The AI-Powered Enterprise. Seth Earley

a woman’s voice answers. Meryl is the bot for Bank of America/ Merrill Lynch, where Perkins’s investments reside. “What can I do for you today, Allen?”

      “I’m a little nervous about the choppiness in the stock market. How am I doing these days?”

      “Your net worth with us has risen by 2% in the last twelve months,” Meryl answers. “While the market has been going up and down by an average of half a percent a day for the last few weeks, we’ve got you in a pretty conservative mix of investments. So the value of your investments hasn’t been moving quite as much.”

      “Are we on track with the kids’ college funds?”

      “The investments for Ray and Sarah are likely to be sufficient to cover their tuition when they start college a few years from now,” Meryl replies. “I’ve been shifting those assets into more conservative positions because they’ll be in college so soon.”

      “Is there a lot of cash in my account?”

      “We’re about 10% in cash right now, Allen.”

      “Can we invest some of that?”

      “Sure, we could. But you’re going to need cash for that remodeling project you told me about last month. There’s not a whole lot lying around in your checking account right now. And don’t forget you just bought a bunch of new suits to go along with your promotion.”

      “OK, leave the cash where it is,” Perkins says. “But let’s check back on this in a month or so. I may be getting a bonus.”

      “Sure thing, Allen,” Meryl says. “Talk to you in February.”

      Perkins’s Tesla pulls into a parking space near the front door and he strides into his office. After checking his emails, he begins the most important work of the day: selecting a new component supplier for six new locomotives for the California High-Speed Rail project.

      He opens a browser and navigates to GraingerBot, a chatbot available from Grainger, one of the biggest suppliers to manufacturing companies like Hecker. He starts typing.

      Hello, GraingerBot.

       Hello, Mr. Perkins.

      Let’s continue ordering parts for the California locomotives.

       I have opened the file that we were working on yesterday. What would you like to work on?

      Suspensions. Can you locate the suspension springs in the 3-D model?

       I see eight helical springs that are 36.3 centimeters high.

      What suppliers make springs like that?

       Several make the chromium-vanadium steel specified in the drawings. But I’ve found some research that shows a new material may allow you to get more durability for the same cost. Shall I show it to you?

      Please.

      The GraingerBot pops up a twelve-page document from the International Journal of Engineering Research. Perkins reviews it and confirms that the GraingerBot is correct—this new material appears to be superior for this part. He resumes the typed conversation.

      How many manufacturers can make this part from the new material?

       Seven. That includes one parts supplier that you’ve already used for six other components; they’ll give you a discount if your order pushes the total over $35,000.

      Get quotes on those, GraingerBot. And get a few quotes on the chromium-vanadium springs, too, just in case we decide to stick with them.

       I’ll have those answers for you by 12:30. I’m connecting with the bots for those parts suppliers right now.

      Perkins signs off. This has been productive. While he would have liked to complete the order for the springs now, it looks like he might have found a more durable material—and with Hecker now doing the maintenance as well as the manufacturing, that would save the company some serious cash.

      Perkins turns his attention to an upcoming business trip. He is going to Sacramento to meet with the California transit team that is buying these locomotives. He puts on his Bluetooth headset and connects with Pete, his virtual travel agent from Expedia.

      “Pete, get me a flight to Sacramento for next Tuesday.”

      “Sure thing, Mr. Perkins,” says Pete. “When will you be returning?”

      “Wednesday or Thursday. Look into both.”

      “OK. I’m checking American Airlines, because that’s where you have the best status. I have 18 possible flight combinations. I’m afraid you will have to connect.”

      “Yeah, nobody flies nonstop Dayton to Sacramento! Don’t route me through Chicago or Denver. Too much snow this time of year,” Perkins says.

      “That leaves nine possible flights. Is this for the two o’clock meeting with the California rail project team?”

      Perkins remembers that he has given Pete access to his schedule, and realizes the bot is figuring out flights that fit his existing appointments.

      “Yes, that’s the meeting,” he says.

      “I’ll make sure your flight gets in well before that. I see that one of your manufacturing partners is based in that area. In fact, it looks like GraingerBot is connecting with them right now to check on some springs. Should I set up a meeting?”

      “Definitely. See if you can get me in to see them on Wednesday. I’d love to tour that facility.”

      “Do you want to take a red-eye back?”

      “No way,” Perkins replies. “Better make it Thursday morning.”

      “OK, I see the best roundtrip now. It leaves at 7 a.m. on Tuesday and your return flight takes off at 8 a.m. on Thursday. Will you be staying at the Hilton at the airport again? They’ve got the best price for a three-star hotel.”

      “Go ahead and book it. And get me a rental car, too. Thanks, Pete.”

      Perkins muses for a moment about travel reservations. All that clicking and checking he used to do. . . . But the bots pulled it together in a way he never could have before. Perkins likes the feeling of control that gives him—and the efficiency, too.

      Perkins doesn’t need to think about what is going on behind the scenes to make investing, manufacturing, and travel so much easier than they used to be. But every time he interacts with a bot, he is drawn deeper into an ecosystem of artificial intelligence. He has grown to rely on the companies that were making his life easier—those that were ready with AI. Everybody else is working with those companies, too, and the competition is slowly fading into irrelevance.

       WHAT IT TAKES TO SUCCEED IN THE AI FUTURE

      We’re on the verge of the future that Allen Perkins lives in. But not every company will make it into that future.

      We know how we want our companies to work. Enterprises ought to be customer-focused, responsive, and digital. They should deliver to each employee and customer exactly what they need, at the moment they need it. The data and technology to do this are available now.

      Any big company is likely to have an abundance of technology. It has systems for customers, inventory, and products, along with websites and mobile apps. These systems are spitting out data all day long. Within that data is exactly the information needed to make a business more responsive. The problem is, the data is often not used as it could (and should) be.

      Artificial intelligence ought to take that data and turn it into effective execution, just as it did for Allen Perkins. IBM’s Watson and Amazon’s Alexa seem pretty smart. But despite the billions of dollars spent so far on bots and other tools, AI continues to stumble. Why can’t it magically take all that data and make an enterprise run faster and better? Why can’t it deliver the experience that Allen Perkins would