for more information later.
    2. Einstein Optical Character Recognition (OCR). OCR has been around a long time because it works and is an important part of scraping usable data off documents. Initially Salesforce sees this as a way to streamline data entry. Also in pilot.
    3. Einstein Prediction Builder enables admins and developers to build AI models for apps running on the Salesforce platform with a declarative setup tool. Generally available.
    4. Einstein Predictions Service enables admins to embed Einstein AI analytics into third party systems like ERP or HR. Also generally available.


    In a move that seems like a commentary on the troubles that social media companies are having, Salesforce also restated its commitment to its core values, especially trust in this case. The company went out of its way to state that its AI products are transparent, responsible and accountable. For instance, the system provides users with justifications for predictions based on which factors influence a prediction. Also, protected fields warn of potential bias in datasets with pop-up alerts. And Model Metrics evaluate the accuracy and performance of AI models. If only things like this were available in social media.

    My two bits

    A few years ago, when sales analytics were the only analytics game in town, I remember some emerging vendors telling me it was hard to get customers to use their tools to develop their own unique analyses. They were happy to use all of the reports that came with the tool out of the box though, which led to delivering a large number.

    In my experience this rang true because sales veterans (and I am one) seem highly attached to their unique approaches. At the time I thought that asking them to develop their own analyses was akin to asking a fish to invent fire. In the years since, I discovered that sales people were not unique. So making analytics’ use as easy as possible is a pre-requisite for getting on with a company’s digital disruption.

    Clicks and code, the two approaches Salesforce emphasized in this announcement are not out of the ordinary for most things the company enables. They want to reach the broadest audience possible for their solutions and that’s good. But it has extra importance at the intersection of digital disruption and analytics.





    Published: 5 years ago

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