WEBVTT - Smart Talks with IBM and Malcolm Gladwell: Can AI be empathetic? Reinventing client experiences

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<v Speaker 1>Hello, Hello, Hello. This is Smart Talks with IBM, a

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<v Speaker 1>podcast from Pushkin Industries, High Heart Media and IBM about

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<v Speaker 1>what it means to look at today's most challenging problems

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<v Speaker 1>in a new way. I'm Malcolm Gladwell. Today I'm chatting

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<v Speaker 1>with a Neil Bout, the senior vice president and Chief

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<v Speaker 1>Technology Officer of Anthem, one of the most prominent health

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<v Speaker 1>insurance companies in the United States. We have been now

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<v Speaker 1>pivoting the more around. Okay, we are building these capabilities,

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<v Speaker 1>we are building these solutions. How are they fundamentally changing

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<v Speaker 1>and improving the lives of our members, our communities and

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<v Speaker 1>really making a difference to the people we saw? And

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<v Speaker 1>Neil has been with Anthem for over thirteen years and

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<v Speaker 1>his spearheaded efforts to improve customer experience and members needs.

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<v Speaker 1>I'll also be chatting with Glenn Finch, Managing Partner of

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<v Speaker 1>Global Business Services at IBM. How you deal with empathy

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<v Speaker 1>in an AI system. It's all based on the choice

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<v Speaker 1>of words that you use and the verbal inflections that

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<v Speaker 1>are present when you have a voice response. Then is

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<v Speaker 1>a twenty five year IBM veteran. His work focuses on

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<v Speaker 1>the most challenging and transformative engagements at IBM. I'm excited

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<v Speaker 1>to share my conversation with the Neil and Glenn about

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<v Speaker 1>artificial intelligence and how it's influencing customers to interact with

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<v Speaker 1>their healthcare in a new way. Al Right, guys, let's

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<v Speaker 1>get started. Hi everyone, Thanks guys for joining me today.

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<v Speaker 1>Why don't we start with the two of you just

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<v Speaker 1>introducing yourself, tell me, tell me what you do as

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<v Speaker 1>great I'm glad to be here today, Thanks for hosting us.

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<v Speaker 1>I basically lead the technology and practice here at Anthem

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<v Speaker 1>as a CTO, managing all the roadmaps for technology, making

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<v Speaker 1>sure that we're building solutions that are meeting our business

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<v Speaker 1>needs on a day to day basis, making sure that

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<v Speaker 1>we are catering to the needs of our members. So

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<v Speaker 1>overall technology roadmap, making sure that we work with partners

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<v Speaker 1>like IBM to bring new technology to the forefront. And

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<v Speaker 1>how how long have you been with Anthem. I've been

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<v Speaker 1>with Anthem for thirteen years actually, and the company has

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<v Speaker 1>evolved while we are in the healthcare business. Our focus

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<v Speaker 1>has been more members centric now, so really understanding how

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<v Speaker 1>a big organization like Anthem can make sure that we

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<v Speaker 1>pivot from being a normal traditional listed company which definitely

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<v Speaker 1>is meeting the expectations of the stockholders, but also catering

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<v Speaker 1>to the need of our members and the communities that

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<v Speaker 1>we serve in. Yeah, why don't you introduce yourself. I'm

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<v Speaker 1>Glenn Finch. I look after data in AI and the

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<v Speaker 1>services side of the IBM company. We take a lot

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<v Speaker 1>of wicked cool technology and bring it to life of

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<v Speaker 1>clients like Anthem and Uh, you know, I get the

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<v Speaker 1>great pleasure of working with a Neil on a daily

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<v Speaker 1>basis to really fundamentally change the member experience using artificial intelligence,

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<v Speaker 1>so usually on the cutting edge of things, and just

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<v Speaker 1>just love coming to work every day. Yeah, so you

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<v Speaker 1>said something. The two of you have been working together

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<v Speaker 1>for some time. When did you guys me me to

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<v Speaker 1>first know each other. As I said, the industry has

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<v Speaker 1>been evolving a lot, Malcolm, So a couple of years back.

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<v Speaker 1>We basically we're kind of figuring out as the consumer

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<v Speaker 1>experience changes, as people get so much used to Netflix

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<v Speaker 1>and Amazon and the way they do their day to

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<v Speaker 1>day shopping, the way they experienced things. We were looking

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<v Speaker 1>for a partner where we could really explore the power

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<v Speaker 1>of AI, really use our data in a way wherein

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<v Speaker 1>we can create these personalized experiences. So that's where Glenn

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<v Speaker 1>and I actually talked a little bit and we figured

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<v Speaker 1>out that there is a possibility of us partnering ib

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<v Speaker 1>AM bringing its UM technology, and basically that's when we

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<v Speaker 1>kind of figure out there's there's a definite role to

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<v Speaker 1>play and partner on this journey together. And it's been great.

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<v Speaker 1>Over the last two years, we have been able to

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<v Speaker 1>deliver on some great, exceptional experiences for our members and

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<v Speaker 1>and we are now moving beyond to other constituents and

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<v Speaker 1>really making sure that UM we make it awesome for

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<v Speaker 1>for members to connect with us. Yeah, Glenn, had you

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<v Speaker 1>worked with an in an insurance provider before? Yea, So

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<v Speaker 1>we have a variety of clients around the world, so yes,

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<v Speaker 1>but Anthem is special to my heart. We started thinking

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<v Speaker 1>through this because when you work with Anthem, this concept

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<v Speaker 1>of member and member experience, you need to show up

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<v Speaker 1>every day with that front and center in your mind.

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<v Speaker 1>So there are other clients who focus on cost or

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<v Speaker 1>technical debt or something like that, but that's not true

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<v Speaker 1>at Anthem. You need to show up front and center

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<v Speaker 1>every day with how are you going to radically improve

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<v Speaker 1>the member experience first, and fas say, but it's the

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<v Speaker 1>relationship between the two companies and the two of you

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<v Speaker 1>goes back so far that I'm really curious to get

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<v Speaker 1>a sense of how the kinds of questions you've been

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<v Speaker 1>asking and problems you've been trying to solve have evolved

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<v Speaker 1>over that time. Tell me about ten years ago, what

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<v Speaker 1>were you guys talking about. So I think the ten

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<v Speaker 1>years back, the conversation is more and more around Okay,

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<v Speaker 1>how many sellers do we have in our data center,

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<v Speaker 1>how many licensing points that we're going to be spending

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<v Speaker 1>this year? What will be our footprint? What is our

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<v Speaker 1>network speed? Are we able to manage the new capability

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<v Speaker 1>that we're delivering? And it really was very technologically focused

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<v Speaker 1>conversation that we used to have. And what has happened

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<v Speaker 1>over the years, Malcolm, is that you know, we have

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<v Speaker 1>been now pivoting too more around Okay, we are building

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<v Speaker 1>these capabilities, we are building these solutions, how are they

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<v Speaker 1>fundamentally changing and improving the lives of our members, our

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<v Speaker 1>communities and really making a difference to the people we serve.

