Examples of machine learning-enabled products aren’t as widespread in the enterprise as they are in the consumer space, but don’t expect that to be the case for much longer.
As more frontline employees and IT leaders see ML enhancing their personal lives, you can expect their appetite for machine learning-enabled tools at work to grow.
Once these AI tools become widespread in the workplace, we might just have large companies like Salesforce to thank for it. Jim Sinai, VP of Marketing at Salesforce Einstein, is one of the people who’s spent years bringing AI-powered products to market. Prior to launching Einstein, he led the team for SalesforceIQ and Salesforce Inbox — early AI-enabled products.
Jim took some time to chat with us about the way AI will impact our jobs, why it’s harder to bring machine learning to enterprise products than consumer ones, and the role Salesforce plays in making it a reality:
Sam: Great to speak to you, Jim. Let’s just dive right in. How is Artificial Intelligence going to impact sales jobs?
Jim: Well, let’s back up one step from that. As a company, we made our bet and achieved success on the migration to the cloud. We brought the cloud to enterprise companies, and in 2016, cloud computing is de facto. Now everyone is trying to figure out what will be the next major shift in technology after the cloud, mobile, and social revolutions.
The truth is, all three of those things were sort of intermediaries to the next big shift: artificial intelligence and machine learning. Machine learning will be as big of a disruption to technology as cloud computing.
So forget just sales jobs. What we need to be asking is, ‘how will AI impact any data-driven job: customer success, marketing, and sales?’ We need to understand the new ways these people will connect with customers and deliver this personalized experience. The future will be all about helping companies find new ways to leverage all of their customer data.
Sam: Why has artificial intelligence been at the center of Einstein’s story instead of machine learning — a word with less baggage?
Jim: We look at it in two ways. For the broad category of what Einstein is, we attach it to the larger narrative of AI. But ultimately, we’re focused on talking about the benefits for the customer.
For example, for someone in Sales, it’s a lead scoring model that learns with you. Sales reps hate entering information into their CRM. We automatically capture a prospect’s email, log it in Salesforce, and apply NLP to help that salesperson be smarter based on words found in that email. Einstein is everyone’s data scientist. It just works, kind of the way google just works.
Sam: Most people are skeptical when they hear companies touting artificial intelligence to describe their product’s capabilities. What do you say to those people who think Salesforce uses the term more to forward their own goals — like recruiting technical talent — than to serve customers?
Jim: The top technical talent will go to the companies working on the hardest problems. The reason every major tech vendor is talking about AI is more about evangelizing to IT leaders around the world, rather than recruiting talent. It took 15 years of evangelizing the cloud to make it generally accepted across IT.
Sam: Is there a risk of evangelizing too early, especially with AI? Thanks to movies, television, and pop culture, people picture superhuman robots when they hear artificial intelligence. Those people will lose faith in the companies that continue to over-promise.
Jim: There is definitely a risk in over-promising on AI as a broad category. But if we look at spectrum of AI from driverless cars to friction-free software, we’re only promising friction free software. We’re not saying that we’ll build something that anticipates everything you do, or that it will be better than a human assistant.
Sam: I’m going to push back on you here, Jim [laughs]. You said before that Einstein is “everyone’s data scientist.”
Jim: [laughs] That’s right! If you think about doing something as basic as scoring leads in a CRM, or predicting which opportunities will close, that’s not replacing any human sales rep function.
It’s saying to a sales team, ‘you’ve been putting tons of data in here. We’re going to make that job easier, provide insight, and make predictions available to everyone.’ The idea is to empower people that lack a skillset to to still reap the benefits. In this case, it’s helping a sales admin to implement lead scoring, identify patterns, and understand leads, all without the help of a data scientist.
Sam: What products come to mind that have actually lived up to the promise of artificial intelligence?
Jim: They mostly exist in the consumer space to be honest. Google and Facebook are examples. They use machine learning to improve our search results and personalize our newsfeed. Consumer companies have it easy. Google solves one problem once. They solve search problems for everyone, once.
Large vendors have done good marketing about what they’re doing with artificial intelligence, but the Enterprise is still at beginning of its journey. Figuring out how you actually use a customers’ data in a repeatable manner that benefits other companies is a hard problem to solve.
We have to solve multiple problems — we have to score leads, we have to find insights on open deals. These insights will be different for every company, and we will have to find scalable ways to build machine learning models differently for every company.
Sam: Thanks so much for your time, Jim. It’s been a pleasure to talk with you. Is there anything else you would like to say to people watching the space?
Jim: It’s got to be one of the most exciting times to be in this space. I’m excited for Salesforce and the industry to deliver on the promise of artificial intelligence, and not just under-delivering on just another hype cycle.
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