While 85 percent of 3,000 surveyed business leaders believe artificial intelligence enables a competitive advantage, only about 1-in-5 have pursued AI solutions, according to an MIT Sloan report. To help organizations evaluate and accelerate their AI journeys, IBM created its Data Science Elite Team earlier this year.

John Thomas (pictured), distinguished engineer and director at IBM Analytics, as well as a member of the Data Science Elite Team, spoke with Dave Vellante (@dvellante), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during IBM Change the Game — Winning With AI event in NYC. They discussed IBM’s Data Science Elite Team and how that team is building AI solutions with clients and teaching them how to maintain work without constant IBM interference. (* Disclosure below.)

[Editor’s note: The following answers have been condensed for clarity.]

Give us the update since we last talked. What have you been up to?

Thomas: We’re living and breathing data science these days. The Data Science Elite Team, we are a team of practitioners. We actually work collaboratively with clients. And I stress the word collaboratively because we’re not there to just go do some work for a client. We actually sit down, expect the client to put their team to work with our team, and we build AI solutions together.

So at the end of the two- or three-month period, when IBM leaves, my team leaves, the client knows what the tools are, what the techniques are, what to watch out for, what [is the] success criteria. They have a good handle on that.

What are some use cases?

Thomas: Trying to improve customer interactions is something that cuts across industries, right? Now that can be across different channels. One of the most prominent channels is a call center.

So we are looking at can we apply machine-learning techniques to understand dominant topics in the conversation. Once we understand dominant topics in the conversation, can we drill into that and understand, “what are the intents, and does the intent change as the conversation progresses? And can you understand that real time?”

Are there humans involved in calibrating the model?

Thomas: There has to be. You need expertise to look at what these models produce. Because if you think about it, machine learning algorithms don’t by themselves have an understanding of the domain. So somebody has to marry the statistical observations with the domain expertise.

And then in terms of operationalizing it, how does that actually get done? What tech is behind that?

Thomas: What you need to do is weave these models into your business processes and your applications. So you need a way to deploy these models. The models should be consumable from your business processes. So the operationalizing is deploying, consuming these models, and then beyond that, how do the models behave over time?

What makes a successful engagement?

Thomas: For our team right now, it is not about revenue; it’s purely about adoption. The way we measure success is have we built out the use case in a way that is useful for the business? 

Two, does a client see value in going further with that. We need involvement from their data science team. We talk about collaborating with them. We need involvement from their line of business.

And, third, people don’t usually think about it:,We need IT to be part of the discussion.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of IBM Change the Game — Winning With AI event. (* Disclosure: TheCUBE is a paid media partner for the IBM Change the Game — Winning With AI event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE