Signpost is a service that lets brick-and-mortar store owners publish incentives and promotions on its website. Last summer, the New York City-based company’s founder and CEO, Stuart Wall, created a new app: the A.I.-centric Mia. Through its natural language generation capability, Mia crafts messages and sends them to prospects at opportune times. It tracks and analyzes a store’s calls, emails, and credit card swipes, and then makes what it decides is the right pitch. “New customers often tell me they show up because of our five-star reviews, which I hear about through Mia,” says Randy Jewart, owner of Resolution Gardens, a landscaping company in Austin, and a Mia subscriber. People contact small businesses to learn about products and services, Wall notes, “so why waste this valuable data that A.I. can use to market to them?”
How A.I. works: problem solving
Unlike traditional computing, which delivers precise solutions within defined parameters, A.I.–sometimes referred to as cognitive computing–teaches itself how to solve problems. “Instead of delivering only specificity, A.I.-centric programming generates millions of solutions, evaluating each for efficacy and then choosing the most viable and optimal ones,” says Amir Husain, CEO and founder of Austin-based SparkCognition, which serves financial, aerospace, energy, and utility enterprises. If A.I. applications seem to be doing the thinking for you, they are.
What it does better: data driving
Manually finding your target customer–by searching and poring through income-level, interest-based, and geographical data–is labor-intensive and time-consuming. A.I. cuts to the chase. “For example, using a feed of three key pieces of information that the entrepreneur provides–a brief product-description text, images, and a price range–an A.I. system can zip through social media and other online outlets, looking for correlations between product and digital conversations,” says Husain, author of The Sentient Machine, to be published this year. A.I. also finds the targets’ contact information.
If you give it the green light, A.I.’s natural language processing technology then writes and sends a sales pitch, notes transmission time, and analyzes feedback. “You can almost hear an A.I. system going, ‘Aha! I’ve cracked the code,’ ” says Husain, adding that A.I. constantly optimizes itself by making slight changes to the message.
Where it works: practical apps
One key reason for A.I.’s upsurge is entrepreneurs’ free or inexpensive access to libraries such as IBM Watson, Google TensorFlow, and Microsoft Azure. These application programming interfaces (APIs) allow coders to build A.I. apps without starting from scratch. Enterprise-focused A.I. companies are catering to all aspects of entrepreneurship. Last year Koru, in Seattle, launched Koru Hire, predictive hiring software that uses A.I. to match job applicants’ skills and experience with profiles of a company’s best current and past employees. It generates a “fit score” that indicates whether a candidate might replicate those successes. And in San Francisco, the Grid launched A.I.-centric website-design software. It analyzes the intended content–text and images–which it separates into components, creating an array of options so the user can “build” the site in minutes. The program then interacts with the user to modify layout, color, and typography. Husain expects to see a proliferation of A.I.-centric marketing, sales, and other service startups focused on small and medium-size businesses. On tap for this summer: Cinch, from Cinch Financial, in Boston, which uses A.I. to analyze personal money data and recommends financial strategies, along with behavioral changes and new products that coincide with those behaviors.
Where A.I can help you, but not replace you
The biggest misconception about A.I. is that it’s robots with human faces sitting at remote desks. “A.I. is nothing more than an add-on technology–spice and flair–to an otherwise conventional system, such as a traditional travel-reservation site that, because of A.I., can now converse with a human,” says Bruce W. Porter, an A.I. researcher and computer science professor at the University of Texas, Austin. Porter emphasizes that future breakthroughs will not be 100 percent A.I. “A.I. will likely provide a 10 percent product- or service-performance boost,” he says. That is, in fact, huge. Firms that fail to make the A.I. leap, he says, may fail to have customers.
Not all searches are as simple as typing a few keywords and having Google take over. Entrepreneurs often need more in-depth and complicated excavations–for patent and trademark data, for example–and that, in turn, involves an often hefty legal budget to pay a highly trained human to do. Porter foresees within five years many companies offering services to consumers who have no expertise in A.I. or specific knowledge fields. They’ll be able to conduct their own A.I.-based data retrieval. Count on industry disruption, he says, as this type of A.I. application will leapfrog current data-retrieval-service providers.
Because it’s able to generate natural language, A.I. is an exceptional tool for helping entrepreneurs assemble contracts, as opposed to buying them off the shelf at, say, LegalZoom. A.I. applications will converse with–by text and, ultimately, voice–and tease information out of humans that will become components of formal agreements, such as details about fee payments and product returns. Porter anticipates users will pay to access cloud-based A.I. computer systems to produce such documents: “A.I.-centric startups, because they don’t require a human in the loop and won’t need to hire staffers, can offer their services at a very low cost, especially given an anticipated large volume of customers and business competition.”