MIT Analyst Webinar

AI in the contact center: The promise, reality and future

Mit tr insights updated

Companies across all industries are using artificial intelligence (AI) in the contact center to improve customer care, increase operational efficiency and enhance security. But how they’re going about it is changing rapidly.

Join Claire Beatty, Editorial Director for International Markets at MIT Technology Review Insights; Janelle Dieken, Senior Vice President of Product Marketing at Genesys; and Aarde Cosseboom, Sr. Director of GMS Technology, Product and Analytics at fashion retailer TechStyle, to learn about the driving forces behind one brand’s vision and success. Get details on current AI use cases, challenges and trends — and see how the COVID-19 pandemic has changed the trajectory of AI in the contact center.

Get an in-depth look into:

  • Market AI adoption
  • Reality, practicality and best practices for implementing and scaling AI
  • MIT predictions about the role of AI

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Good morning, evening and afternoon everyone. This is Josh Reed from

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the digital events team here at Genesys and I’ll be

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moderating today’s webcast. Let me be the first to welcome

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you all to today’s webcast titled AI in the Contact Center:

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The Promise, Reality and Future. So to make sure that you

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have the best experience viewing today’s webcast, let me highlight

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a few things first. First off, if you experience any

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problems viewing or listening to today’s webcast, refresh your browser

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usually fixes any console issues that you might experience. Also it

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might help to switch over to something like Mozilla Firefox

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or Chrome as well as these are the best browsers

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fine. You do have the ability to enlarge that by

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dragging one of the corners throughout the presentation. Also note

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this webcast is designed to be an interactive experience between

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you and our presenters today. So at any time during

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the webcast, feel free to throw questions into the Q&

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A window below this slide window and we’ll answer as

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many as we can at the end of the presentation. However,

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as sometimes it does happen if time gets away from

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us and we aren’t able to answer your question aloud,

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don’t frank we will follow up with you via email

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within the next few business days. And also know if

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receive an on demand recording from ON24 within the next

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that. Also at any time during today’s webcast, feel free

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so don’t worry about that. It’ll open up in a

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new tab in your browser, but these resources expand on

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today’s topic. And lastly, we welcome and appreciate your feedback.

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So you’ll have the opportunity to fill out a short

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survey today. It can be found in the last icon

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to the left or it’ll show up automatically at the

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end of the presentation, but we definitely want to collect

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your feedback about what you would like to hear in

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the future for our webcasts. And like I said, short

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and sweet. So today we have three excellent presenters excited

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to discuss how to get details on current AI use

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cases, challenges, and trends, and see how the COVID-19 pandemic

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has changed the trajectory of AI in the contact center. First

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we have Janelle Dieken. She’s the Senior Vice President of

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Product Marketing here at Genesys and joining Janelle today, we

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have two special guests from MIT technology review insights. Let

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me welcome Claire Beatty, the Editorial Director for International Markets

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and we also have Aarde Cosseboom, the Senior Director of GMS

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Technology, Product and Analytics from Textile. So with all that

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being said, I’m actually going to hand things off Claire

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to kick us off today. Claire, the floor is yours.

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Thank you. Thank you for the introduction, Josh. And I’m delighted to

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welcome you to this webinar today. We will be presenting

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the findings of a piece of research conducted with Genesys.

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It’s called the Global AI Agenda, and we will be

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facilitating a discussion around that. So first of all, let

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me introduce you to the research that we will be

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presenting today. In December, 2019 and January, 2020, we conducted

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a global survey of about 1000 AI leaders, C level

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executives, heads of sales and marketing all across the world,

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so in Latin America, North America, Europe, the middle East and

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Africa and Asia Pacific. And the questions that we asked

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them are here on your screen. So what is the status of AI

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adoption globally? And what are the leading use cases? What

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are the benefits that companies are seeing through AI adoption

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so far and what are some of the obstacles? And then lastly, the

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idea of data sharing. So how are companies looking at

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the data sets that they have thinking about the potential benefits that could

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be gained if they share that data with third parties

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and what would it mean? What would they need to

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see in order to jump into this new world of

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data sharing? So with that, let me dive straight into

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some of the results of the survey. So the headline

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finding is that of the large companies that we surveyed, by

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the end of 2019 about 87% said that they were

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using AI somewhere in their business operations and the chart

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that’s on your slide here shows the industries that are

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perhaps furthest ahead in this AI journey. And we see

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that financial services has really high expectations of AI. The

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question is within three years, approximately what percentage of your

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business processes will use AI? So you can see financial

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services is the furthest ahead. And they just have so

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many use cases from customer processes to back office operations,

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to risk management, to portfolio management, to cybersecurity. So they’re actually

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very mature in using AI in the business. So next

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we looked at what are the largest AI use cases

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globally. And we saw the lead finding is that quality

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control followed by customer care and support and cybersecurity are

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the leading use cases globally. And if we think about

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quality control, there are just so many different ways that

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might look like depending on what industry you’re in, whether you’re in pharma

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or manufacturing, you might be using a different array of

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AI technologies for that use case, but that is the

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leading use case globally. So I’d like to bring in

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Aarde at this point to tell us a little bit

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about AI at Textile. What are some of the leading

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use cases you’re seeing in your business Aarde? Aarde I think

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you might be on mute. Josh, perhaps you can help get Aarde

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off mute. Janelle I want to it over to you at this point?

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Hello. No, go ahead Janelle if you’d like to step

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in real fast and let me see if I can get Aarde

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unmuted here. Okay. No problem. Janelle tell us about your customers.

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Sure. So I think when we think of use of

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AI, especially as it relates to a couple of the

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columns in your chart here around customer service and support

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as well as even personalization, bots are often the first

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type of use case that comes to mind especially for

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customer support. But we also see it as more than

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just a Voicebot or a Chatbot. When I look at

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both personalizing products and services, as well as customer care

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and support, here at Genesys we believe that the future

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of customer experience is rooted in those highly personalized experiences

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powered by AI. That’s a part of what we call

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experience as a service. And so that phrase experience as

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a service might be new to some, if not all

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of you. So let me unpack that for a moment.