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<v Speaker 1>So as we looked at technology and engineering, we kind

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<v Speaker 1>of pivoted from that to more platform and a product

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<v Speaker 1>that we are building for our constituents. And as that

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<v Speaker 1>pivot happened, you know, I would say around three or

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<v Speaker 1>four years back, the conversation then evolved to more around Okay,

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<v Speaker 1>how are we improving the experience? How are we making

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<v Speaker 1>sure that we're making it easier for the members? And

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<v Speaker 1>and it pivoted from being reactive and kind of what

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<v Speaker 1>I called sick care management to more wellness oriented conversation

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<v Speaker 1>how do we keep our members healthy? And that's where

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<v Speaker 1>the overall pioneering of personalized experiences, predictive and proactive health

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<v Speaker 1>care management kind of started. And as we had interactive

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<v Speaker 1>that IBM, we knew that they had the technology and

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<v Speaker 1>they had the real backbone, which good. So the needs

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<v Speaker 1>that we wanted to kind of bring forward and talk

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<v Speaker 1>about that pivot. I'm curious what's driving it. Did you

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<v Speaker 1>go to a NIL and say, look, you have an

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<v Speaker 1>opportunity to do so much more here? Does a neil

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<v Speaker 1>come to you and say I don't want to be

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<v Speaker 1>just focused on technology. Are members are telling us X,

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<v Speaker 1>Y and Z or take me back to that transformative

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<v Speaker 1>moment when you start thinking about this project in a

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<v Speaker 1>different way. There's been a a massive shift at the

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<v Speaker 1>IBM company in general to shift away from pure technology

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<v Speaker 1>and move towards technology on behalf of a workflow. When

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<v Speaker 1>you think about artificial intelligence and you are trying to

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<v Speaker 1>have a conversation with someone, right, you don't need just

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<v Speaker 1>deep artificial intelligence programmers. You need to have people attached

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<v Speaker 1>to that that know how to have a conversation with

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<v Speaker 1>people and what sequence some words are gonna elicit a response,

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<v Speaker 1>and how that experience feels. To remember, that's a very

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<v Speaker 1>different type of program then just dropping in a chatbot

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<v Speaker 1>and hoping it works right to answer the twelve questions

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<v Speaker 1>that you get most of the time, right, And you

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<v Speaker 1>mentioned this concept of personalization, right, just making sure we

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<v Speaker 1>put the right people together on the program is half

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<v Speaker 1>the battle, right, And that's a shift that IBM has

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<v Speaker 1>made very consciously, started about five years ago. You know,

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<v Speaker 1>we really in Earnest called out intelligent workflows about two

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<v Speaker 1>or three years ago and that's when we started doing

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<v Speaker 1>this together. M. It was tough, it was ambitious as

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<v Speaker 1>compared to anything else that we had done out here.

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<v Speaker 1>And one thing which Malcolm was very very beautiful and

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<v Speaker 1>and has been very important for us learning on the goal.

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<v Speaker 1>When you have so much data that you're capturing, when

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<v Speaker 1>you have a technology that really can give you in

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<v Speaker 1>a nanosecond the response to what exactly is happening. The

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<v Speaker 1>beauty of it is that you can pivot and kind

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<v Speaker 1>of change on the fly. The agility that you build

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<v Speaker 1>into our system, the agility that you build into our

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<v Speaker 1>operations is a key and that's what we have been

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<v Speaker 1>able to do. And unfortunately at Anthem, we have been

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<v Speaker 1>really at the forefront of that, investing the right dollars

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<v Speaker 1>and bringing the agility, bringing the way we can kind

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<v Speaker 1>of pivot to what is more important to the concerns.

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<v Speaker 1>That has been a great thing that has been happening here.

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<v Speaker 1>Let's go through some some very specific examples. So, I

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<v Speaker 1>am a I'm a member of Anthem, I am on

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<v Speaker 1>your website. I would like to accomplish something. Tell me

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<v Speaker 1>a specific thing that an expectation a member might have,

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<v Speaker 1>and how you have said about trying to satisfy that expectation,

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<v Speaker 1>and let's get let's get super specific. Give me a scenario,

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<v Speaker 1>a tough a tough scenario. Yeah, yeah, well, I think

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<v Speaker 1>I can give you a comparison to the past. Right,

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<v Speaker 1>So when you were enrolled as a member, we probably

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<v Speaker 1>would send you an ID card which was a hard

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<v Speaker 1>piece of paper, a very good piece of paper which

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<v Speaker 1>costs us a lot. Then there was nothing that we

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<v Speaker 1>would let you know other than that, hey, if you

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<v Speaker 1>want a register on our website, please, you're welcome, right,

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<v Speaker 1>And then that's where our first interaction with you as

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<v Speaker 1>a member used to happen. And frankly, there was nothing

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<v Speaker 1>after that. There was a vacuum, and then you would

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<v Speaker 1>probably try to understand your benefits. You will make sure

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<v Speaker 1>that you know what your co pay is, and then

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<v Speaker 1>we will not hear from you for a long time,

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<v Speaker 1>and all of a sudden, someday, unfortunately somebody is tack

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<v Speaker 1>in your family and then you pick up the card,

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<v Speaker 1>go to a provider and basically have a visit um

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<v Speaker 1>there and then you go from there. So that's the

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<v Speaker 1>traditional experience that somebody would have had. Right now we

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<v Speaker 1>have totally revamped that. So as a member, when you

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<v Speaker 1>enrolled with us, we send you a welcome kit which

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<v Speaker 1>sent you a digital well kit. We send you an

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<v Speaker 1>ID card which is available on your phone. We send

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<v Speaker 1>you a link to our Sydney have tapp which basically

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<v Speaker 1>you can download, you can register in a minute, but

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<v Speaker 1>if you've been an existing member, you will get a personalized,

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<v Speaker 1>curated news feed which is specific to you based on

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<v Speaker 1>your prior experience and based on your claims, history and

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<v Speaker 1>other things that we know about you. We work with

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<v Speaker 1>IBM around the AI chat part, which is basically a

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<v Speaker 1>Watson enabled chat board which you can ask the questions

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<v Speaker 1>from what is my copay? You don't have a call us,

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<v Speaker 1>you don't have to send us an email. You can

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<v Speaker 1>really ask a question there itself. You can ask for

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<v Speaker 1>what are the providers near me? And we'll match a

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<v Speaker 1>provider to you based on your past history. And that's

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<v Speaker 1>where AI comes in that what do we think Malcolm's

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<v Speaker 1>age group, Malcolm's prior history tells us who should be

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<v Speaker 1>the right provider for him to take care of things.

