What the heck is A.I.? And who cares?

A Dummy’s guide to Einstein A.I.

Deep learning. Artificial intelligence. Natural language processing. Machine learning. Image recognition. Recurrent neural networks.

What the heck does all that mean? And, who cares?

You should care. Really!

All the recent hype about artificial intelligence (A.I.) in the media can seem like just that – hype. When I entered the CRM world a year ago, I didn’t come across too many articles about A.I., and I didn’t pay it very much attention, as I was too busy learning how to build out a Salesforce org. Now the subject of A.I. pops up in my newsfeeds every day. The big players in the data and technology arena are taking it very seriously and are investing billions of dollars into research and acquisition.

Some very smart people are saying that the A.I. revolution is going to eclipse the industrial revolution in its scope and impact1. Really?

Consider this:

  • Google paid $600 million for an AI solution called DeepMind, which beat a human world champion at the board game Go.
  • Twitter paid $150 million for Magic Pony, an image-processing platform.
  • Microsoft paid $200 million for Equivio, a machine learning start-up.
  • Splunk paid $190 million for Caspida, a cybersecurity AI firm.
  • Apple paid $30 million for MapSense, an AI mapping company.

They say a picture is worth a thousand words, so:

ai startups

For example, there is a new A.I. loan company that can approve a loan in a few seconds with a default rate much lower than a human can achieve in several days. It is expected to approve 30 million loans in 2017. No, I didn’t make a typo – that is thirty million!

For the unschooled and initiated, artificial intelligence is a subject that seems altogether too complex and obscure to warrant serious attention, so it often gets relegated to the “too hard” basket. Big mistake!

You can understand the basics of A.I. – what it is, how it works, and what it can do. And you must understand A.I., lest you and your business join the teeming ranks of those who drift into irrelevance by sticking their head into the technological sand.

As a Salesforce admin/dev and business analyst, I believe that artificial intelligence really is going to transform the way in which billions of people live, work, and play. Disrupt is too weak a term – A.I. will revolutionise the world. Life will never be the same. Therefore, we need to make a serious effort to understand A.I., develop A.I., and implement A.I.

Unfortunately, in my research, I have found that most of the articles, blogs and videos regarding A.I. are too complex and intricate to be very useful, even for a nerd like me with a strong science and mathematics background. Someone needs to distil all of the scientific mumbo jumbo into something that the ordinary tech enthusiast can grasp and communicate. So, here we go.

1. What is Artificial Intelligence?

Although you probably have an idea of what A.I. is, let us compare three definitions:

  1. An area of computer science that deals with giving machines the ability to seem like they have human intelligence; the power of a machine to copy intelligent human behaviour. (Merriam-Webster Dictionary)
  2. The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. (Google dictionary)
  3. Artificial intelligence is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. (Techopedia)

Here is my simple definition of artificial intelligence:

A.I. enables machines to think and act like people.

Some of the activities computers with artificial intelligence are designed for include:

  1. Speech recognition
  2. Learning
  3. Planning
  4. Problem solving
  5. Analysis of human intent and sentiment

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as knowledge, reasoning, problem solving, perception, learning, and planning.

I find this infographic to be helpful:

how does machine learning work?

At a grass-roots level, there are three basic steps involved in utilising A.I.:

  1. Data gathering – Feed detailed, relevant data into the system. The better the quality and greater the quantity, the better the result.
  2. Data training – As the system learns the meaning of this data, users must test the conclusions and predictions made by the system, and continue to feed in pertinent data to correct errors and facilitate learning.
  3. Data prediction – The refined A.I. system is now able to make conclusions and predictions with increased accuracy, and more so as it is tuned and perfected.

For example, in the case of buying and selling shares:

  1. Data gathering – Enter comprehensive and detailed data into your A.I. system around company background, geopolitical events, stock history, strategic developments, and more.
  2. Data training – Continue to feed in relevant data as you test the system and facilitate its machine learning process.
  3. Data prediction – Use the refined A.I. model to give you meaningful and accurate predictive results around what and when to buy and sell.

Think of it like this. An infant grows up around his family and peers as they talk in English. He slowly absorbs a tremendous amount of linguistic data, and his young, fertile mind processes it. He begins speaking with a word or two, and quickly experiments with words, phrases, then sentences. He makes many mistakes, and learns from them, training his mind to become adept at speaking this exciting new tongue. As his expertise grows, he learns the more sophisticated nuances of body language, tone, tense, and more. It takes years of data gathering, data training, and data prediction for him to become fluent in English.

Do you get the picture? On a simple level, this pictures A.I. Of course, there is a great deal of very complex science, being performed by very smart people, in the background, making all of this happen in the technological world.