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Experience as a service, it really just looks to help

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companies to provide personalization at scale where employees of companies

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can interact with their customers not only with efficiency and

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effectiveness, which are still important, but also with that added

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element of empathy, to help foster customer loyalty and trust

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along the way. And so if I go to the

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next slide and go to a little bit more detail

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on that, how we think of it here at Genesys

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with experience as a service is that each interaction between

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someone in your business and your end customers should start

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with your customer right at the center. And if you

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gain an understanding of that customer’s needs, their intent and

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their preferences, then you can make customers feel remembered, heard

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and understood just as if you would in your other

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relationships that you build in life. And so in other

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words, in your experiences, you can go beyond the elements

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of efficiency and effectiveness in your contact center environment, which

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are still important and be more empathetic to really personalize

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that experience further. On interactions rooted in empathy, where you

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make the customer feel remembered, heard and understood, make it

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easier to build trust and earn loyalty for stronger connections

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and better results. And to achieve that at scale, which

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can be really where the challenge and complexity comes into the picture,

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you need to just have the right tools and technology

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in place. And we believe with experience as a service,

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that’s a combination of three core components, data, artificial intelligence

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and engagement tools. And if I unpack that a little

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bit more as it relates to your question, Claire, around

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various use cases, we need to start with data because

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if I’m going to provide a personalized experience where I

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inject empathy and I want the customer to feel known

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and heard and understood, I have to know something about

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them. And so empathy requires information, not only about your

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customer, but also the employees that are serving them. And

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we see your contact center as really a gold mine

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for that data. By unifying historic third party and behavioral

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data, you can put that good data use. And that’s

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where artificial intelligence comes into play, where AI can turn

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the mounds and mounds and mounds of data that you

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have into those real time insights and actions and make

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sense of it all with all your conversational data to

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predict, maybe engaging in the right moment, if they’re surfing

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on your website or to predict who is the best

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person to answer that phone call or that email, or

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even predict who is the right resources to staff to

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handle that volumes to begin with. So it can automate

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decisions about who, when and how to engage extending beyond

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just the example of bots. And then thirdly, with the

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right engagement tools, it allows you to personalize those experiences,

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not just for customer service, but really across the lifecycle

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of your engagement in person across marketing sales and service,

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and the best engagement tools can help your employees know

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the road that your customer has traveled on and anticipate

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what they need most. So all together with data, with

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artificial intelligence making sense of that data and really using

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AI and those applications, spanning marketing, sales, and service, we

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feel you can create personalized experiences at scale that can

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help you provide true empathy to build trust and drive

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loyalty and that’s experience of the service. And Claire if

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I can take a few more minutes maybe to give

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an example that the audience could relate to potentially. I’ll

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go to this slide real quick. And so let’s say

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Claire, you are in the midst of moving and you’re

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working from home as we all are, and you have

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to time everything just right. So when you get into

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your new place, you have to have high speed internet

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connected and ready to go because you have this big

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webinar coming up, you have important client meetings that are

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already scheduled and you’re kind of stressed about it. Actually

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you’re really stressed about it, right? So you go online

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and you log in to your existing cable provider’s website,

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you’re finding it’s kind of confusing, there’s too many offers,

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it’s not really clear on which one’s best. You don’t

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want to cancel your account and then go through the

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whole process of opening a new one. Maybe you just

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want to transfer to a new address. You want to

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understand what’s the situation with the global pandemic going on and

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somebody coming to your home, all of that stuff, right?

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So as you’re searching online and researching on your cable

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provider’s website, you get a popup and that brings us to

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this top bubble across the customer journey and employee journey,

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and the popup greets you personally and offers the opportunity for

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you to chat with someone. And so you accept that

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invitation and what you realize maybe, or don’t even realize

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is that there’s some automation upfront, and it’s asking you

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some questions. It’s actually already identified based on your behavior

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that maybe you’re looking to do a move. And so

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it asks you that, and you start to dialogue with

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this bot, which brings us to the second bullet. And

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as you’re talking through that, it asks for your new

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address, it’s going pretty well, you indicate your new address

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and that you want to just transfer your service to

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that. And as you were talking you realize, well, I’d really just

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like to talk to someone on the other line. So

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you ask it to speak to a representative, right? So

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behind the scenes, what’s happening at this cable provider, if

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we go down to the employee journey, is that Aarde

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is one of many contact center resources that are now

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working from home in this global pandemic. And so the

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cable provider has already captured a ton of information about

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already his skillsets, his profile information, a lot of his

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performance and talking and handling key issues. And we’re using

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AI powered forecasting scheduling to make sure Aarde’s staffed at

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the right moment to handle these interactions that we’re expecting

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to begin with. So all of that information feeds into

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this middle journey of, to do predictive routing because what

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the performance DNA also told us it’s not only is

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Aarde available to take this call or this chat interaction

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that we have going on here, but he also actually

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lives in the area where you’re moving to and might

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have some insights that would be helpful as you make the

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transition. And so he gets the chat interaction. And if

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we go to the second bullet, he also then gets

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all of the context about you in using agent assistance.

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So he can see everything you were doing on the

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website. He can see your past loyalty to the cable

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company and really interact with you and make you feel

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known, heard, and understood during this stressful time. You get

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all your answers, questions, you get the service scheduled to

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be set up in the moment that you need it, you

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end the interaction, and then behind the scenes we can

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take all that great performance info and your customer feedback

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to feed that into ongoing process improvement. So you can

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see here in this example, it brings together the power

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of data about you, Claire, as the customer, Aarde as

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the employee, connects them together in the customer and employee

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journey beyond just what you might think of with a

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Chatbot or a Voicebot itself to really foster engagement, personalize

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that experience and build trust and loyalty. I’d like to just

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pick up on the point about empathy and in the

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survey we asked respondents to what extent do they agree

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with the following statements. Is AI driven by a need

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to improve customer experience efficiency? Is AI driven by a

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need to improve customer intimacy? Intimacy being defined as personalization,

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customization, a personalized journey. And then we pass the results into all

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companies, large companies and the companies that also have the

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highest level of customer satisfaction. And what we found is

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that across the board, companies are focusing on efficiency, but

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really the customer experience leaders, the one with the best

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customer satisfaction scores are focusing the most strongly on empathy,

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on intimacy and building that personalized journey. So at this point

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I will just throw out a question to the audience,

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a poll question. And that is, how would you categorize

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your company’s use of AI to support customer experience personalization

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today? And then you can choose from the following options

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using now it’s core to our strategy, seeing successes from

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early use cases, still seeking the benefits from early use

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cases, in the planning and discovery phases, or considering for

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the future. And I’ll just give you a couple of

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seconds to answer that question. So Aarde, I think you’ve

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got your audio back. It’d be great for you tell

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us how would you answer that question? Okay. You’re on

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mute again. Oh, no. Claire or Janelle can you just hear now. Now, you’re

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here. Yeah. Yes. You’re back. Right. Perfect. Sorry about that. Yeah, you

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guys will hear a little bit more about this in

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the future slides, but we’re using it now and it’s

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a part of our core strategy. I would say there’s

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definitely some room for improvement. It’s an evolving product. It’s never

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really complete. It’s always something that we could always strive to do better with. Okay.

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I noticed the way that you didn’t reveal the exact answer,

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not to influence results. So we will go now and

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take a look at what the results are. Okay. All

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right. So how would you categorize your company’s use of

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AI to support customer experience? Only less than 4% say

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using now as core to our strategy, just over 25%

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are saying, seeing successes from early use cases and the largest

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majority in the planning and discovery phases. Okay. That’s very interesting.