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<v Speaker 1>So that interactive, more personalized, more engaging experience is what

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<v Speaker 1>is different. Let me give you an example. I'd love

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<v Speaker 1>for both of you the way on this. So I'm

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<v Speaker 1>fifty seven years old. It is indicated for someone at

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<v Speaker 1>my age that I get a shingles vaccine. I didn't

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<v Speaker 1>notice never occurred to me. A friend of mine got shingles.

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<v Speaker 1>It was like the worst experience of his life. He

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<v Speaker 1>lost three weeks. It was like so painful, and he's like,

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<v Speaker 1>whatever you do, Malcolm, you need to get a shingles

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<v Speaker 1>vaccine right now. So I went out and got my

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<v Speaker 1>shingles vaccine and then I had to get the booster.

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<v Speaker 1>I remember the booster and blah blah blah. Now when

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<v Speaker 1>you're talking about Sydney and about about drawing on past experience,

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<v Speaker 1>if I was a long time Anthem subscriber, would you

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<v Speaker 1>reach out to me and say, Malcolm, you gotta get

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<v Speaker 1>your shingles vaccine? Would you do? Is that what you're

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<v Speaker 1>thinking about? Exactly? Exactly not only we will tell you

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<v Speaker 1>that you need to take shingles vaccine, will tell you

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<v Speaker 1>exactly which provider probably is the right one for you.

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<v Speaker 1>And that is what the beauty is right now, that

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<v Speaker 1>not to care really that the care gap that we have.

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<v Speaker 1>How does the data tell us that these are the

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<v Speaker 1>care gaps in Malcolm's journey? You know, you you pay

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<v Speaker 1>a lot for your insurance company to take care of you,

0:13:09.320 --> 0:13:11.079
<v Speaker 1>and how do we make sure that we take care

0:13:11.120 --> 0:13:14.360
<v Speaker 1>of you? We be your advocate, We be your journey partners.

0:13:14.480 --> 0:13:17.719
<v Speaker 1>Rather than just allowing for you to come to us

0:13:17.800 --> 0:13:21.360
<v Speaker 1>when you feel that you're sick. So this this AI

0:13:21.440 --> 0:13:23.800
<v Speaker 1>system is called Sydney. First of all, who came up

0:13:23.840 --> 0:13:26.680
<v Speaker 1>with Sydney actually loved the name? But is that who?

0:13:26.720 --> 0:13:30.040
<v Speaker 1>Whose decision was it to call this system Sydney? Actually

0:13:30.040 --> 0:13:34.200
<v Speaker 1>you know, uh, it was it was our team. We

0:13:34.240 --> 0:13:36.559
<v Speaker 1>did some research in terms of what could be a

0:13:36.640 --> 0:13:39.319
<v Speaker 1>very neutral name that we can keep out there. And

0:13:39.320 --> 0:13:41.240
<v Speaker 1>and Malcolm, I can tell you that I love the

0:13:41.320 --> 0:13:44.280
<v Speaker 1>name so much that the beginning of when we had

0:13:44.320 --> 0:13:47.440
<v Speaker 1>COVID hit us, my daughter was asking for a dog

0:13:47.480 --> 0:13:49.720
<v Speaker 1>for a long time and we got a dog, and

0:13:49.760 --> 0:13:52.960
<v Speaker 1>actually we named the dog Sydney. So that's how how

0:13:53.040 --> 0:13:55.040
<v Speaker 1>much how much I care about the name and how

0:13:55.120 --> 0:13:57.319
<v Speaker 1>much I love the name. But thank you very much

0:13:57.320 --> 0:13:59.600
<v Speaker 1>for that's just so we true you Sydney. I'm getting

0:13:59.640 --> 0:14:01.439
<v Speaker 1>the AI assistum, but not your dog, That's all I

0:14:01.480 --> 0:14:05.120
<v Speaker 1>want to be clear. That's what you know we make.

0:14:05.280 --> 0:14:07.280
<v Speaker 1>We may supply you with a picture of Sydney on

0:14:07.440 --> 0:14:09.640
<v Speaker 1>when you when you come to the come to the app,

0:14:09.720 --> 0:14:12.320
<v Speaker 1>but yeah, you're getting the A. Yeah. So what we

0:14:12.480 --> 0:14:17.240
<v Speaker 1>find is that to build trust in AI systems and

0:14:17.760 --> 0:14:21.000
<v Speaker 1>to build the willingness for remember to go along a

0:14:21.080 --> 0:14:25.720
<v Speaker 1>journey experience. There's some things we have to do at

0:14:25.760 --> 0:14:28.440
<v Speaker 1>the table stakes level, at the grassroots level, that we

0:14:28.520 --> 0:14:31.040
<v Speaker 1>have to get right inexorably. And I'm going to go

0:14:31.080 --> 0:14:33.640
<v Speaker 1>back to a Neil's comment about the I D card.

