Tips for jumpstarting your journey to developing AI apps
Use cases and tips to help businesses take full advantage of AI technology.
“AI won’t replace managers, but managers who use AI will replace those who don’t.” So write Erik Brynjolfsson and Andy McAffee in the Harvard Business Review. That’s an important point to which all managers must pay attention: AI isn’t a technology for assisting humans; it’s for augmenting them. For managers, it’s for helping them make better decisions, and for enabling their staff to be more effective. Programmers can use AI to build products that are better at taking their customers’ needs and requirements into account—and they can build new kinds of products that help their customers be more effective. We’re entering an era in which whoever helps others to be the most effective will win. Those solutions will certainly involve AI.
But how does an enterprise take advantage of AI? It’s not by pouring magic AI sauce over everything in an effort to get ahead of some mythical curve. That’s a recipe for expensive mistakes. To get ahead with AI, enterprises must make plans that are in line with their business objectives, and that help you to be more effective at what you already do. AI isn’t a strategy; it’s part of an overall strategy. And it isn’t about replacing humans; it’s about augmenting them, making them better at what they do. Likewise, enterprises need to use AI to do what they already do, but better.
How can your enterprise take advantage of AI? Here are a few use cases and tips:
Most people hate calling customer service: there are long waits, and tired front-line support staff who frequently can’t solve their problems. A chatbot can be a great tool for answering the phone, handling the simple questions (often, most of them), and forwarding the difficult questions to the human support team. Customers don’t wait as long; staff isn’t as burdened, and doesn’t have to spend time on the easy questions; and the AI never gets tired or bored. Autodesk made their customer support staff much more efficient by using IBM’s Watson Conversation to build a chatbot to handle simple requests, like getting software activation codes.
AI systems are excellent at classifying images. There are many enterprise applications of classification, in industries ranging from health care to agriculture. Systems can be built that fly a drone over a field to take pictures of crops, then use AI to determine whether the crops are healthy. Similar systems are used to inspect manufactured items for flaws, or to recognize faces to allow entry. In the last California drought, a company called OmniEarth used Watson’s Visual Recognition Service to identify properties that needed to reduce water consumption by identifying swimming pools, water-intensive landscaping, and other features.
Don’t underestimate the time it will take to build the AI systems you need. But don’t get discouraged, either; while AI experts are in short supply, you don’t need experts unless you’re doing basic research. You need a platform that gives you tools, APIs, and infrastructure. That’s what IBM, along with Amazon, Microsoft, and Google, have been building. Their job is to hire the experts who can implement the low-level algorithms for speech recognition, computer visions, and other tasks. Your job is to understand your business problems, and to use the platforms to make your staff more effective. That’s what will enable your business to grow in the coming century.
30 or so years ago, I read about a manufacturing company that proudly said it had never used computers, and never would. They kept their customer lists on index cards, and I don’t want to think about how they did accounting. These days, their sign still exists, but the company doesn’t; their building is a historic landmark that has been converted into apartments. If Brynjolfsson and McAffee are right, AI may be as important to tomorrow’s successful enterprises as computing was to the leaders of the 20th century. AI isn’t magic; it’s about solving real-world business problems by making people more effective.
This post is a collaboration between O’Reilly and IBM. See our statement of editorial independence.