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So hopefully this webinar will be just at the right time, as

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you’re looking at some of the technologies that you can

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roll out in your customer experience. So very interesting to see

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that. Okay, so now I’ll share a few more findings

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from the global survey. Across industries, we looked at specifically what

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the leading use cases are and broke it down in

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IT and telecoms, consumer goods and retail. So looking at

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the IT and telecommunications industry, the number one use case

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is cyber security. And I think if you think about

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the amount of customer data that they have, that really

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makes sense that they prioritize cyber security in that way,

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followed by customer care. Everyone has got a mobile phone

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account and they have a very high transaction volume in

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the telecommunications industry. So that would make sense. The next industry

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is consumer goods and retail. And the leading use case

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in that industry is customer care and support followed by

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quality control and inventory management. So I guess, Aarde this

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is a good place for me to throw it back

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to you. You’re in the retail space. We’d love to

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hear about some of the use cases that you are using with

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AI. Yeah. This is a great slide. Specifically on the right hand

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side where we’re talking of consumer goods and E- commerce

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retail, I would say we lean a little bit more

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heavily on the customer care. So if I were to

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break it down from 100%, we probably are investing a

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lot of time and energy in AI around customer care,

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probably in the like 60 or 70% range, but we do

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use it for inventory management, quality control with regards to

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our products and also some personalization. Going back Janelle said

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earlier about creating that customer journey and empathy. We do use

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some AI to help with that, but it’s not a

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core piece of our technology infrastructure. I would say we

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lean very heavily on conversational customer care. Fantastic. Okay. So

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if we look at a couple of other industries now,

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here we have financial services focusing very heavily on fraud

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detection, and we’ve seen some surveys in insurance, which shows

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that about three to 4% of all claims are fraudulent.

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So fraud detection is a very important use case for AI

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followed by financial processes and analysis and cyber security. If

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we look at pharma and healthcare, the leading use case

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is quality control, very high at 60% followed by customer

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care and then monitoring and diagnostics. Janelle, you were saying

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that you might have an example from the pharma industry and

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healthcare industry that brings a couple of those use cases

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together. Yeah. And I would say it’s definitely been prominent, not

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only in pharma and healthcare, but also in government. Recent

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example with the pandemic with COVID-19 we saw the use

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of bots or virtual assistants become very important and urgently

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needed especially in being able to handle influxes of inbound

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phone calls to hospitals as you can imagine, as well

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as even to key government agencies during this time, like

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unfortunately, unemployment offices, right? So what we did to help

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companies handle that load and that volume is we partnered

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with Google to provide a joint solution that quite a

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few healthcare and government agencies across the globe took advantage

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of to easily address that onslaught of calls and chat

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interactions that were asking maybe some just basic questions on

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status related to their unemployment check or questions related to the

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COVID- 19. And so one specific example, we had a

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government agency that was supporting unemployment and they were up

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and running with just in two weeks or less with

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this. We had a healthcare example the same way where they

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were supporting millions of calls per day, that they weren’t

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expecting. And with a bot that would have otherwise resulted

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in hours upon hours of QA time and abandoned calls and

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very stressed and frustrated people on the other end. So

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that’s just one example that’s become very prominent, especially during

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this time to leverage automation, because sometimes going back to

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empathy, the most empathetic thing you can do is provide

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a very efficient experience. And so bot certainly met the

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need there. Yeah. And let’s take a look now at the

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benefits that companies are achieving through their AI investments. The leading

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benefits that we saw across the board are around operational

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efficiency, cost savings, and improved management decision making. So having

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a lot of data, being able to understand and make better

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decisions across the business. What we found quite interesting on

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this chart is that just about a quarter of businesses

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are using AI or seeing a benefit from AI that

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is improved revenue. And I can think of a couple

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of reasons for that. So firstly that many of the

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technologies around improving operational efficiency might be more mature. And

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that potentially, there’s also a lot of low hanging fruit in

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that area. I’d like to get your perspective Janelle. Do

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you think it’s more challenging to use AI to increase

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revenue or are there just more use cases around improving

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operational efficiency? I don’t think it’s necessarily more challenging, for

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example, in the example I shared earlier where you were

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looking to transfer your service with the cable provider. Using

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AI to drive revenue and I’ll give some more examples

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of this later to do conversions as people are shopping

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online. We’ve seen companies get that up and running and

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implemented and achieving benefits in less than a month, sometimes

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even within a week. So I think that oftentimes there

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can be a perception that incorporating AI into your environment

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is super difficult, which it can be. I won’t say

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that it can never be, but if you target specific

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use cases that are tied to a very specific business

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outcome around improving web conversions as one example, it doesn’t

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have to be hard and it doesn’t have to be

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challenging. And I think that over time we will see

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more and more use cases in this area. I know

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just speaking for Genesys in our MarTech stack, it’s full

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of AI applications. So I think that there’s a lot

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more to come and I would predict that 26% is

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going to grow. Great. Okay. So at this point, let me

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hand over to you Aarde. We know that Textile is an

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online retailer, but we’d love to hear more about your

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business and about your AI journey. Yeah, absolutely. And before

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we go onto the next slide, what I want to

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talk a little bit about is some content on the

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slide because it is relevant to our customer journey and

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how we developed AI to help support that customer journey.

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The low hanging fruit that we tackled for us was

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of course the operational efficiency and cost savings, reducing of

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handle time for our agents, reducing of transactional type conversations

402
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via chat or email or phone or social. So we

403
00:26:54,030 –> 00:26:57,580
did see some initial, low hanging fruit routines around operate

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00:26:57,580 –> 00:27:02,220
efficiently. And of course some gains in the customer experience

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00:27:02,220 –> 00:27:04,850
based on and I’m going to give you an example of that in the next

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00:27:04,850 –> 00:27:08,120
couple of slides. But one of the things that is

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00:27:08,900 –> 00:27:13,170
rarely looked at or often overlooked is since we are

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a membership model and E- commerce retailer membership model, we

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did increase our revenue. And the way that we did

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00:27:21,940 –> 00:27:26,910
this using AI with using machine learning tool to give

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00:27:26,910 –> 00:27:30,220
us a lifetime value of the customer as they’re going

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00:27:30,220 –> 00:27:34,730
through certain processes, of course the obvious processes are contacting

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00:27:34,730 –> 00:27:39,610
our customer care team. But also as they’re navigating through

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00:27:39,610 –> 00:27:42,710
the E- commerce website, we know exactly what their lifetime

415
00:27:42,710 –> 00:27:46,420
value is, and that changes over time using AI and

416
00:27:46,420 –> 00:27:52,840
machine learning. So back to your guys’ points, increasing revenue

417
00:27:52,840 –> 00:27:56,580
is an opportunity, it’s just a little bit harder to

418
00:27:56,580 –> 00:27:58,900
really get to that. It’s a lot easier to do

419
00:27:58,900 –> 00:28:03,040
the low hanging fruit, which is cost efficiencies. So talking

420
00:28:03,040 –> 00:28:06,110
a little bit more about what we did at Textile

421
00:28:06,110 –> 00:28:09,700
Fashion Group this is just a zoom and so we’re

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going to talk specifically around our conversational AI and how we

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use Genesys to deliver this AI to not only our

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agents but to our customers. Zooming into our specific project,

425
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the things we’re looking for we’re a cloud- based product.