0:14:34.320 --> 0:14:37.640
<v Speaker 1>What happens if you've lost your I D card. You

0:14:37.680 --> 0:14:41.360
<v Speaker 1>don't want to wait on the phone for anybody to

0:14:41.400 --> 0:14:43.920
<v Speaker 1>get a replacement a D card. You'd like to be

0:14:43.960 --> 0:14:47.080
<v Speaker 1>able to do that once and done on the web

0:14:47.160 --> 0:14:49.360
<v Speaker 1>or the mobile. Might have to ask a couple of

0:14:49.440 --> 0:14:53.640
<v Speaker 1>questions and have it done lights out right. So there's

0:14:53.720 --> 0:14:59.239
<v Speaker 1>this combination of doing the more routine things with absolute

0:15:00.000 --> 0:15:04.960
<v Speaker 1>decision writes out complete ease of member, and then that

0:15:05.040 --> 0:15:09.800
<v Speaker 1>builds this trust to have this more longitudinal journey to

0:15:09.960 --> 0:15:13.040
<v Speaker 1>answer your questions or to recommend to you about shingles,

0:15:13.120 --> 0:15:17.000
<v Speaker 1>vaccine right, or a variety of other things based on

0:15:17.200 --> 0:15:19.760
<v Speaker 1>you know your your health challenges. So it's a it's

0:15:19.840 --> 0:15:22.240
<v Speaker 1>kind of a double edged sort of taking care of

0:15:22.240 --> 0:15:25.520
<v Speaker 1>the table stakes and taking people along the journey. Your

0:15:25.560 --> 0:15:29.640
<v Speaker 1>point is you start with the very prosaic stuff and

0:15:29.720 --> 0:15:32.400
<v Speaker 1>you build a trust in the system, and then you

0:15:32.440 --> 0:15:36.200
<v Speaker 1>can move to the more high end stuff. Tell me

0:15:36.240 --> 0:15:39.200
<v Speaker 1>about how you build an AI system like this. This

0:15:39.280 --> 0:15:45.000
<v Speaker 1>is not a trivial accomplishment. What went into building Sydney. Yeah,

0:15:45.120 --> 0:15:49.480
<v Speaker 1>so I think you know, Malcolm, Traditionally, we have a

0:15:49.520 --> 0:15:52.160
<v Speaker 1>lot of data over the years that we have accumulated

0:15:52.360 --> 0:15:57.840
<v Speaker 1>for every member, and we have eighty million lives, multiple

0:15:57.920 --> 0:16:01.960
<v Speaker 1>petabytes of data which is sitting on our systems, and

0:16:02.000 --> 0:16:05.200
<v Speaker 1>that data basically allows us to learn. You know, the

0:16:05.280 --> 0:16:07.680
<v Speaker 1>data is data. As long as you don't touch it,

0:16:07.840 --> 0:16:11.080
<v Speaker 1>you don't do anything. But once you start really using

0:16:11.120 --> 0:16:14.360
<v Speaker 1>technologies and and when when we call AI, these are

0:16:15.440 --> 0:16:18.760
<v Speaker 1>mathematical models that you can run on this data to

0:16:18.880 --> 0:16:21.960
<v Speaker 1>give you insights. And those insights are the key at

0:16:22.040 --> 0:16:25.320
<v Speaker 1>the end of the day. And as we get those insights,

0:16:25.400 --> 0:16:26.960
<v Speaker 1>we have to make sure that we have a way

0:16:27.000 --> 0:16:29.400
<v Speaker 1>to use those insights to make a difference in the

0:16:29.800 --> 0:16:31.920
<v Speaker 1>in the life of any member that we have or

0:16:31.960 --> 0:16:35.080
<v Speaker 1>any constant. Actually, you know, our sales experience for our

0:16:35.120 --> 0:16:39.040
<v Speaker 1>brokers are providers. Getting to know exactly what they need

0:16:39.120 --> 0:16:42.080
<v Speaker 1>to know is very very important. So we are making

0:16:42.120 --> 0:16:44.560
<v Speaker 1>sure that this data and the minding of this data

0:16:44.760 --> 0:16:49.080
<v Speaker 1>is constant. So when we talk about the partnership with IBM.

0:16:49.080 --> 0:16:51.800
<v Speaker 1>We're talking about ability for us to mind this data

0:16:52.000 --> 0:16:55.880
<v Speaker 1>on the fly at a very very quick speed, and

0:16:55.960 --> 0:16:58.880
<v Speaker 1>that is what is key. Then we're able to use

0:16:58.920 --> 0:17:00.880
<v Speaker 1>AI in a different text. And I'm going to give

0:17:00.920 --> 0:17:03.400
<v Speaker 1>you example of something that really we're bringing to the

0:17:03.480 --> 0:17:06.399
<v Speaker 1>forefront of of what we call as a nutrition tracker.

0:17:07.320 --> 0:17:10.239
<v Speaker 1>So imagine that you have your phone in front of you,

0:17:11.680 --> 0:17:14.040
<v Speaker 1>You have a plate of food that came in front

0:17:14.040 --> 0:17:17.879
<v Speaker 1>of you, and you can open Sydney and show the

0:17:17.920 --> 0:17:21.200
<v Speaker 1>food of plate to Sydney. Sydney can tell you based

0:17:21.240 --> 0:17:23.520
<v Speaker 1>on what it plate. You take a picture of the

0:17:23.520 --> 0:17:26.320
<v Speaker 1>photo and Sydney looks at the photo and says, why

0:17:26.359 --> 0:17:28.040
<v Speaker 1>are you loading up on carbs? I mean, is that

0:17:28.080 --> 0:17:30.679
<v Speaker 1>what we're talking about exactly? That's what I'm talking about.

0:17:30.720 --> 0:17:33.040
<v Speaker 1>So you know, this is a great partnership we have

0:17:33.119 --> 0:17:35.760
<v Speaker 1>with one of our ecosystem partners. And actually this isn't

0:17:35.760 --> 0:17:38.760
<v Speaker 1>pilot with our house account, which is eighty thousand members

0:17:38.840 --> 0:17:42.080
<v Speaker 1>right now, and it can tell you. It can you

0:17:42.119 --> 0:17:43.960
<v Speaker 1>can show it a cup and it can tell you

0:17:44.000 --> 0:17:47.240
<v Speaker 1>this is a coffee with no milk, and it's going

0:17:47.280 --> 0:17:49.919
<v Speaker 1>to be seventy calories and it keeps track of what

0:17:50.080 --> 0:17:52.800
<v Speaker 1>you're eating and basically that's how we build the healthy

0:17:52.800 --> 0:17:56.160
<v Speaker 1>habits out there. So the advancement in the in the

0:17:56.200 --> 0:17:58.480
<v Speaker 1>field of technology and how do we make sure that

0:17:58.640 --> 0:18:01.720
<v Speaker 1>we move away from that leg thee information technology to

0:18:02.200 --> 0:18:05.159
<v Speaker 1>really the exponential technology that is in front of us

0:18:05.240 --> 0:18:10.439
<v Speaker 1>is the key. We needed to take all of that

0:18:10.600 --> 0:18:15.680
<v Speaker 1>AI and persist a conversation with a member, right And

0:18:15.880 --> 0:18:20.359
<v Speaker 1>and that's where Watson came in to help Sydney persist

0:18:20.520 --> 0:18:25.520
<v Speaker 1>conversations with members, right Because crunching through data and and

0:18:25.560 --> 0:18:28.639
<v Speaker 1>knowing about your claim is one thing, but being able

0:18:28.680 --> 0:18:31.320
<v Speaker 1>to talk to you about that claim and understand your

0:18:31.359 --> 0:18:34.560
<v Speaker 1>responses back, whether you're on a keyboard, whether you're speaking,

0:18:34.560 --> 0:18:37.800
<v Speaker 1>whether you're doing whatever. That's kind of where Watson came

0:18:37.840 --> 0:18:41.880
<v Speaker 1>in to help augment Sydney and again designing those conversations.