2. What can Artificial Intelligence do for your business?

You might think that A.I. has no practical applications in your business. After all, you aren’t planning on designing and building a team of infallible androids to replace your C-suite any time soon. Even though that might sound attractive. 🙂

What use is A.I. in your place of work?

Here are just a few use cases:

  1. Image recognition – remembering that 1.2 trillion photos will be taken in 2017!
  2. Service case creation, classification and routing.
  3. Sentiment and satisfaction monitoring without using surveys etc.
  4. Case escalation based upon sentiment.
  5. Data cleansing and improvement.
  6. Lead augmentation.
  7. Identification of market trends.
  8. Automated self-service.
  9. Smart data discovery
  10. Automated Analytics & Storytelling
  11. Smart Newsfeed For CRM
  12. Product Recommendations
  13. Predictive lead scoring
  14. Automated opportunity probability updates
  15. Chatbots

Consider this slide from TrailheaDX1:

Embed NLP

Imagine that you could somehow gather the data from every email received from all of your clients over, say, the last five years. You then analyse that data for customer sentiment and identify overall trends for your client base. You could employ people to do this for you, if you don’t mind paying for, say, 16,000 hours of labour to analyse two million emails at 30 seconds each. Plus, there would be significant human error involved.

Wouldn’t it great if a computer could do this for you? Well, it can2.

Here are some ideas from Trailhead3:

  • Sales Cloud Einstein: Guide sales reps to the best leads and opportunities so they can focus on closing the right deals.
  • Service Cloud Einstein: Deliver proactive service by helping customers find their own answers and recommending the right content so service agents solve cases faster.
  • Marketing Cloud Einstein: Help marketers create more personalized marketing campaigns by predicting what customers are likely to do next and recommending content and products based on audience preferences and channel.
  • Community Cloud Einstein: Personalize community experiences by recommending content, experts, and more for any customer question.
  • Analytics Cloud Einstein: Automate and prioritize the next insight you need to know.
  • Commerce Cloud Einstein: Personalize shopper experiences by recommending the right products and offers at the right time, to drive engagement, maximize conversions, and increase order value.
  • Salesforce Platform Einstein: Embed intelligence everywhere, and enable IT and app developers to build AI-powered apps for any use case.
  • IoT Cloud Einstein: Automate and predict events, and enable end users to act on their most important insights.

3. How can you implement Artificial Intelligence?

I am going to have a wild guess and assume that you do not have a team of highly-qualified data scientists, mathematicians, and the such like at your disposal, nor do you have a bank of supercomputers in your back office. How, then, do you make use of A.I. in your business?

If you employ the Salesforce cloud CRM platform, you now have access to a powerful A.I. tool knows simply as Einstein. Einstein is the result of years of research and development, plus many millions of dollars thrown in for good measure. It allows users to tap into the extraordinary resources made available by Salesforce in order to implement real-world solutions powered by artificial intelligence.

With Einstein A.I. already built into the Salesforce platform, the possibilities are almost as limitless as your ability to dream and implement them. Artificial intelligence can add power and accuracy to your Salesforce apps without it costing the earth.

“Salesforce Einstein is not a “general AI” offering that attempts human-like perception, thinking and action. Rather, Einstein is an intelligence capability built into the Salesforce platform and focused on delivering smarter customer relationship management (CRM). The features are designed to discover insights, predict outcomes, recommend actions and automate tasks.” 6

deep learning apps

One example is Einstein Intent and Einstein sentiment. I suggest that you watch the video, “Einstein – Adding Intelligence with Deep Learning” 2, as listed below, for a thorough explanation, beginning at around 18:00 (for this example scenario).

Intent and Sentiment

intent and sentiment 2

On a practical level, I suggest that you speak with your Salesforce solutions engineer for help in implementing and utilising Einstein A.I. Go ahead and complete the relevant Trailhead modules3, access the Salesforce Knowledge Base, and reach out to the Salesforce Success Community. There is plenty of help out there if you are willing to look for it. Alternatively,  you can reach out to a Salesforce Implementation Partner.


A.I. is real, it is relevant, and it is revolutionary. You can either embrace A.I. and reap the benefits, or you can ignore it, and suffer the consequences. Either way, A.I. is a game changer. What you do about it is up to you.


Helpful links:

1. Key note address to Class of 2017 at Columbia University:


2. Einstein – Adding Intelligence with Deep Learning


3. Salesforce Trailhead



4. Artificial Intelligence (AI) by Techopedia (a very digestible article)


5. An overview of Salesforce Einstein


6. Inside Salesforce Einstein Artificial Intelligence – A Look at Salesforce Einstein Capabilities, Use Cases and Challenges


7. Salesforce Einstein pricing



Note: Most images were sourced from TrailheaDX (July, 2017)



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