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So making sure that we had an ecosystem where we

427
00:28:29,440 –> 00:28:33,950
can deliver this service irregardless of location or irregardless of

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00:28:33,950 –> 00:28:37,760
server stacks. So making sure that it was truly cloud- based

429
00:28:37,760 –> 00:28:40,930
and global, making sure that we focused on our digital

430
00:28:40,930 –> 00:28:43,870
channels with our bots, but also our voice channels as

431
00:28:43,870 –> 00:28:48,800
well too. So traditional channels alongside with our chat and

432
00:28:48,800 –> 00:28:53,330
social channels introducing bots and what we call as botlets,

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00:28:53,520 –> 00:28:58,410
so many bots within bots to doing multiple tasks at

434
00:28:58,410 –> 00:29:01,530
once. And then of course, self service and automation was

435
00:29:01,530 –> 00:29:06,360
the key goal and the key challenge for this project.

436
00:29:06,730 –> 00:29:08,900
So a little bit of the results before I get

437
00:29:08,900 –> 00:29:13,370
into kind of some quantitative return on investment results, we

438
00:29:13,370 –> 00:29:19,080
developed and deployed Genesys within less than 90 days of

439
00:29:19,430 –> 00:29:23,900
contract signature. The actual build itself took less than 30

440
00:29:23,900 –> 00:29:28,330
days, but 90 days from the start of project planning

441
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to the actual go live. What we found is by

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00:29:32,170 –> 00:29:34,530
migrating to the cloud, by migrating to a tool like

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00:29:34,530 –> 00:29:38,220
Genesys, it’s easier for our resources, so that we can handle

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00:29:38,430 –> 00:29:44,500
large increases in volume, large spikes. Obviously, with COVID hitting

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00:29:45,000 –> 00:29:49,530
we had a huge increase in our online retail or

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through Fabletics, which is one of our brands because it

447
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is athletes or at home wear. So everyone wanted to

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00:29:55,570 –> 00:29:58,910
buy that at home wear as they’re working at home

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versus having to go into the office and dressing up.

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We also found that we had increased efficiency in managing

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00:30:06,050 –> 00:30:09,110
our interactions. And I’ll go into that in a little bit

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in the next couple of slides. And then we wanted

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to make sure that there was an integration into not

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only our current ecosystem and tool sets, but we also

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00:30:17,440 –> 00:30:20,730
have the ability to integrate into new areas that we

456
00:30:20,730 –> 00:30:24,350
haven’t done quite yet, social would be a great example

457
00:30:24,350 –> 00:30:28,580
of that. So background of who Textile Fashion Group is.

458
00:30:28,580 –> 00:30:30,790
So we have a little bit of context. I said this

459
00:30:30,790 –> 00:30:34,160
a couple of times before we’re conglomerate E- commerce website

460
00:30:34,470 –> 00:30:37,310
that sells fashion. We call it Fast Fashion because all

461
00:30:37,310 –> 00:30:39,960
of the products on our websites change every single month.

462
00:30:39,960 –> 00:30:41,610
So you’re not going to see a product that will

463
00:30:41,610 –> 00:30:46,290
exist on our websites for longer than a certain period.

464
00:30:46,290 –> 00:30:49,570
Usually it’s one month turnaround. We have five different brands.

465
00:30:50,120 –> 00:30:52,570
The big ones that you would probably know about or have heard

466
00:30:52,570 –> 00:30:56,310
are Fabletics. We just launched Fabletics men with Kevin Hart.

467
00:30:57,190 –> 00:31:02,080
Fabletics is a huge product of ours with Kate Hudson.

468
00:31:03,130 –> 00:31:06,890
We launched Savage X Fenty with Rihanna, which is disrupting

469
00:31:06,890 –> 00:31:10,360
the lingerie market. And then some of our brands are

470
00:31:10,390 –> 00:31:13,710
JustFab and ShoeDazzle and of course, FabKids for our lovable

471
00:31:13,710 –> 00:31:17,950
kids as well. And we partner with celebrities across all

472
00:31:17,950 –> 00:31:23,170
different platforms uniquely using their marketing skills across social media,

473
00:31:23,490 –> 00:31:26,850
and then we are a monthly membership. So our customers

474
00:31:26,850 –> 00:31:31,200
do contact us on a pretty regular basis. We’ve had

475
00:31:31,200 –> 00:31:34,570
at least two touch points with every single member every

476
00:31:34,570 –> 00:31:38,450
single month, which is a pretty high contact rate. So

477
00:31:38,450 –> 00:31:41,580
to put some more specific numbers to that five million

478
00:31:41,580 –> 00:31:47,550
members globally, 12 different countries across seven different languages. We

479
00:31:47,550 –> 00:31:50,820
get about six million phone calls per year, three million

480
00:31:50,820 –> 00:31:54,210
chats per year. And on average, our conversations last about

481
00:31:54,210 –> 00:32:01,470
nine minutes. What we did when we released our Chatbot for

482
00:32:01,470 –> 00:32:03,900
the first time for the first year, this was the

483
00:32:03,900 –> 00:32:09,430
results of our Chatbot. Obviously, the operational savings here we

484
00:32:09,430 –> 00:32:13,180
saved $ 1. 1 million in the first year and how

485
00:32:13,180 –> 00:32:17,480
we achieved that was by going from 0% containment to

486
00:32:17,530 –> 00:32:23,510
18.5% self service containment. So that means that members and customers

487
00:32:23,510 –> 00:32:26,850
were able to get what they’re looking for 18.5% of

488
00:32:26,850 –> 00:32:29,900
the time without having to go to a live agent.