0:18:42.320 --> 0:18:43.760
<v Speaker 1>I don't know if you've been in a in a

0:18:43.840 --> 0:18:46.080
<v Speaker 1>situation where you're sitting next to somebody and they're talking

0:18:46.160 --> 0:18:48.720
<v Speaker 1>and you say, oh my god, I can't believe they

0:18:48.720 --> 0:18:54.000
<v Speaker 1>said that. Well, you have to engineer that out of

0:18:55.240 --> 0:18:58.199
<v Speaker 1>the conversations that you have with members so that you know,

0:18:58.680 --> 0:19:01.480
<v Speaker 1>all of the members are delighted and one of the

0:19:01.520 --> 0:19:04.800
<v Speaker 1>things I'm proudest of is when by our work together,

0:19:05.520 --> 0:19:11.120
<v Speaker 1>we have members that are thanking Sydney when we're working

0:19:11.160 --> 0:19:16.119
<v Speaker 1>with them with artificial intelligence, responding to their questions, just

0:19:16.280 --> 0:19:20.280
<v Speaker 1>as if Sydney was a fully human worker, right, And

0:19:20.480 --> 0:19:24.160
<v Speaker 1>that that's what I get delight from is when we've

0:19:24.200 --> 0:19:27.880
<v Speaker 1>been able to change a member experience and work through

0:19:27.880 --> 0:19:35.400
<v Speaker 1>that all of the things members might ask, Now, are

0:19:35.400 --> 0:19:39.200
<v Speaker 1>you taking real life conversations, looking at them and feeding

0:19:39.240 --> 0:19:42.520
<v Speaker 1>them to Sydney and saying okay? In the last two years,

0:19:42.520 --> 0:19:45.040
<v Speaker 1>these are all the These are all the phone conversations

0:19:45.040 --> 0:19:46.520
<v Speaker 1>we've had with our members. These are the kinds of

0:19:46.520 --> 0:19:50.800
<v Speaker 1>things they ask. Is that where it starts? We build

0:19:50.800 --> 0:19:53.800
<v Speaker 1>what we call the anthology of the conversations? You know,

0:19:53.960 --> 0:19:56.960
<v Speaker 1>how are we making sure that as we get the

0:19:57.040 --> 0:20:01.200
<v Speaker 1>interactions noted down for our members or provide us into

0:20:01.240 --> 0:20:04.240
<v Speaker 1>our system, whether it's a phone call, whether it's a chat,

0:20:04.320 --> 0:20:06.960
<v Speaker 1>whether it's basically even they came to the website and

0:20:07.000 --> 0:20:11.000
<v Speaker 1>they clicked through specific things, right, so we are noting

0:20:11.040 --> 0:20:13.879
<v Speaker 1>those down. We are kind of creating a what we

0:20:13.960 --> 0:20:16.960
<v Speaker 1>call a graph model and a flow of When a

0:20:17.040 --> 0:20:19.480
<v Speaker 1>member asked this, the next question possible level, it's going

0:20:19.520 --> 0:20:22.240
<v Speaker 1>to be this. If you give up yes to that

0:20:22.320 --> 0:20:24.480
<v Speaker 1>answer or not to that answer, they're gonna probably ask

0:20:24.520 --> 0:20:27.520
<v Speaker 1>you this. So that kind of slow Sydney can be

0:20:27.600 --> 0:20:31.399
<v Speaker 1>thinking two in three steps ahead exactly. So Sydney is

0:20:31.440 --> 0:20:33.560
<v Speaker 1>thinking two or three steps ahead and making sure that

0:20:33.560 --> 0:20:36.120
<v Speaker 1>the anticipation of what you're going to be doing and

0:20:36.119 --> 0:20:38.800
<v Speaker 1>and beyond. Sydney, our overall system is thinking two or

0:20:38.880 --> 0:20:45.000
<v Speaker 1>three systems steps ahead and predicting proactively those conversations as

0:20:45.040 --> 0:20:48.040
<v Speaker 1>well as those interventions that we need to give to

0:20:48.119 --> 0:20:51.920
<v Speaker 1>the members. So really using ai UM you know Watson

0:20:52.000 --> 0:20:55.680
<v Speaker 1>as a back backbone to this, Sydney is basically what

0:20:55.760 --> 0:21:00.000
<v Speaker 1>we call the human centered, designed, focused Interaction and Engagements

0:21:00.000 --> 0:21:02.720
<v Speaker 1>system that sits on top of the backbone of the

0:21:02.760 --> 0:21:05.760
<v Speaker 1>AI as the data at the bottom, So that basically

0:21:05.840 --> 0:21:08.760
<v Speaker 1>is layered away. How Sydney is able to answer the

0:21:08.840 --> 0:21:11.680
<v Speaker 1>question that we have it aspects you of what type

0:21:11.720 --> 0:21:14.680
<v Speaker 1>of question it is because our intology of the data

0:21:14.920 --> 0:21:16.960
<v Speaker 1>as well as the AIS that we have built is

0:21:17.160 --> 0:21:20.240
<v Speaker 1>very very dock solid. And that is and the good

0:21:20.280 --> 0:21:23.320
<v Speaker 1>thing is that it's it's a gift that keeps giving

0:21:23.359 --> 0:21:27.399
<v Speaker 1>because the more data we collect, the more the system

0:21:27.440 --> 0:21:31.720
<v Speaker 1>it gets Yeah, wait, can you Stumps, Sydney, can you

0:21:31.760 --> 0:21:34.600
<v Speaker 1>ask it a question? You can? I'm sure it's possible too.

0:21:34.880 --> 0:21:37.040
<v Speaker 1>And then you know, when we when we get into

0:21:37.080 --> 0:21:39.800
<v Speaker 1>that situation, what we want to do is we want

0:21:39.800 --> 0:21:44.040
<v Speaker 1>to bring the member to a human agent so that

0:21:44.200 --> 0:21:48.399
<v Speaker 1>the member satisfied seamlessly right, so that there's there's no

0:21:48.640 --> 0:21:52.320
<v Speaker 1>daylight at all regardless of how the member has wants

0:21:52.480 --> 0:21:55.800
<v Speaker 1>to connect with the human agent. A lot of members,

0:21:55.880 --> 0:21:59.359
<v Speaker 1>you know, are are dealing with time challenges and they

0:21:59.359 --> 0:22:01.959
<v Speaker 1>don't want to call up anymore. They just want somebody

0:22:02.000 --> 0:22:04.440
<v Speaker 1>to you know, be able to chat with. We try

0:22:04.480 --> 0:22:06.880
<v Speaker 1>and respond to all that, and then if if somebody

0:22:06.960 --> 0:22:11.160
<v Speaker 1>needs a human agent, then we go there. Yeah. Yeah,

0:22:11.720 --> 0:22:16.280
<v Speaker 1>what's what did your what did your members tell you about,

0:22:16.760 --> 0:22:22.439
<v Speaker 1>either explicitly or implicitly about what they wanted? You know,

0:22:22.480 --> 0:22:24.199
<v Speaker 1>we've been through this. We've just been through a year

0:22:24.200 --> 0:22:28.320
<v Speaker 1>and a half of craziness, you know, where everything is

0:22:28.320 --> 0:22:31.040
<v Speaker 1>being turned upside down. I'm curious, what have you what

0:22:31.080 --> 0:22:34.520
<v Speaker 1>have you learned from them over the last stretch is

0:22:34.560 --> 0:22:37.119
<v Speaker 1>what a member wants today very different than it was

0:22:37.119 --> 0:22:41.160
<v Speaker 1>two years ago. Definitely, Malcolm. If you look at that,

0:22:41.720 --> 0:22:43.920
<v Speaker 1>you know, the terms that you use in healthcare are

0:22:44.040 --> 0:22:46.560
<v Speaker 1>very very complex, and it's very difficult for people to

0:22:46.640 --> 0:22:49.480
<v Speaker 1>understand what my cope is. What is an out of network,

0:22:49.560 --> 0:22:53.520
<v Speaker 1>what is it in network? What does a claim uh

0:22:53.640 --> 0:22:56.760
<v Speaker 1>that that needs a pre authoriation mean to me? So

0:22:56.800 --> 0:23:01.200
<v Speaker 1>if you look at the conversation that we were having before,

0:23:01.400 --> 0:23:06.119
<v Speaker 1>they were really very hardcore health care oriented conversations and

0:23:06.119 --> 0:23:10.000
<v Speaker 1>and the transparency to to what I'm going to pay

0:23:10.240 --> 0:23:13.680
<v Speaker 1>was not there. So this was this was industry where

0:23:13.720 --> 0:23:17.200
<v Speaker 1>in you know, you're going to buy insurance and you're

0:23:17.200 --> 0:23:19.520
<v Speaker 1>going to buy a product without really understanding what I'm

0:23:19.520 --> 0:23:21.480
<v Speaker 1>going to get. At the end of the day, what

0:23:21.560 --> 0:23:24.800
<v Speaker 1>we did and basically what our customers actually demanded from

0:23:24.880 --> 0:23:27.800
<v Speaker 1>us is that irrespect you of the channel that they

0:23:27.800 --> 0:23:30.719
<v Speaker 1>come to us. Um what we call here at Anthem

0:23:30.760 --> 0:23:33.920
<v Speaker 1>connected experiences. We want to build the connected experiences, whether

0:23:33.960 --> 0:23:36.520
<v Speaker 1>they come to us from a phone call, whether they're

0:23:36.600 --> 0:23:39.560
<v Speaker 1>chatting with us, they're having a web in traction, whether

0:23:39.680 --> 0:23:42.400
<v Speaker 1>they're in the provider's office. How do we make sure

0:23:42.400 --> 0:23:45.480
<v Speaker 1>that we connect the experience end to end. Now, once

0:23:45.520 --> 0:23:47.399
<v Speaker 1>we connect the experience, we want to make sure that

0:23:47.440 --> 0:23:51.520
<v Speaker 1>we are building a very human centered design way of

0:23:51.600 --> 0:23:54.520
<v Speaker 1>answering their questions. So it is as simple as making

0:23:54.520 --> 0:23:58.520
<v Speaker 1>sure that we provide them a nudge on probably this

0:23:58.600 --> 0:24:01.720
<v Speaker 1>is what you're looking for, and that clicks with them

0:24:01.720 --> 0:24:03.359
<v Speaker 1>and they say, yeah, that's what I was looking for.

0:24:03.600 --> 0:24:07.000
<v Speaker 1>So that input, simple interaction really helps to make sure

0:24:07.000 --> 0:24:09.800
<v Speaker 1>that you make the member feel good. Having the ability

0:24:09.840 --> 0:24:13.480
<v Speaker 1>to text, having an ability to get dancers while you're

0:24:13.480 --> 0:24:16.040
<v Speaker 1>cooking your dinner and you can text and say that, hey,

0:24:16.080 --> 0:24:18.399
<v Speaker 1>could you please tell me what will cope for the

0:24:18.440 --> 0:24:20.919
<v Speaker 1>next visit? I have a daughter X And you go

0:24:20.960 --> 0:24:23.240
<v Speaker 1>ahead and start cooking your dinner and when you come back,

0:24:23.280 --> 0:24:25.199
<v Speaker 1>you have a text back out there which tells you

0:24:25.240 --> 0:24:27.520
<v Speaker 1>exactly what it is. And the beauty of it is

0:24:27.560 --> 0:24:30.560
<v Speaker 1>that we had a very constant loop out there, you know,

0:24:30.600 --> 0:24:34.399
<v Speaker 1>the technologies that we use that that allowed us to

0:24:34.560 --> 0:24:37.800
<v Speaker 1>have a constant feedback on those complex interactions that we

0:24:37.880 --> 0:24:40.200
<v Speaker 1>were having. And that's where IBM team and we work

