Take a deep dive into artificial intelligence.

We live in a hyperconnected world where every digital interaction — from phone call to purchase to page view — adds to a never-ending onslaught of data. And with the advent of the internet of things (IoT), even inanimate objects like cars, refrigerators, and clothing generate additional data by themselves millions of times a day.

All this data can be used to increase sales, fine-tune marketing, and provide the immediate and personalized service today's customers want. But how can your business turn a bottomless ocean of data into the steady stream of insight needed to fulfill those expectations? Artificial intelligence is the answer.

Learn more with our interactive guided tour.

Start with the AI basics

Artificial Intelligence (AI) is the concept of having machines “think like humans” — in other words, perform tasks like reasoning, planning, learning, and understanding language. While no one is expecting parity with human intelligence today or in the near future, AI has big implications in how we live our lives. The brains behind artificial intelligence is a technology called machine learning, which is designed to make our jobs easier and more productive.

A lot of things have aligned to make this an exciting time for major advancements in AI.

  • Processing power has improved at an amazing rate — there’s been a trillion-fold increase in performance over the last 60 years
  • The cost of data processing has become more affordable
  • AI has already significantly improved consumer apps — now customers expect companies to provide the same improvements across all their experiences


  • Siri and Cortana act as your personal assistants using voice processing
  • Facebook recommends photo tags using image recognition
  • Amazon recommends products using machine learning algorithms

Bringing AI to every business

AI is already transforming your customers’ expectations. Think of the consumer who lives by Uber, Google, and Amazon. If he walks into a department store to buy a suit, what does it take to provide him the same level of service he’s grown accustomed to?

Retailers should know who he is because he bought something online. They should know his size and preferences based on his purchase history. And they should be able to suggest the perfect pair of shoes to go with whichever suit he chooses.

The same principle applies across every type of business. Customers know you have their data. They know everything you can do with it. And they expect you to use it to provide fast, smart, personalized engagement across every interaction.

Think of AI as an iceberg. What you see as a user is just the tip — but beneath the surface lurks a behemoth support system of data scientists and engineers, massive amounts of data, labor-intensive extraction and preparation of that data, and a huge technology infrastructure.

It takes a specialized team of data scientists and developers to access the correct data, prepare the data, build the correct models, and then integrate the predictions back into an end-user experience such as CRM.

We’ve designed Salesforce Einstein so that all those challenges are ours instead of yours. That means everyone can now use AI to work smarter in their CRM.

Getting an AI project off the ground can be a long and painful experience. First you have to frame the business problem. Next you have to figure out the data that’s available to solve the problem. Then you have to assign significant resources and infrastructure to tackle it.

The beauty of Einstein is that all the AI technology you need is built right into your CRM apps. You get the same outcomes you would with your own AI team, but without the hassle.

Another way of asking this question is, “What will AI give me that I didn’t already have?” Three of the most-valuable and most-used outcomes will be predictive scoring, forecasting, and recommendations.

Predictive scoring — When Einstein gives you a score, it will also give you insight into how it was arrived at. For example, predictive lead scoring gives each sales lead a score representing the likelihood it will convert into an opportunity. You also get the reasons behind the score — for instance the lead source, the industry, or some other factor is an especially strong indicator that a lead will or won’t convert.

Forecasting — The predictive capabilities of AI aren’t limited to scoring; they can also be used to predict the future value of something, like a stock portfolio or a real estate investment. If you’re a sales manager, AI can predict your quarterly bookings and let you know ahead of time whether or not your team is on track to meet its quota.

Recommendations — Anyone who shops online knows that AI makes suggestions for retail purchases, but it can also make smart recommendations for any other product or service category from business software to tax consulting to cargo containers. And AI can also recommend things other than products — for instance, which white paper you should email a prospect in order to optimize your chance to close a deal.

Until now, AI was so complex and cost-prohibitive that only a select few were able to use it in a truly meaningful way. Salesforce Einstein changes that forever. Now everyone in any organization can easily use AI to analyze their data, predict and plan next steps, and automate their tasks and decisions. With Einstein’s comprehensive AI for CRM:


If you have data scientists in-house, Einstein gives them cutting-edge AI technology that helps them be even more productive. If you don’t have your own data scientists, that’s fine, too. Einstein has revolutionized AI by taking it out of the lab and making it easy for everyone to use right in their CRM. We have the world’s leading data scientists working for us — and that means they’re working for you.

Not while we’re around. AI is going to make you more productive and valuable than ever. You’ll be able to analyze information instantly and in-depth, anticipate customer needs, and automate repetitive manual tasks like data entry.

In fact, a study by Narrative Science found that 80% of executives believe artificial intelligence improves worker performance and creates jobs.

Machine learning

Machine learning is the core driver of AI. It’s the concept of having computers learn from data with minimal programming.

Machine learning works with structured data to detect patterns that provide insight. Everyday examples are personalized recommendations from services like Amazon or Netflix. In the financial arena, machine learning predicts bad loans, finds risky applicants, and generates credit scores.

Sales — It can analyze information from email, calendars, and CRM data to proactively recommend actions like the best email response to move a deal forward.

Service — It can automatically classify cases and intelligently route them to the right service agent.

Marketing — It can intelligently score the likelihood of a customer to open an email, subscribe to a newsletter, or make a purchase.

Deep learning

Deep learning is AI that uses complex algorithms to perform tasks in domains where it actually learns the domain with little or no human supervision. In essence, the machine learns how to learn.

While there’s lots of exciting experimentation happening with deep learning, most practical applications you’re familiar with are based on image analysis. With image analysis, a computer learns to classify random images by analyzing thousands or millions of other images and their data points. For example, consumer apps like Google Photos and Facebook use deep learning to power face recognition in photos.

Sales — It can analyze product images attached to a deal and use that information to suggest the best upsell and cross-sell opportunities.

Service — It can analyze images of a product attached to a service case and use the information to classify the case and route it to the right agent.

Marketing — It can analyze images across Facebook, Pinterest, and Twitter to suggest the best visuals for an upcoming advertising campaign. It can even identify brands in the images whether or not they’re mentioned in the text.

Natural language processing

NLP is AI that recognizes language and its many usage and grammar rules by finding patterns within large datasets.

One application of NLP that’s gaining traction is sentiment analysis within social media. Computers use algorithms to look for patterns in user posts across Twitter, Facebook, or other social networks to understand how customers feel about a specific brand or product.

Sales — NLP can dig through the text of emails exchanged with customers to estimate the likelihood of a potential sale, detect the best possible deals, identify deals a team is at greatest risk of losing, and provide recommended actions to improve the sales process.

Service — NLP can help route and more efficiently respond to customer emails by analyzing the written content.

Marketing — NLP can be used for sentiment analysis on text to understand how customers feel about your specific brand and products.