Inside Salesforce’s Quest to Bring Artificial Intelligence to Everyone
Optimus Prime—the software engine, not the Autobot overlord—was born in a basement under a West Elm furniture store on University Avenue in Palo Alto. Starting two years ago, a band of artificial-intelligence acolytes within Salesforce escaped the towering headquarters with the goal of crazily multiplying the impact of the machine learning models that increasingly shape our digital world—by automating the creation of those models. As shoppers checked out sofas above their heads, they built a system to do just that.
Scott Rosenberg is an editor at Backchannel.
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They named it after the Transformers leader because, as one participant recalls, “machine learning is all about transforming data.” Whether the marketing department thought better of it, or the rights weren’t available, the Transformers tie-in didn t make it far out of that basement. Instead, Salesforce licensed the name of a different world-transforming hero—and dubbed its AI program Einstein.
The pop culture myths the company has invoked for its AI effort—the robot leader; the iconic genius—represent the kind of protean powers the technology is predicted to attain by both its most ardent hypesters and its gloomiest critics. Salesforce stands firmly on the hype side of this divide—no one cheers louder, especially not in AI promotion. But the company’s actual AI program is more pragmatic than messianic or apocalyptic.
This past March, Salesforce flipped a switch and made a big chunk of Einstein available to all of its users. Of course it did . Salesforce has always specialized in putting advanced software into everyday businesses hands by moving it from in-house servers to the cloud. The company’s original mantra was “no software.” Its customers wouldn’t have to purchase and install complex programs and then pay to maintain and upgrade them—Salesforce would take care of all that at its data centers in the cloud. That seems obvious now, but when Salesforce launched in 1999 it sounded as revolutionary as AI does to us today.
Talkin’ revolution has been good for Salesforce. The firm now has 26,000 employees worldwide, and it has pasted its name on the city’s new tallest skyscraper. Its founder, Marc Benioff, is a philanthropist who has put his own name on hospitals and foundations. Despite all this, in its own world of B2B (business-to-business) software, Salesforce still holds onto its scrappy upstart self-image.
So naturally, when the AI trend took off, the people inside the company and the experts they recruited coalesced around an idealistic mission. The team set out to create “AI for everyone”—to make machine learning affordable for companies who’ve been priced out of the market for experts. They promised to “democratize” AI.
That sounds a bit risky! Can we trust the people with such awesome powers? (Cut to chorus of Elon Musk, Stephen Hawking, and Nick Bostrom singing a funeral mass for humanity.) But what Salesforce has in mind isn’t all that subversive. Its Einstein isn’t the guy who overthrew centuries of orthodox physics and enabled the H-bomb; he’s just a cute brainiac who can answer all your questions. Salesforce’s populist slogan is simply about making a new generation of technology accessible to mere mortals. Other, bigger companies—Microsoft, Google, Amazon—may outgun Salesforce in sheer research muscle, but Salesforce promises to put a market advantage into its customers’ hands right now. That begins with the mundane business of ranking lists of sales leads.
“What do I work on next?” Most of us ask that question many times every day. (And too many of us end up answering, “Check Facebook” or “See if Trump tweeted again!”) To-do apps and personal productivity systems offer some help, but often turn into extra work themselves. What if artificial intelligence answered the “next task” question for you?
That’s what the Salesforce AI team decided to offer as Einstein’s first broadly available, readymade tool. Today Salesforce offers all kinds of cloud-based services for customer service, ecommerce, marketing and more. But at its root, it’s a workaday CRM (customer relationship management) product that salespeople use to manage their leads. Prioritizing these opportunities can get complicated fast and takes up precious time. So the Einstein Intelligence module—a little add-on column at the far right of the basic Salesforce screen—will do it for you, ranking them based on, say, “most likely to close.” For marketers, who also make up a big chunk of Salesforce customers, it can take a big mailing list and sort individual recipients by the likelihood that they’ll open an email.
But wait, what qualifies this as artificial intelligence? Anyone can tell a spreadsheet to sort a list based on different factors. The machine learning difference is simple but profound: The program studies the history of the data and figures out for itself which factors best predict the future—and then it keeps adjusting its model based on new information over time. The more data, the subtler and more powerful the answers, which is why Einstein can work not only from columns of basic Salesforce data but also from information like sales email threads that it parses and images that it reads.
Salesforce director of product marketing Ally Witherspoon uses the example of a solar-panel sales outfit using the machine learning tool to discover that a key factor in predicting a customer’s chances of saying “yes” is whether the house’s roof is pitched in a solar-friendly way. Further down the road, a different deep learning-style program could check satellite photos of different properties and automatically tag homes by roof geometry.
This roof info might start out as a major ingredient in how the machine learning program sorts its list—and, in one of Einstein’s nifty design flourishes, users can click to reveal which factors shaped each priority scoring. If users are going to trust the tool, that kind of transparency helps. But what happens when all the sales reps have learned to ignore the folks whose roofs are flat?
As Salesforce President of Technology Srini Tallapragada explains, “At a certain point, a column of data can become useless—it becomes a best practice, so it loses predictive value. The model has to keep changing.”
That is cool. It’s also pretty standard-issue machine learning tech for 2017. But to get it up and running at your company, you’d need to spend a ton of time and effort building a model that understands what’s important in your business, and then cleaning up your data to get good results. That’s the reason your bank, your insurance company, and your doctor aren’t all using AI already, explains Vitaly Gordon, who left LinkedIn in 2014 to become one of Salesforce s machine learning pioneers. Ironically, for a field predicated on the ideas of automating human work, “It’s an access to people problem,” Gordon says. These companies probably know more about you than Facebook or Google, but they can’t compete for the data scientists who know how to mine the mountains of information.
Right now, the demand for these experts is like the run on internet routing gurus in the ’90s or SEO experts in the 2000s—even crazier than the Bay Area housing market. If you’re the likes of Facebook, Google, or Amazon, you can hire the field’s leading lights and put them to work optimizing algorithms and inventing new ways of serving billions of customers with more artificial intelligence. If you’re anyone else, you’re pretty much screwed. You’ll either pay a fortune to a giant consultancy to custom-build a machine learning system, or you’ll watch from the sidelines. What Salesforce is selling is the idea that if your business is in its hands, you’re going to get the benefit of AI without fighting for that talent to customize it for you. It all comes in the box—or would, if there were a box. (Our metaphors need to keep changing, too.)