0:24:40.280 --> 0:24:42.240
<v Speaker 1>together and kind of figured out, Okay, what will be

0:24:42.240 --> 0:24:45.919
<v Speaker 1>our game plan. What did you learn from working with

0:24:46.040 --> 0:24:50.040
<v Speaker 1>other people on the Watson platform that helped a Neil

0:24:50.080 --> 0:24:53.200
<v Speaker 1>and Anthem? What did you bring to them? So from

0:24:53.240 --> 0:24:55.840
<v Speaker 1>what you've learned from others. So what we what we've

0:24:55.880 --> 0:25:00.240
<v Speaker 1>tried to do with Watson, Well, Watson first started, we

0:25:00.320 --> 0:25:04.760
<v Speaker 1>thought that everybody wanted of a spoke suit, and so

0:25:05.359 --> 0:25:08.320
<v Speaker 1>we kind of go on a journey together to make

0:25:08.359 --> 0:25:11.199
<v Speaker 1>up a spoke suit. And what what we found the

0:25:11.200 --> 0:25:15.240
<v Speaker 1>clients really wanted was well, look, I want you to

0:25:15.320 --> 0:25:20.679
<v Speaker 1>show up with the suit partially done to answer some

0:25:20.760 --> 0:25:24.440
<v Speaker 1>of the basic things, and then I want to make

0:25:24.480 --> 0:25:27.879
<v Speaker 1>it my own, right, So so show up ready to

0:25:27.920 --> 0:25:32.320
<v Speaker 1>go so that we can get into production answering questions

0:25:32.359 --> 0:25:35.400
<v Speaker 1>in a few months, and then we will work together

0:25:35.880 --> 0:25:40.199
<v Speaker 1>to radically customize and taylor that experience. That's been my

0:25:40.200 --> 0:25:46.000
<v Speaker 1>biggest learning, right, So whether it was UM in financial services,

0:25:46.080 --> 0:25:49.600
<v Speaker 1>or healthcare or Telco or you know, there's about seven

0:25:49.680 --> 0:25:53.560
<v Speaker 1>or eight dominant industries. UM. We tried to make a

0:25:53.640 --> 0:25:58.840
<v Speaker 1>series of industry specific cartridges so that Watson came kind

0:25:58.840 --> 0:26:01.720
<v Speaker 1>of pre trained, right, so that we were ready to

0:26:01.720 --> 0:26:06.879
<v Speaker 1>go quickly. And then the second learning was we needed

0:26:06.920 --> 0:26:10.320
<v Speaker 1>to show up with the right people because remember you're

0:26:10.359 --> 0:26:14.760
<v Speaker 1>creating a conversational interaction with someone, right, so you've got

0:26:14.760 --> 0:26:17.800
<v Speaker 1>to make sure that people are designing the words correctly

0:26:17.800 --> 0:26:20.399
<v Speaker 1>and the user experience right. Those are the two things

0:26:20.440 --> 0:26:22.919
<v Speaker 1>I think that you know, we brought that tried to

0:26:22.960 --> 0:26:27.840
<v Speaker 1>help Anthem accelerate. I mean, and Neil said something that

0:26:28.560 --> 0:26:31.560
<v Speaker 1>I thought was fascinating. You're talking about designing a system

0:26:31.600 --> 0:26:35.840
<v Speaker 1>with empathy, and I'm curious what does First of all,

0:26:36.560 --> 0:26:39.440
<v Speaker 1>what does empathy look like in an AI system? And

0:26:40.080 --> 0:26:45.119
<v Speaker 1>b have you has anyone ever, has any non healthcare

0:26:46.840 --> 0:26:51.639
<v Speaker 1>player ever asked you, Glenn to put empathy in the system. UM.

0:26:51.800 --> 0:26:56.160
<v Speaker 1>Clients outside of healthcare are less focused on empathy. They

0:26:56.200 --> 0:27:00.879
<v Speaker 1>are focused more on making sure to get UM, you know,

0:27:01.160 --> 0:27:06.159
<v Speaker 1>the information out there correctly, especially in highly regulated industries. Right.

0:27:07.840 --> 0:27:13.600
<v Speaker 1>How you deal with empathy in an AI system, it's

0:27:13.640 --> 0:27:15.880
<v Speaker 1>all based on the choice of words that you use

0:27:16.200 --> 0:27:20.000
<v Speaker 1>and the verbal inflections that are present when you have

0:27:20.040 --> 0:27:24.520
<v Speaker 1>a voice response, right, and you you and I UM

0:27:24.520 --> 0:27:27.680
<v Speaker 1>when we're talking right now, was Malcolm with Annal with whomever.

0:27:28.520 --> 0:27:31.359
<v Speaker 1>We can just by the words that somebody chooses, we

0:27:31.400 --> 0:27:34.920
<v Speaker 1>can know whether it matters to them about what we're

0:27:34.960 --> 0:27:38.080
<v Speaker 1>talking about, right, And so we try and build a

0:27:38.119 --> 0:27:42.080
<v Speaker 1>lot of those human characteristics into all the responses as

0:27:42.119 --> 0:27:45.520
<v Speaker 1>compared to just getting the information right. Just what's you know?

0:27:46.080 --> 0:27:50.320
<v Speaker 1>Usn't just telling people you're sorry, right, Those are the

0:27:50.359 --> 0:27:52.440
<v Speaker 1>types of things that you have to engineer in as

0:27:52.480 --> 0:27:57.760
<v Speaker 1>compared to just being flawlessly precise about the answer. Yeah.

