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Depending on who’s listening, the current discussion involving the growing role of Artificial Intelligence in business inspires a range of dramatically divergent emotions. There’s often fear, because of what some believe to be AI’s vaguely sci-fi vibe and dystopian possibilities. Among business people, there is also confusion, on account of the inability of most laypeople to separate AI hype from AI fact. Apprehension also looms large, usually from managers who sense that a great wave of technology disruption is about to hit them, but who feel utterly unprepared for it.
But from our experience with Fortune 500 companies, we’ve come to believe that the proper response by business leaders to AI should be more benign: appreciation. Whatever anxieties it might produce, the fact is that AI is ready today to bring a trio of new efficiencies to the enterprise. Specifically, scores of companies have learned how AI technologies can transform how they process transactions, how they deal with data and how they interact with customers.
Better still, they have been able to take advantage of this AI triad without turning themselves into an Internet Giant and hiring huge new teams of hard-to-find, and not to mention expensive, data scientists. AI products are available now in nearly turnkey form from a growing list of enterprise vendors. True, you and your IT staff will need to do a certain amount of homework to be able to evaluate vendors, and to make sure product implementations map on to your precise business needs. But doing so isn’t a heavy lift, and the effort will likely be rewarded by the new efficiencies AI makes possible.
Companies are benefiting from AI right now, in ways that are making a difference on both the top and bottom line.
“Robotic and Cognitive Automation” is the name we at Deloitte give to AI’s ability to automate a huge swath of work that formerly required hands-on attention from human beings. The most popular form of R&CA involves gathering data from disparate sources and bringing them together in a single document. An invoice, for example, usually cites a number of sources, each of which stores relevant information in slightly different formats. An R&CA system has the intelligence necessary to transcend the usual literal-mindedness of computer systems, and process the information it needs despite the fact that it might have different representations in different places.
As AI techniques have become more robust in recent years, so too have the capabilities of R&CA packages. Now, instead of simply pulling spreadsheet-type data from sundry sources, they can process whole passages of text. Not as well as a human being can, for sure, but enough to get a general sense of the topics that are being covered. As a result, there are now R&CA systems that can “read” through emails and flag those that might be relevant to a particular issue. Such systems are now commonly found, for example, at large law practices, which use them to search through huge email libraries to discover which materials might need to be produced in connection with a particular bit of litigation. This is the sort of routine work that previously required paralegals.
Another cluster of AI applications involves the ability to make better use of a company’s data; these go by the name of “Cognitive Insights.” These tools allow companies to manage the flood of information they collect every day, from business reporting tools to social media accounts. More importantly, it gives businesses the ability to use that information to generate real-time insights and actions.
Consider just one area in which these new AI capabilities can be useful: digital marketing. Staffers running email campaigns can now improve click-through rates by using their AI-acquired knowledge of each customer’s personality to determine which words or phrases in the subject line might be more likely to get the person to read the email. Small changes can make a big difference; reports of double-digit increases in opened emails are common with AI.
Finally, AI is fundamentally changing the way companies work with their customers. This is occurring everywhere, but is most common with interactions with millennials. This cohort grew up with texting on their mobile phones, and is often more comfortable interacting with an app than with a human being.
As a result, millennials are extremely receptive to a new breed of automated customer service applications that AI is making possible. (These are vastly superior to the rudimentary “chatbots” that some companies used in the early days of the Web.) With advances in the AI field known as Natural Language Processing, computers are now able to deal with the sorts of real-world questions that customers are likely to ask, such as “Why is this charge on my credit card statement?” Deploying computers for these types of routine inquiries allow companies to deliver a uniform, high-quality customer experience while simultaneously improving the value of your brand.
You’ve probably noticed that while AI is often described as the equivalent of “thinking machines,” all of the tasks described above are relatively discrete and well-defined. That’s because for all the progress that’s been made in AI, the technology that still doesn’t come close to being able to match human intelligence. AI products perform specific tasks just fine, but don’t expect them (yet) to handle everyday human skills like professional judgment and common sense.
What’s more, AI can’t be used to paper over inefficiencies in a business, whether they be strategic or operational. If the processes you’re using AI for aren’t fundamentally sound to begin with, the new technology won’t be of any help, and may exacerbate problems by hiding them behind added layers of software. You’ll need to use some old-fashioned intelligence to take a good, hard look at your organization before trying to take advantage of the new, artificial variety. It will, though, be well worth the effort.
Jeff Loucks is the executive director of the Deloitte Center for Technology, Media and Telecommunications. In his role, he conducts research and writes on topics that help companies capitalize on technological change. An award-winning thought leader in digital business model transformation, Jeff is especially interested in the strategies organizations use to adapt to accelerating change. Jeff’s academic background complements his technology expertise. Jeff has a Bachelor of Arts in political science from The Ohio State University, and a Master of Arts and PhD in political science from the University of Toronto.
Mic Locker is a director with Deloitte Consulting LLP and leader of its Enterprise Model Design practice. With more than 15 years of consulting experience, and more than three years of operations experience, she specializes in leading organizations through transformational changes ranging from business model redesign and capability alignment, process reinvention, operational cost reduction, and new business/product launches.