Cognitive Computing Energizes the Enterprise

Like it or not, humans and robots are increasingly being forced to coexist in the same environment, and there’s little anyone can do to prevent it.

This is becoming especially apparent in the CRM space, where customer service, sales, and marketing professionals are all starting to feel the impact of technological advancements in their respective fields. More software vendors are now offering software that incorporates artificial intelligence (AI), machine learning, deep learning, cognitive computing, chatbots, intelligent assistants, augmented reality, and a slew of other innovations, designed to perform the otherwise time-consuming tasks that needlessly tie up humans. The technologies also promise to process CRM data, along with other structured and unstructured data, more quickly to learn from behavioral patterns, discover solutions, and surface recommendations and insights to help customer-facing professionals do their jobs more efficiently and improve interactions with customers.

Though AI has been around for decades, the applications are coming out in droves. In April, an IDC forecast predicted that this year, worldwide spending on cognitive and artificial intelligence systems would increase by 59.3 percent to reach $12.5 billion. Of that amount, 9.8 percent is likely to encompass automated customer service agent tools. This suggests that these systems have really come into their own and can now be used by organizations of all sizes and experience levels.

There is a lot of buzz in the air, but the hype is not unfounded. To keep up in today’s competitive business landscape, companies that automate areas that can be simplified for the sake of a better customer experience will lead the way. And experts agree that all companies should at least be figuring out where to invest to get the ball rolling.

SO WHAT’S SO SPECIAL?

Michele Goetz, principal analyst at Forrester Research, says it’s important to understand what makes a solution “cognitive” or “intelligent,” and what doesn’t.

Chatbots, for example, have typically worked in a programmatic, or deterministic, fashion, meaning they can identify certain keywords or phrases that will indicate to which type of agent a case should be routed within the call center. A financial institution’s chatbot might pick up the term “loan” or “account balance” and then link that customer to the agent most qualified to help.

But “today’s intelligent chatbots—you can think of them as cognitive agents—are very different,” Goetz says. These systems, she points out, go steps beyond, as they can take voice-of-the-customer information, call center notes and recordings, facts, email info, and other components of a conversation before moving the case into recognition analysis. At this point, the assistant can understand the types of questions being asked, why they are being asked, when those interactions are taking place, and the results that they produce.

When a customer asks about his account balances, an intelligent assistant can reason that he might be interested in seeing his last five transactions. Or it might surmise that the customer could be concerned about potential fraud. Registering this, it can surface information to the agent who will take the call, or to the customer directly. “It’s much more evolutionary. It’s aware. It’s adaptive, and that’s where the intelligence comes from,” Goetz says.

This kind of functionality can be applied in many ways to benefit customers. A financial institution can use the technology to help customers with portfolio reallocation. In such scenarios, the intelligent assistant can guide the financial adviser as he works. It can observe the conversations, acting as a search mechanism that helps the adviser find the right information across different systems and resources, so he can make more intelligent recommendations faster. Instead of an agent having to write down the customer’s information during a call and get back to him in a week with a proposal, the interaction can now happen in near real time.