0:27:58.119 --> 0:28:00.119
<v Speaker 1>Um wait, one last question for for both of has

0:28:00.160 --> 0:28:04.160
<v Speaker 1>been such a fun conversation. We talked about ten years

0:28:04.160 --> 0:28:06.760
<v Speaker 1>ago when you guys started talking and then this sort

0:28:06.760 --> 0:28:10.399
<v Speaker 1>of this transition moment five years ago. Now let's go

0:28:10.480 --> 0:28:14.479
<v Speaker 1>five years in the future. So let's imagine it's twenty

0:28:16.400 --> 0:28:18.760
<v Speaker 1>and we're three of us are talking again. I want

0:28:18.760 --> 0:28:23.040
<v Speaker 1>to know what problems you're trying to solve. Then the

0:28:23.119 --> 0:28:26.800
<v Speaker 1>problems we're trying to solve at that time would would

0:28:27.080 --> 0:28:29.359
<v Speaker 1>would definitely be much different on where we are. But

0:28:29.800 --> 0:28:31.800
<v Speaker 1>what I can tell you before I get there is

0:28:31.840 --> 0:28:34.119
<v Speaker 1>that you know, we want to make sure that in

0:28:34.160 --> 0:28:39.000
<v Speaker 1>the next five years, anthem Is is treated like a

0:28:39.040 --> 0:28:44.520
<v Speaker 1>platform company which is focused on creating these solutions with

0:28:44.600 --> 0:28:47.480
<v Speaker 1>the help of our partners that really meet the need

0:28:47.560 --> 0:28:50.520
<v Speaker 1>of the members in the journey that they have from

0:28:50.520 --> 0:28:52.920
<v Speaker 1>a healthcare perspective, and we do want to pivot from

0:28:52.920 --> 0:28:57.280
<v Speaker 1>a from a sick care to more proactive and predictive

0:28:57.320 --> 0:29:00.440
<v Speaker 1>care and wellness for members. So we're gonna will down

0:29:00.520 --> 0:29:03.720
<v Speaker 1>and keep working on that because it's a it's something

0:29:03.720 --> 0:29:07.000
<v Speaker 1>that never ends and it's going to keep keep going

0:29:07.360 --> 0:29:11.960
<v Speaker 1>in the years to come. I'm still processing this fantastic

0:29:12.000 --> 0:29:16.280
<v Speaker 1>idea about taking a photo of your meal, if your

0:29:16.360 --> 0:29:20.160
<v Speaker 1>plate of food, and getting instantent feedback and analysis on that.

0:29:20.600 --> 0:29:23.880
<v Speaker 1>So Sydney starts gets all these pictures of my food,

0:29:23.920 --> 0:29:26.880
<v Speaker 1>gets it gets a sense of what I'm eating over

0:29:26.920 --> 0:29:28.880
<v Speaker 1>the course of the given day. Is the idea that

0:29:29.280 --> 0:29:31.840
<v Speaker 1>so I'm thinking about this five year from now conversation.

0:29:32.080 --> 0:29:34.680
<v Speaker 1>So five years from now I might be taking a

0:29:34.720 --> 0:29:37.640
<v Speaker 1>photo of everything, and then at the end of every day,

0:29:37.680 --> 0:29:43.040
<v Speaker 1>Sydney text me and says, Malcolm, you should be aware

0:29:43.080 --> 0:29:47.120
<v Speaker 1>of the fact that you're nutritional patterns of the last

0:29:47.120 --> 0:29:49.520
<v Speaker 1>few days. You need to eat a few more vegetables

0:29:49.600 --> 0:29:51.560
<v Speaker 1>or you'll be useful to have some. Is that what

0:29:51.600 --> 0:29:54.040
<v Speaker 1>we're talking about here? But I think this idea is

0:29:54.080 --> 0:29:57.400
<v Speaker 1>fantastic because there is no we have no way of

0:29:57.480 --> 0:30:00.640
<v Speaker 1>making any nutritional sense of the stuff we unless you

0:30:00.720 --> 0:30:04.320
<v Speaker 1>spend two hours on a on Google before you make

0:30:04.360 --> 0:30:07.400
<v Speaker 1>your dinner. How do you know whether the sum total

0:30:07.440 --> 0:30:09.080
<v Speaker 1>of the things you eat in a given day is

0:30:09.120 --> 0:30:12.520
<v Speaker 1>going to be um is optimum? I love this. I

0:30:12.560 --> 0:30:15.280
<v Speaker 1>want this with this now? Can I do I have

0:30:15.320 --> 0:30:19.720
<v Speaker 1>to wait five years? Can I have? Guys, It's been

0:30:19.720 --> 0:30:22.920
<v Speaker 1>a really, really fun conversation. I really appreciate you taking

0:30:22.920 --> 0:30:26.320
<v Speaker 1>the time, Neil Glenn have a wonderful day, and that

0:30:26.600 --> 0:30:30.040
<v Speaker 1>the future cannot come fast enough, at least for me,

0:30:30.240 --> 0:30:33.680
<v Speaker 1>So bring it on. I'm waiting for it. Thank you

0:30:33.760 --> 0:30:37.880
<v Speaker 1>very much for Malcolm. Oh yeah, awesome, Thanks Malcolm. Bye, guys.

0:30:41.080 --> 0:30:44.400
<v Speaker 1>Understanding customer needs has become even more important in the

0:30:44.400 --> 0:30:48.640
<v Speaker 1>wake of COVID nineteen. Companies like IBM and Anthem are

0:30:48.720 --> 0:30:52.520
<v Speaker 1>learning to leverage technology to deliver a more personal experience,

0:30:52.920 --> 0:30:57.280
<v Speaker 1>a crucial part of our evolving healthcare system. Thanks again

0:30:57.480 --> 0:31:00.160
<v Speaker 1>to a Neil Bot and Glenn Finch for talking king

0:31:00.200 --> 0:31:04.560
<v Speaker 1>with me, I learned a lot Smart Talks with IBM

0:31:04.680 --> 0:31:08.680
<v Speaker 1>is produced by Emily Rosteck with Carlie Migliori, edited by

0:31:08.760 --> 0:31:13.080
<v Speaker 1>Karen Shakergee engineering by Martin Gonzalez, mixed and mastered by

0:31:13.160 --> 0:31:19.000
<v Speaker 1>Jason Gambrell and Ben Tolliday. Music by Granmoscope. Special thanks

0:31:19.000 --> 0:31:23.479
<v Speaker 1>to Molly Sosha, Andy Kelly Mia, Label, Jacob Weisberg, Heather Faine,

0:31:23.880 --> 0:31:27.400
<v Speaker 1>Eric Sandler, and Maggie Taylor and the teams at eight

0:31:27.440 --> 0:31:31.600
<v Speaker 1>Bar and IBM. Smart Talks with IBM is a production

0:31:31.600 --> 0:31:34.320
<v Speaker 1>of Pushkin Industries and I Heart Media. You can find

0:31:34.400 --> 0:31:38.520
<v Speaker 1>more Pushkin podcasts on the i Heart Radio app, Apple Podcasts,

0:31:38.760 --> 0:31:42.520
<v Speaker 1>or wherever you like to listen. I'm Malcolm Gladwell, See

0:31:42.520 --> 0:31:43.080
<v Speaker 1>you next time.