489
00:32:30,340 –> 00:32:33,360
And then for the people who did have to go

490
00:32:33,360 –> 00:32:35,900
to a live agent reduced the handle time by 45

491
00:32:35,900 –> 00:32:38,840
seconds. And the ways we did that of course were

492
00:32:38,840 –> 00:32:43,130
by screen pops, creating of cases and tickets automatically also

493
00:32:43,130 –> 00:32:47,420
auto dispositioning those cases, because we already knew we captured the

494
00:32:47,420 –> 00:32:52,430
intent of the initial questions and then also providing the

495
00:32:52,430 –> 00:32:55,760
full transcript of the bot to the agent before they

496
00:32:55,760 –> 00:32:58,080
answered the call. So they know exactly what they need

497
00:32:58,080 –> 00:33:01,280
to jump into without having to ask those kind of

498
00:33:01,700 –> 00:33:04,420
easy questions like who am I speaking with and what

499
00:33:04,420 –> 00:33:07,810
can I help you with today? And then of course, without

500
00:33:07,810 –> 00:33:11,030
sacrificing any customer experience, we wanted to make sure that

501
00:33:11,030 –> 00:33:15,780
we had a high level of satisfaction and we scored

502
00:33:15,780 –> 00:33:20,900
a 92% member satisfaction score. And that’s specifically to the

503
00:33:20,900 –> 00:33:25,980
self contained bots which ironically is slightly higher than what

504
00:33:25,980 –> 00:33:30,470
our member satisfaction scores for live agents. So our bot

505
00:33:30,470 –> 00:33:36,060
actually outperformed from our member satisfaction standpoint across all of

506
00:33:36,060 –> 00:33:39,780
our member base. So to drill a little bit further

507
00:33:39,780 –> 00:33:45,020
into what our customers were saying about AI and automation

508
00:33:45,020 –> 00:33:50,170
and conversational AI as they’re interacting with our bots. So this is

509
00:33:50,170 –> 00:33:54,420
just a quick snippet of what our members were saying

510
00:33:54,420 –> 00:33:57,740
in response through the feedback loop. I won’t read all

511
00:33:57,740 –> 00:34:00,500
of these. I know that this content will be recorded

512
00:34:00,500 –> 00:34:02,470
and you could always come back and review them on

513
00:34:02,470 –> 00:34:05,280
your own. But I love the middle one here that

514
00:34:05,280 –> 00:34:09,740
says, ” I just talked to an automated customer service line

515
00:34:10,110 –> 00:34:13,940
that understood full sentences, work better and faster than actual

516
00:34:13,940 –> 00:34:18,820
people.” Of course, actual people are very, very productive. I

517
00:34:18,820 –> 00:34:21,430
don’t want to downplay that at all, but what I

518
00:34:21,430 –> 00:34:24,730
want to show here is that you can design these

519
00:34:24,730 –> 00:34:30,700
customer experience journeys to Janelle’s point to really not sacrifice

520
00:34:30,700 –> 00:34:34,820
customer experience and really create that level of empathy as

521
00:34:34,820 –> 00:34:39,620
they’re going through this journey or this process. So I’d like

522
00:34:39,620 –> 00:34:41,860
to pause here and pass it back to Claire. I

523
00:34:41,860 –> 00:34:45,490
know there’s another poll question coming up, so I’ll pass

524
00:34:45,490 –> 00:34:51,620
it over to you. Thanks for that Aarde and just

525
00:34:51,620 –> 00:34:54,550
building on what Aarde was sharing about all of the benefits

526
00:34:54,550 –> 00:34:57,350
that they’ve achieved at Textile, we’d like to ask you

527
00:34:57,350 –> 00:35:01,980
the question now, what AI benefit is most of interest

528
00:35:01,980 –> 00:35:07,360
to you today? Improved operational efficiency and cost savings, improved

529
00:35:07,360 –> 00:35:13,890
management decision- making, improved customer experience, faster time to market,

530
00:35:14,250 –> 00:35:20,160
better risk management, increased revenue or improved compliance? So you have

531
00:35:20,160 –> 00:35:22,300
a few options and I’ll give you a few seconds

532
00:35:22,300 –> 00:35:27,170
to answer that question. Janelle based on the conversations that you’re having

533
00:35:27,610 –> 00:35:30,900
with customers, what would you anticipate would be the response?

534
00:35:32,130 –> 00:35:38,280
Well, I’m super interested to see it because really the

535
00:35:38,280 –> 00:35:41,210
ones that stand out to me in my conversations with

536
00:35:41,210 –> 00:35:45,330
customers are the first one, which was cost savings, which

537
00:35:45,330 –> 00:35:47,540
was at the top of the list from the survey

538
00:35:47,540 –> 00:35:52,090
responses. At the same time, Aarde just mentioned how they’re

539
00:35:52,090 –> 00:35:56,630
using it to improve customer experience and drive revenue as

540
00:35:56,630 –> 00:35:59,590
well. And when you live in a state of the

541
00:35:59,590 –> 00:36:02,740
world where many businesses are just looking for new ways

542
00:36:02,740 –> 00:36:07,100
to continue to service and sell, I’m wondering what the

543
00:36:07,100 –> 00:36:10,180
feedback will be. So maybe we can go to the

544
00:36:10,180 –> 00:36:17,750
results and see where we are. take a look. Okay. So what

545
00:36:17,750 –> 00:36:21,440
AI benefit is most of interest to you today? So

546
00:36:21,480 –> 00:36:26,450
45%, almost half say improved operational efficiency and cost savings

547
00:36:27,900 –> 00:36:35,530
followed by customer experience and increased revenue is just 4.2%.

548
00:36:35,930 –> 00:36:39,500
That’s very interesting. And I wonder if that relates back

549
00:36:39,500 –> 00:36:44,420
to where the audience is in the AI journey, if

550
00:36:45,450 –> 00:36:48,130
a lot of the audience is in the early testing

551
00:36:48,130 –> 00:36:54,120
stages, looking at rolling AI out in the customer journey, perhaps

552
00:36:54,120 –> 00:36:56,530
there’s still a lot of low hanging fruit on the

553
00:36:56,530 –> 00:37:02,230
operational efficiency side. Janelle, do you have a reaction? I think

554
00:37:02,230 –> 00:37:05,890
you might be right. I think also as Aarde alluded

555
00:37:05,890 –> 00:37:08,660
to just in the benefits that they’ve seen so far

556
00:37:09,020 –> 00:37:12,610
at Textile, it doesn’t have to be one or the

557
00:37:12,620 –> 00:37:18,070
other necessarily either, right? Where you can go for some

558
00:37:18,070 –> 00:37:21,040
low hanging fruit around efficiency gains, but at the same

559
00:37:21,040 –> 00:37:25,760
time that that bot that gained efficiency is also improved customer

560
00:37:25,760 –> 00:37:29,860
experience and drove loyalty, which drives more revenue. So I

561
00:37:29,860 –> 00:37:33,480
think that even though the audience had one option I

562
00:37:33,480 –> 00:37:36,310
bet that what they’ll find in their planning and discovery

563
00:37:36,310 –> 00:37:39,430
is that several of these benefits will come into play.

