Every business has customers. Every customer needs care. That’s why CRM is so critical to enterprises, but between incomplete data and clunky workflows, sales and marketing operations at most companies are less-than-optimal.

At the same time, companies who aren’t Google or Facebook don’t have the billion dollar R&D budgets to build out A.I. teams to take away our human efficiencies. Even companies with the right technical talent don’t have the petabytes of data that the tech titans use to train cutting-edge neural network models.


Enterprise A.I. Shouldn’t Be Impossible

Salesforce hopes to plug this A.I. knowledge gap with Einstein. According to Chief Scientist, Richard Socher, Einstein is an “AI layer, not a standalone product, that infuses AI features and capabilities across all the Salesforce Clouds.”

The 150,000+ companies who already use Salesforce should be able to simply flip a switch and deploy A.I. capabilities to their organization. Organizations with data science and machine learning teams of their own can extend the base functionality through predictive APIs such as Predictive Vision and Predictive Sentiment Services, which allows companies to understand how their products feature in images and video and how consumers feel about them.

The improvements are already palpable. According to Socher, Salesforce Marketing Cloud’s predictive audiences feature helps marketers hone in on high-value outreach as well as re-engage users who might be in danger of unsubscribing. The technology has led to an average 25% lift on clicks and opens. Customers of Salesforce’s Sales Cloud have seen a 300% increase in conversions from leads to opportunities with predictive lead scoring while customers of Commerce Cloud have seen a 7-15% increase in revenue per site visitor.

Achieving these results has not been cheap. Salesforce’s machine learning and A.I. buying spree includes RelateIQ ($390 million), BeyondCore ($110 million), PredictionIO ($58 million) as well as deep learning specialist Metamind of which Socher was previously founder & CEO / CTO. Marc Benioff has spent over $4 billion  to acquire the right talent and tech in 2016.


The Competition in Enterprise Tech Is Intense

Even with all the right money and the right people, rolling out A.I. for enterprises is fraught with peril due to competition and high expectations. Gartner analyst Todd Berkowitz pointed out that Einstein’s capabilities were “not nearly as sophisticated as standalone solutions” on the market. Other critics say the technology is “at least a year and a half from being fully baked.”

Infer is one of those aforementioned standalone solutions offering predictive analytics for sales and marketing, putting them in direct competition with Salesforce. In a detailed article about the current A.I. hype, CEO Vik Singh challenges that big companies like Salesforce are “making machine learning feel like AWS infrastructure” which “won’t result in sticky adoption.” Singh adds that “Machine learning is not like AWS, which you can just spin up and magically connect to some system.”


Challenges Are Real But Surmountable 

Chief Scientist Socher acknowledges that challenges exist, but believes they are surmountable.

Communication is at the core of CRM, but while computers have surpassed humans in many key computer vision tasks, natural language processing (NLP) and natural language understanding (NLU) approaches fall short of being performant in high stakes enterprise environments.

The problem with most neural network approaches is that they train models on a single task and a single data type to solve a narrow problem. Conversation, on the other hand, requires different types of functionality. “You have to be able to understand social cues and the visual world, reason logically, and retrieve facts. Even the motor cortex appears to be relevant for language understanding,” explains Socher. “You cannot get to intelligent NLP without tackling multi-task approaches.”

That’s why the Salesforce AI Research team is innovating on a “joint many-task” learning approach that leverages transfer learning, where a neural network applies knowledge of one domain to other domains. In theory, understanding linguistic morphology should also also accelerate understanding of semantics and syntax.

In practice, Socher and his deep learning research team have been able to achieve state-of-the-art results on academic benchmark tests for main entity recognition (can you identify key objects, locations, and persons?) and semantic similarity (can you identify words and phrases that are synonyms?). Their approach can solve five NLP tasks – chunking, dependency parsing, semantic relatedness, textual entailment, and part of speech tagging – all at once and also builds in a character model to handle incomplete, misspelled, or unknown words.

Socher believes that A.I. researchers will achieve transfer learning capabilities in more comprehensive ways in 2017 and speech recognition will be embedded in many more aspects of our lives. “Right now consumers are used to asking Siri about the weather tomorrow, but we want to enable people to ask natural questions about their own unique data.” For Salesforce Einstein, Socher’s team is building a comprehensive Q&A system on top of multi-task learning models.


A.I. Research Is Hard, Operationalizing Workflows Is Harder

Solving difficult research problems is only step one. “What’s surprising is that you may have solved a critical research problem, but operationalizing your work for customers requires so much more engineering work and talented coordination across the company,” Socher reveals.

“Salesforce has hundreds of thousands of customers, each with their own analyses and data,” he explains. “You have to solve the problem at a meta level and abstract away all the complexity of how you do it for each customer. At the same time, people want to modify and customize the functionality to predict anything they want.”

There are three key phases of enterprise A.I. rollout: data, algorithms, and workflows. Data happens to be the first and biggest hurdle for many companies to clear. “In theory, companies have the right data, but then you find the data is distributed across too many places, doesn’t have the right legal structure, is unlabeled, or is simply not accessible.”

Hiring top talent is also “non-trivial”, as computer scientists like to say. Different types of A.I. problems have different complexity. While some A.I. applications are simpler, challenges with unstructured data such as text and vision mean experts who can handle them are rare and in-demand.

The most challenging piece is the last part: workflows. What’s the point of fancy A.I. research when nobody uses your work? Socher emphasizes that “you have to be very careful to think about how to empower users and customers with your A.I. features. This is very complex but very specific. Workflow integration for sales processes is very different from those for self-driving cars.”

Until we invent AI that invents AI, iterating on our data, research, and operations is a never-ending job for us humans. “Einstein will never be fully complete. You can always improve workflows and make them more efficient,” he concludes.