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The Tipping Point for Big Data and AI

By February 2, 2015Article

Consider the iPhone. Once a totem of the tech-savvy few, it at first didn’t penetrate the wall of “how we do things here” in large corporations. Most employees used the standard-issue cellphone and carried a laptop to access email or take care of simple office tasks. 

And then, just like magic, we hit a tipping point. Smartphones are now an indispensable business tool of the “how did we function without this before?” variety. In a recent survey, nearly half of U.S. adults noted they would not last a full 24 hours without their smartphones. 

We’re now on the cusp of a new tipping point — the emergence of Artificial Intelligence (AI) -powered big data solutions. 

As big data continues to get bigger, the advancement of AI in the workplace becomes inevitable. It will move from a “cool new thing” to “the necessary thing” that powers essential tools and technologies critical to doing business efficiently and effectively. 

Companies must take advantage of all the data available to remain competitive, and the only way they will be able to do it is with the help of intelligent machines that can provide the necessary analysis and understanding of all data available. 

The sheer scope of information is already beyond what humans are capable of processing, understanding and using. According to IBM, 2.5 quintillion bytes of data are created each day. That’s an incredibly accelerated rate from the past — to the point that 90 percent of the data in existence was created in the past two years. 

Companies continue to gather as much data as they can possibly get. One study estimated that 1.8 zetabytes of business data were in use in 2011, 30 percent more than the previous year. Forward-thinking companies trying to get a handle on this information admit that simply analyzing what has been gathered is the number one challenge — a challenge driving the need for better methods of machine-to-human communications. 

Rising to meet this challenge are companies bringing commercial AI-based technologies to the market. Hand in hand, data plus AI enables scale and allows companies to effectively communicate to internal and external audiences in a way that is meaningful, useful and trustworthy — something that can’t be done without these new applications. 

Freeing up the workforce 

We’ve already seen the impact of automation in industrial work. When machines take on repetitive tasks, it allows people to engage in higher-value activities. Now, AI-powered technologies are finding their way into the world of the knowledge worker, taking on rote tasks that require a good deal of employees’ time, freeing them to focus on more meaningful work. 

Imagine a financial advisor working for a large wealth-management firm with thousands of customers. To advise just one customer, that advisor must log into a system, understand the holdings of a particular customer, write up a summary and then create tailored advice based on that analysis. With AI-powered analysis and language generation applications, this process is automated, providing wealth-management clients with personalized, timely performance summaries and updates, conveyed meaningfully through narrative communications. 

When a machine performs this work, advisors have an opportunity to understand investments at a much deeper level. If they are picking stocks, they can spend more time learning about the companies and make far more informed decisions about portfolio selection as opposed to looking at a spreadsheet, interpreting the information and putting results into a document for somebody else. 

That same process of analyzing, sorting and presenting information exists in almost every industry, so as AI-fueled technologies become more integrated into knowledge work, organizations will function in a completely different way than they do now. For example, in the healthcare industry, doctors now use and trust IBM Watson to assist in diagnosing patients, expediting the research process and allowing physicians to more efficiently and effectively treat patients.  

Deepening customer relationships 

The more information you can give customers and the more meaningful touch-points you have, the greater loyalty you get in return. New AI applications enable companies to use all of the customer data they’ve aggregated and deliver it in a way that will make their customers’ lives easier and help them make more-informed decisions. 

For example, a credit card company can help families understand their budgets simply by giving them reports each month that explain how much they are spending, comparisons over the past three months and comparisons with similar families in their demographic. The company can then use that information to make decisions or to think about expenses in a new light. In return, the customers get something of value and feel good about their experience with that credit card issuer. It’s not just a piece of paper that comes with a list of transactions to be verified every 30 days; it’s real information that can be acted on to create brand loyalty. 

Keeping up with demand 

Businesses accepted iPhones because people were using them in their personal lives and demanded them in their work lives. Right now, consumers in their everyday lives are using tools and technologies powered by AI. For example, Siri recommends restaurants or reminds you to call your mother, and Google now gives you sports updates and tells you where you parked your car. 

Machines are getting smarter all the time. They constantly do more for you and with you. Your employees and customers are increasingly using these tools, and it’s only a matter of time before they expect the same level of innovation and service from your company. 

We’re nearing the tipping point, so get ready or your company will be left behind. You don’t want to be the only company still using the standard-issue flip phone. 

Stuart Frankel is CEO and co-founder of automated narrative generation company Narrative Science, which he founded with Kris Hammond and Larry Birnbaum. Prior to Narrative Science, Stuart was CEO of the Performics division of DoubleClick and an SVP at DoubleClick. Stuart was a member of DoubleClick’s senior management team when the company was sold to Google. Follow Stuart on Twitter @stuartfrankel and Narrative Science @narrativesci.