How natural language processing will change the smart office forever

FPX chief experience officer Mark Bartlett explained how artificial intelligence and predictive analytics will shape the future of business.

How natural language processing will change the smart office forever

TechRepublic's Dan Patterson spoke with Mark Bartlett, the chief experience officer of FPX, about how AI and predictive analytics will shape the future of business.

Watch the video, or read the transcript of their conversation below:

Patterson: Hey, Alexa. Can you repeat the last thing you just heard? Mark, I wonder if we could start with natural language processing. Everyone in business is looking at the smart office and the smart home office. In what ways is Alexa helping to transform the productivity experience?

Bartlett: Well, Alexa and other voice recognition or assistive bot technologies are really starting to add that level of conversation to what has typically been a manual or onerous task. The thing about B2B specifically is the buying-and-selling relationship in B2B has always really been conversational. It's been a direct sales person or a distributor having a conversation face-to-face, a phone call with a customer so that conversational workflow has really always been part of the B2B buying and selling experience. Here, digitizing that experience has always been a challenge. B2B has been a bit of a slow follower to these consumer innovations in digital. Ecommerce, for instance, right? The wave of ecommerce that were 20 years into here, that's so much part of our lives in B2C, well, in B2B, they're only just stepping into this because of the value of those personal one-to-one relationships in buying and selling. This is a really exciting moment for B2B to adopt these conversational technologies and bring the conversational interfaces into the digital realm for B2B buying and selling. This is one of these great transformative moments here.

Patterson: When we talk about natural language processing, what we're really talking about are machine-learning tactics that need to process complex transactions. What does Alexa and other smart assistants need to do to get from where they are now which is fairly simple interactions to more complex conversations in interactions?

SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research)

Bartlett: Well, if we talk about machine learning, the machines have to learn a lot more. The simplicity of the interaction today and the frustration of so many of our interactions today of "No, Alexa. No." all of those fits and starts that we're having with NLP and that we're having with the conversational interfaces, these fits and starts are going to have to get better a lot faster in order to encompass all of the complexity, especially in B2B because if we take an Amazon paradigm of choosing a widget from my assortments, asking Alexa to add that to my cart and then having an easy checkout process through my Prime membership or whatever, that's a very straightforward and simple transaction on the scale of simple versus complex, and Alexa today is struggling with some of those standard B2B use cases.

Once we start to layer in the complex products, services, solutions that are really part and parcel of the B2B experience, the machine learning aspect, understanding all of the nuance of compatibility, of pricing, of business rules, the Alexas of tomorrow are going to have to get a lot smarter a lot faster to ingest all of this information from the enterprise to make a conversational transaction biome.

Patterson: How do we get from here to there and what does the steepness of the curve look like?

Bartlett: The curve is pretty steep and some might say it feels at this moment, these early days, the curve feels so steep that it looks like a cliff in front of you. It looks almost insurmountable, right? But we jokingly say if you can get your access out and get your gear on and ascend that cliff, there's glory at the top. If you can climb that peak, there's a lot of opportunity up there. Being able to take all of the technologies inside B2B buying and selling today such as CRM, ERP, pricing engines, engineering files, project information management, the commerce systems, if we can unify all of those diverse systems and sources and harmonize all of that data and then expose that to the machine learning and cognitive services that are out there, we could start to climb and ascend that peak that's up in front of us.

Software such as CPQ or configure price quote software in B2B, it helps to bridge that and reduce some of that complexity because those systems are designed to harmonize all of that data and sources in the IT ecosystem. Bringing CPQ technologies into these conversational interfaces and exposing those data points to the machine learning engines and cognitive computing engines is going to give us the right material to climb that peak and get up that learning curve faster.

Also see:

  • How we learned to talk to computers, and how they learned to answer back (cover story PDF) (TechRepublic)
  • Alexa for Business likely to win in smart office, leverage AWS, Echo, developers and consumers (ZDNet)
  • How to become an Alexa developer: A cheat sheet (TechRepublic)
  • After a month of Alexa in every room: The good, the bad, and the creepy (ZDNet)
  • How to send an SMS text message with Amazon Alexa (TechRepublic)
  • Alexa Skills: Cheat sheet (TechRepublic)
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