Cognitive Computing Energizes the Enterprise

Steve Laughlin, vice president and general manager of IBM’s global consumer industry division, points out that his company’s Watson APIs can be accessed via the BlueMix app development platform to help create and test programs before they are actually deployed.

Among the uses he’s seen already, some companies are turning the APIs into programs to help them understand personality insights from written texts on social media or to analyze images that customers have already seen to determine the next set of images they will likely want to see.

Staples, for one, recently used IBM Watson APIs to create a real-life Easy Button based on its plastic marketing icon. After doing design thinking with B2B customers of all sizes, the office supply superstore chain understood that there was a demand for an office assistant’s assistant to help stock up on supplies. The button responds to voice commands. “Right now, you can go into the supply room and say, ‘Hey, Easy, we need more copier paper, and by the way, it looks like we’re short on blue pens,’” Laughlin says.

“A crazy thing happened, and people started asking Easy for things that Staples doesn’t do, and it’s created a whole pipeline of new ideas for them to work with,” Laughlin adds. “So it’s become not only an innovation for them to engage with customers, but it’s become a source of ideas from customers for potentially new innovations.”

While IBM’s Watson is likely the most prominent technology in this space, other solutions, such as CognitiveScale’s Augmented Intelligence, Digital Reasoning’s Synthesis, and Narrative Science’s Quill, are emerging as competitive options.

Schubmehl suggests several other considerations when starting out. The first is getting the right stakeholders in place and then getting them all together to identify the business processes or functions that AI will replace. With that step taken, a company can set up a shortlist of service providers and discuss the potential outcomes for the application.

“One of the things that you’ve got to remember is that if you’re going to do an AI system, it’s going to be based on data, so you need to make sure that you have access to all of the data that’s necessary to drive the system,” Schubmehl adds. “A lot of organizations don’t have any real strategy about how they keep track of their knowledge, so really getting a handle on that first is something they should think about.”

Then companies need to engage subject matter experts in the design and implementation process, Schubmehl says. If you’re designing for the call center, you need to have the call center people involved in the design and the development. “If you don’t, you’re essentially asking for trouble,” because the design might not align with their needs, he cautions.

Goetz agrees that the design element is key, noting that adopting artificial intelligence calls for a desire to provide a human experience. Companies should ask, she says, “how do I reproduce, or almost replicate, what a human experience is going to be?”

“There are plenty of instances where we, as humans, still want human interaction,” Pegasystems’ Jeffs agrees.

In fact, designing an AI system, Goetz says, should mimic the process companies use to design a customer experience program.

“Sometimes they speak, so the tone, the way that they speak—is it very formal, is it conversational, and at what age groups are they speaking—those sorts of things really go into creating the best success in adopting artificial intelligence for customer engagement and customer experience,” Goetz says. “You can’t underestimate the design component that goes into that, and that’s how businesses have to approach this technology. You cannot think this is just another scoring engine or analytic engine that runs under the hood of your CRM or website or advertising platform.”

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Associate Editor Oren Smilansky can be reached at osmilansky@infotoday.com.