I keep having the same conversation with companies that want to brief me on their latest AI-related news. It goes something like this: They have some new feature or improved stats or fresh achievement that’s specific to their business. It’s a great win for them internally, but not exactly the most compelling news in and of itself. Then we get to the AI part, and I start to salivate, asking them to tell me more. The reply is increasingly some version of “We use [a mostly unexciting AI tool] to do it.”
Being accustomed to covering technology for technology’s sake, I always feel a little let down. Given how rich, complex, and world-changing AI technologies can be, I keep expecting a dazzling tale of technological magic.
But I’ve come to see my lukewarm response as immature and arrogant. I was thinking like kids who see their parents — who have great jobs and provide a nice life for them — as severely uncool because they aren’t professional athletes or movie stars. Sure, AI is sexy when it’s theoretical or a moonshot, but in the real world the smartest people in the room are those actually using it to do things for their companies.
Here’s just a smattering of recent examples:
Booking.com needs to predict its customers’ travel needs in volume, from flights to lodging to events. As the service has to be personal and make excellent recommendations, the company’s solution is to build a graph for its customers so their questions can be answered as quickly as possible. This involves quite a mix of AI. Ram Papatla, VP of experiences at Booking.com told me in an interview that there may be as many as 20 AI techniques employed on the Booking.com homepage at any given time. That mix includes chatbots and numerous machine learning models, but these, Papatla pointed out, are just commodities. The key is the customer graph.
ZipRecruiter is using a deep learning-based recommendation algorithm and natural language processing (NLP) to match potential employees and employers. The process started with a large investment in AI, first in machine learning and then in deep learning. The company used “candidate calibration” to train the algorithm on what particular employers were looking for and ideally match them to job candidates. But there was still a disconnect — a social engineering problem — because it was taking too long for employer and employee to make the needed social connection. And ZipRecruiter realized that having companies recruit job seekers would actually work better than the more traditional application method.
Part of the solution to this problem is Job Seeker Profiles, an AI-powered tool that helps job candidates gussy up their profiles to attract potential employers. For example, if the system sees that you entered “I am a nurse,” it will automatically ping you to add more information and better quantify your experience. It also gives job applicants insight into who is looking at their profile and how.
This has led to Get Recruited, which is the realization of a three-year plan. Since the feature’s launch in April, employers have sent 4.38 million unique messages to job seekers. Some 618,000 jobs have been filled, with a run-rate that predicts more than 1.94 million hires over the next 12 months. The number of filled jobs on ZipRecruiter on the whole rose 19% in July 2019 from prelaunch numbers in March.
Then there’s AB InBev, one of the world’s largest beer brewers, which is actively fostering AI innovation in its “Beer Garage” — served up with a buzzword side salad. “Beer Garage is also scaling our existing capabilities in AI and ML, IoT, Cloud and Data Analytics, Automation, and Robotics, and exploring emerging technologies such as Blockchain, AR and VR, and others,” reads the site in part. It sounds like the sort of place where exactly nothing ever happens except for beer consumption, but it has already produced some key AI tools that are helping the company improve its processes.
One of those tools is SensAI, which uses machine learning to help brewers keep an eye on various aspects of the brewing process, like temperature and timing, to ensure quality and reduce waste. There’s a whole advertising wing called Alehouse Creative that intelligently tracks how various ad placements are performing and helps the human team generate more effective ads. AB InBev’s AI tools extend to its AB-Credit, which uses machine learning to look at variables for its individual retailers (even small ones) and extend additional credit so they can better manage their inventory.
These are examples of AI at work in three significant companies, but large or small, businesses with AI-powered solutions tend to follow the same patterns. The key is that instead of falling in love with some shiny AI tool or technique, smart companies are trying to solve real problems and make better products and services. And although you typically need people with special skills to build or implement AI capabilities, project managers are still in the driver’s seat.
Hillary Mason, GM of machine learning for Cloudera, spelled it out plainly in an earlier interview with VentureBeat, pushing against the idea that data scientists and their ilk should be driving AI projects within companies. “I think that’s wrong and that the people who are best positioned in an organization — [who] recognize where the opportunities lie — are not the data scientists and the technologists themselves, but … the people who actually own the product, who live with it every day, who work with the customers and clients. [These are] the people who are integrating all of that information to build a mental model of decision of where that product and … that business [are] going to go. Those are the people who are best positioned to recognize where AI should fit into it,” she said.
You can see this approach working in real companies today. There’s nothing particularly flashy about improving inventory management for small retailers, giving a nurse tools to make an online job profile stand out, or helping someone book a hotel with a little less time and hassle. But those sorts of improvements add up, brick by proverbial brick, and they all depend on AI.
Businesses simply can’t afford to sleep on new technologies that could completely change their industry or even just give them an edge on the competition. It was true of the internet boom, of social media, mobile, and the cloud, and it is most definitely true of AI. But the winners will be those who see AI as a set of tools and technologies — not the thing, but the thing behind the thing — and use it to do the unglamorous work of solving business problems, offering better services to customers, and improving their products.