564
00:37:41,230 –> 00:37:46,430
Yeah. And maybe what I can do too, is going

565
00:37:46,430 –> 00:37:50,730
back to what I shared with the various AI applications

566
00:37:51,150 –> 00:37:55,470
that are fueled to provide experience as a service to

567
00:37:55,470 –> 00:37:59,980
really personalize those experience, share some more benefits too on

568
00:38:00,250 –> 00:38:04,150
what companies are seeing out there. So I won’t hit

569
00:38:04,150 –> 00:38:07,330
on every one of these as we looked at the

570
00:38:07,620 –> 00:38:11,590
eight different AI powered applications that support the customer journey

571
00:38:11,590 –> 00:38:14,620
and employee journey. I’ll just cover a couple of these

572
00:38:15,600 –> 00:38:19,990
related to Voicebots, as well as Chatbots, which I’ll showcase

573
00:38:19,990 –> 00:38:23,850
later. Aarde alluded to some benefits here. We see as

574
00:38:23,850 –> 00:38:29,610
well that oftentimes companies are seeing 50% or more containment

575
00:38:29,610 –> 00:38:33,790
rates but we know too that it isn’t just about containing

576
00:38:33,790 –> 00:38:37,190
and self- service, it really capturing the context of the customer

577
00:38:37,400 –> 00:38:39,610
and getting them to that next step of the journey

578
00:38:39,610 –> 00:38:41,930
and that bots can do that really well. And in

579
00:38:42,150 –> 00:38:46,220
Textile’s case, feedback showed you even more so in some

580
00:38:46,220 –> 00:38:52,420
instances than real people. So certainly tons of benefits there

581
00:38:52,900 –> 00:38:57,050
but even ahead of that and with predictive engagement Claire,

582
00:38:57,050 –> 00:39:01,010
you alluded to financial services being in the leading position

583
00:39:01,010 –> 00:39:05,570
around looking to incorporate AI over the next three years

584
00:39:05,570 –> 00:39:08,530
if they haven’t already. And this is certainly where we’re

585
00:39:08,530 –> 00:39:12,550
seeing predictive engagement come into play too. So these example

586
00:39:12,550 –> 00:39:17,790
benefits come from a financial services institution over in Europe,

587
00:39:17,790 –> 00:39:22,940
where they implemented that engagement feature, where someone is searching

588
00:39:22,940 –> 00:39:28,230
for mortgages and at that right moment they would monitor that

589
00:39:28,230 –> 00:39:32,500
behavior and pop up an invitation to chat or to

590
00:39:32,500 –> 00:39:35,960
offer a callback or a special offer to really drive

591
00:39:36,170 –> 00:39:38,440
more sales at the end of the day. And they

592
00:39:38,440 –> 00:39:43,080
saw gigantic benefits, not only in increasing revenue with Forex

593
00:39:43,080 –> 00:39:47,130
conversion rates but also cost savings and lower cost per

594
00:39:47,130 –> 00:39:52,650
lead and improved experience double digits in NPS improvement from

595
00:39:52,650 –> 00:39:55,850
that application. If I go to some of the others

596
00:39:56,200 –> 00:40:00,870
predictive routing, that’s that application that takes into account all

597
00:40:00,870 –> 00:40:03,960
of that context around the customer as well as that

598
00:40:03,960 –> 00:40:07,960
employee to get to that really especial match between the

599
00:40:07,960 –> 00:40:11,790
two as somebody wants to talk to or interact with

600
00:40:12,080 –> 00:40:16,730
a live person. We’re seeing this very popular in large

601
00:40:16,730 –> 00:40:22,270
telecommunications companies as one industry example where maybe they have

602
00:40:22,270 –> 00:40:27,270
thousands of contact center resources already, maybe they’re already doing really

603
00:40:27,270 –> 00:40:31,160
sophisticated skills- based routing, but they want to take it

604
00:40:31,160 –> 00:40:34,160
to the next level. And so predictive routing for them

605
00:40:34,460 –> 00:40:36,900
gave them some of the benefits that you see here.

606
00:40:37,490 –> 00:40:41,240
They started in some customer service based use cases and

607
00:40:41,240 –> 00:40:46,400
improving handle times or first contact resolutions quickly saw the

608
00:40:46,400 –> 00:40:50,220
benefits and then next we’re targeting those increased revenue benefits

609
00:40:50,220 –> 00:40:55,510
for their sales organization. Moving on, looking at interaction analytics,

610
00:40:55,850 –> 00:40:59,990
of course, being able to get those better insights into

611
00:40:59,990 –> 00:41:03,710
the employee’s performance. And the intent of the customers help

612
00:41:03,710 –> 00:41:06,300
in a variety of different ways when it comes to

613
00:41:06,380 –> 00:41:10,910
coaching and quality. In addition, we’re seeing a rise in

614
00:41:10,910 –> 00:41:15,670
interest around agent assist, especially in these times where employees

615
00:41:15,670 –> 00:41:20,020
are distributed and virtual and need those extra prompts for

616
00:41:20,020 –> 00:41:23,170
engagement and support, being able to have that context right

617
00:41:23,170 –> 00:41:26,500
in front of them in that consolidated desktop to really

618
00:41:26,500 –> 00:41:31,820
guide them through the conversation is increasingly important. And then

619
00:41:32,090 –> 00:41:37,530
related to employee engagement too, we’re proud to have offered

620
00:41:37,530 –> 00:41:41,620
the first AI based workforce management in the market where

621
00:41:42,120 –> 00:41:46,460
for forecasting and scheduling, we could not only accelerate the

622
00:41:46,460 –> 00:41:50,680
speed of running the schedules, but also the accuracy, which

623
00:41:50,680 –> 00:41:54,150
is evermore important because many employees aren’t in a contact

624
00:41:54,150 –> 00:41:56,260
center that you can monitor and just walk up to

625
00:41:56,260 –> 00:41:59,010
their desk anymore. They’re all virtual. So being able to

626
00:41:59,010 –> 00:42:03,670
have those tools at hand and make changes with faster agility

627
00:42:03,900 –> 00:42:07,350
becomes increasingly important. So those are just a couple of

628
00:42:07,350 –> 00:42:10,910
examples to just drill in a little bit more around

629
00:42:10,910 –> 00:42:15,080
the business benefits and business outcomes, because as you’re doing

630
00:42:15,370 –> 00:42:19,200
your planning and you’re discovering, it’s not about just implementing

631
00:42:19,200 –> 00:42:21,970
AI for the sake of implementing AI, right? It’s about

632
00:42:21,970 –> 00:42:26,290
really driving towards what business benefits and business outcomes that

633
00:42:26,290 –> 00:42:29,710
you’re targeting. So with that said, Claire I will turn

634
00:42:29,710 –> 00:42:33,430
it back over to you. Great. Thank you for that.

635
00:42:36,520 –> 00:42:39,830
So we touched on this earlier, and of course there’s a

636
00:42:39,830 –> 00:42:43,640
lot of benefits to be achieved from rolling out AI,

637
00:42:43,910 –> 00:42:48,760
but the survey also polls the respondents on the challenges.

638
00:42:48,940 –> 00:42:51,860
What are the greatest challenges to your company’s use of

639
00:42:51,860 –> 00:42:55,240
AI? And the leading challenge that came back was the

640
00:42:55,240 –> 00:43:00,550
business or process challenges of using AI insights, followed by

641
00:43:00,550 –> 00:43:05,710
data quality or availability, and then the shortage of AI

642
00:43:06,260 –> 00:43:09,000
talent in the business. So let me just give you

643
00:43:09,000 –> 00:43:13,090
an example that we actually highlighted in the report around the

644
00:43:13,090 –> 00:43:17,090
business process challenges. So we spoke to the head of

645
00:43:17,090 –> 00:43:21,050
AI at the Leading Global Airline, and they identified that

646
00:43:21,050 –> 00:43:25,680
a lot of costs could be saved by correctly forecasting

647
00:43:25,720 –> 00:43:32,060
what meal the passengers would select in premium class. So

648
00:43:32,060 –> 00:43:36,680
rather than having three choices available to every passenger, being

649
00:43:36,680 –> 00:43:39,510
able to predict in advance, I’m going to choose the

650
00:43:39,510 –> 00:43:41,920
fish and the person sitting next to me is going to

651
00:43:41,920 –> 00:43:44,490
choose the beef, would save a lot of money for

652
00:43:44,490 –> 00:43:47,890
the airline. And actually they became very, very good at

653
00:43:47,950 –> 00:43:52,620
predicting what people would choose. The challenge was changing the

654
00:43:52,620 –> 00:43:56,980
catering processes globally to be able to use that data

655
00:43:56,980 –> 00:44:00,240
in real time as the passenger list changes. So that

656
00:44:00,240 –> 00:44:05,160
was one example that came through the survey. Janelle, can

657
00:44:05,160 –> 00:44:06,790
I kind of pick on you to share some of

658
00:44:06,790 –> 00:44:12,470
the challenges that you’ve seen with your customers? Yeah. A couple comes

659
00:44:13,110 –> 00:44:15,650
to mind and certainly they can span all of the

660
00:44:15,870 –> 00:44:18,610
ones that are listed here, but I’ll just highlight maybe three

661
00:44:18,610 –> 00:44:23,640
specifics and then share some advice as well as some

662
00:44:23,640 –> 00:44:26,620
proof points that can go with those three. So the

663
00:44:26,620 –> 00:44:30,600
first one is your point around data quality or availability

664
00:44:30,600 –> 00:44:34,030
where you had mentioned 48% of the respondents noted this

665
00:44:34,030 –> 00:44:37,460
as a challenge. Absolutely, we see that too. So my

666
00:44:37,460 –> 00:44:41,530
advice here is when you’re looking to make a selection

667
00:44:41,610 –> 00:44:46,370
for your AI applications, keep a vendor in mind that

668
00:44:46,660 –> 00:44:51,930
provides openness and flexibility there, so that for your AI

669
00:44:51,930 –> 00:44:56,040
applications, you can seamlessly integrate to all of these underlying

670
00:44:56,040 –> 00:44:59,390
data sources that it needs to pull from CRM systems

671
00:45:00,590 –> 00:45:04,390
and any third party data, as well as the real time information,

672
00:45:04,670 –> 00:45:08,180
of course. And so being able to leverage an AI

673
00:45:08,230 –> 00:45:11,830
platform that is open, that doesn’t just lock you into

674
00:45:11,830 –> 00:45:15,170
their own set of data, that might be still very

675
00:45:15,960 –> 00:45:19,820
siloed, but allows you to orchestrate and collect data from

676
00:45:19,820 –> 00:45:24,170
any source as well as even orchestrate multiple AI technologies

677
00:45:24,170 –> 00:45:27,570
together. And so they can interact between one another, I

678
00:45:27,570 –> 00:45:32,220
think is really, really important. And so one proof point

679
00:45:32,220 –> 00:45:36,370
here is a company called Intel that chose us to

680
00:45:36,400 –> 00:45:40,590
help overcome that challenge. They had a live tech support

681
00:45:41,000 –> 00:45:45,250
Voicebot and they were using our solutions along with a

682
00:45:45,250 –> 00:45:48,990
variety of other different third party technologies to sort of

683
00:45:48,990 –> 00:45:53,100
be that orchestrator, even amongst their bots, because in some bots

684
00:45:53,100 –> 00:45:57,320
they might choose for certain specialties or they already had

685
00:45:57,320 –> 00:46:01,940
that, they wanted to leverage their existing investments. So garbage in,

686
00:46:01,940 –> 00:46:05,040
garbage out, right? So data quality of course is a

687
00:46:05,040 –> 00:46:08,180
really important challenge. And so be able to choose an open

688
00:46:08,180 –> 00:46:11,210
platform that allows you to pull from any source is

689
00:46:11,210 –> 00:46:14,690
really, really key. The second one here on the shortage

690
00:46:14,690 –> 00:46:18,290
of AI developers and data scientists, Aarde I am going

691
00:46:18,290 –> 00:46:20,810
to pick on you because I got the proof point and

692
00:46:20,910 –> 00:46:22,540
you, why don’t you speak to this one for us?

693
00:46:24,700 –> 00:46:29,570
Definitely. It’s a living organism. Whenever you’re creating AI or

694
00:46:29,570 –> 00:46:34,730
deploying machine learning around a certain business objective, you can’t

695
00:46:34,730 –> 00:46:38,110
just implement and forget. So not only is there a

696
00:46:38,110 –> 00:46:42,030
need for resources, AI developers and data scientists in the

697
00:46:42,030 –> 00:46:45,700
beginning, but also a little bit of resources needed on

698
00:46:45,700 –> 00:46:48,610
the backend just to maintain the product, making sure that

699
00:46:50,120 –> 00:46:53,220
it’s doing the right thing. I think the best analogy

700
00:46:53,220 –> 00:46:57,060
I’ve ever heard is it’s like a garden. You’re growing vegetables

701
00:46:57,060 –> 00:46:59,880
out in the garden, you need to not only dig the

702
00:46:59,960 –> 00:47:03,270
initial holes to plant with the initial plants, but as

703
00:47:03,270 –> 00:47:06,380
it’s growing, you need to make sure you’re watering it.

704
00:47:06,380 –> 00:47:11,230
It gets enough sun, that you’re clipping the appropriate leaves.

705
00:47:11,390 –> 00:47:13,790
It’s not something that you could set and forget, and

706
00:47:13,790 –> 00:47:17,190
that does take time, energy and resources. It’s very easy

707
00:47:17,190 –> 00:47:21,690
for us to deploy something and then prove its success

708
00:47:21,690 –> 00:47:24,410
and say, what? That’s great. Let’s now deploy something else.

709
00:47:24,480 –> 00:47:26,800
And then we totally forget about it. And then two

710
00:47:26,800 –> 00:47:29,890
years later, it’s completely stale. It’s doing things that it

711
00:47:29,890 –> 00:47:33,240
shouldn’t be doing, or it’s not necessarily creating the outcomes

712
00:47:33,240 –> 00:47:36,110
that we wanted. So it’s really important to make sure

713
00:47:36,110 –> 00:47:39,360
that you invest in developers and data scientists to help

714
00:47:39,360 –> 00:47:44,610
support your business objectives. Thanks Aarde. And the third one

715
00:47:44,610 –> 00:47:47,770
I would highlight is a challenge we often see too.

716
00:47:47,770 –> 00:47:50,950
And just being able to demonstrate the business value and

717
00:47:50,950 –> 00:47:54,460
return on investment with AI. So one of the ways

718
00:47:54,460 –> 00:47:58,020
that we help our clients is by even just offering

719
00:47:58,020 –> 00:48:01,930
some really fast and easy proof points, build a bot

720
00:48:01,930 –> 00:48:05,650
workshop where within an hour, you can get your hands

721
00:48:05,650 –> 00:48:09,680
on the technology and build a basic bot to be

722
00:48:09,680 –> 00:48:14,270
able to understand and prove out and showcase how you

723
00:48:14,270 –> 00:48:18,520
can gain efficiencies in handling influxes of volume as an

724
00:48:18,520 –> 00:48:22,650
example. We offer pretrials so you can try before you buy

725
00:48:22,650 –> 00:48:25,650
to be able to prove out that technology too. And

726
00:48:25,650 –> 00:48:28,930
then lastly, I mentioned that we have a value consulting

727
00:48:28,930 –> 00:48:31,890
and business consulting team, whereas as part of the process

728
00:48:31,890 –> 00:48:36,050
and working with you, we look to help you with

729
00:48:36,080 –> 00:48:39,500
the financial models and the business benefit and benchmarks that

730
00:48:39,500 –> 00:48:42,690
you need to help justify the solution, and then can

731
00:48:42,690 –> 00:48:45,340
we follow up with you and working with you in

732
00:48:45,420 –> 00:48:50,670
understanding how you can realize that information and prove it

733
00:48:50,670 –> 00:48:52,760
out to share with your key stakeholders that were a

734
00:48:52,760 –> 00:48:55,970
part of the decision and in investing to begin with.

735
00:48:56,640 –> 00:49:00,830
So those are a couple from your slide before. Claire,

736
00:49:00,830 –> 00:49:03,590
that jumped out to me in terms of AI challenges and proof

737
00:49:03,590 –> 00:49:07,120
points and how we help companies. Thank you very much

738
00:49:07,410 –> 00:49:10,650
Janelle. Okay. I’m going to change tack a little bit now.

739
00:49:10,910 –> 00:49:13,480
I don’t know if you recall at the beginning, I said that

740
00:49:13,480 –> 00:49:17,290
we will be talking about this concept of data sharing. So

741
00:49:17,330 –> 00:49:21,450
what would be the value of you taking an anonymized

742
00:49:21,450 –> 00:49:24,370
set of data and sharing it with a third party

743
00:49:24,370 –> 00:49:27,650
and what could be some of the mutual benefits that

744
00:49:27,650 –> 00:49:31,320
would come out of that. And we’re already seeing various examples

745
00:49:31,310 –> 00:49:34,990
across industries. I’ll give you a couple of examples. So

746
00:49:35,390 –> 00:49:40,670
transportation companies sharing much more detailed supply chain data along

747
00:49:40,780 –> 00:49:42,760
the supply chain so that people can give much more

748
00:49:42,810 –> 00:49:49,790
accurate timing estimates to their customers or a telecommunications company sharing

749
00:49:49,790 –> 00:49:54,750
information about customers are switching SIM cards frequently and sharing that

750
00:49:54,860 –> 00:49:58,160
information with banks to help them identify cases of fraud.

751
00:49:58,550 –> 00:50:02,070
So various examples of where companies are looking at the

752
00:50:02,070 –> 00:50:04,990
value of their data and how in future, they might

753
00:50:04,990 –> 00:50:09,380
share it with third parties to develop AI, to develop new

754
00:50:09,380 –> 00:50:13,610
business models, to develop new supply chain efficiencies. And so

755
00:50:13,610 –> 00:50:16,970
we ask the survey respondents, what would be the greatest

756
00:50:16,970 –> 00:50:21,220
benefits of sharing data with companies in your own or

757
00:50:21,220 –> 00:50:26,840
adjacent industries? And the majority 56%, say that the greatest benefit they

758
00:50:26,840 –> 00:50:32,240
could foresee will be faster and more transparent supply chains

759
00:50:32,580 –> 00:50:36,600
followed by faster and more innovative product development and then

760
00:50:36,620 –> 00:50:41,190
new and enhanced customer services and experiences. And I know

761
00:50:41,190 –> 00:50:45,770
that Genesys has been taking some initiatives looking into this

762
00:50:45,770 –> 00:50:48,500
space of data sharing. Janelle, can you give us a

763
00:50:48,500 –> 00:50:50,360
couple of examples of what you see this kind of

764
00:50:51,060 –> 00:50:56,730
the value of sharing data to be. Well, absolutely. So

765
00:50:56,730 –> 00:51:00,240
when it comes to sharing data, I’ll go back to

766
00:51:00,240 –> 00:51:03,360
some of the obstacles, right? So if you go to

767
00:51:03,360 –> 00:51:07,010
the next slide real quick Claire and I look at

768
00:51:07,330 –> 00:51:13,530
the second bar chart, as a technology provider, the second

769
00:51:13,530 –> 00:51:17,050
one here jumps out to me as something where along

770
00:51:17,050 –> 00:51:20,240
with global tech companies, we can be part of removing

771
00:51:20,240 –> 00:51:24,700
the obstacles associated with managing and transforming that data across

772
00:51:24,700 –> 00:51:27,810
many systems, because if you’re going to share it, you need

773
00:51:27,810 –> 00:51:30,830
to have it sort of in a shareable format to

774
00:51:30,830 –> 00:51:35,640
begin with. So that’s why Genesys has become one of

775
00:51:35,640 –> 00:51:39,380
the only providers that are part of two key initiatives

776
00:51:39,380 –> 00:51:46,340
in being able to have open APIs amongst the key

777
00:51:46,340 –> 00:51:49,200
data providers. So two that come to mind are the

778
00:51:49,200 –> 00:51:53,300
Cloud Information Model, where we partner with companies like Amazon

779
00:51:53,300 –> 00:51:56,690
and Salesforce, as well as the open data initiative, where

780
00:51:56,690 –> 00:52:01,420
we’re partnering with other companies like Adobe, SAP and Microsoft.

781
00:52:01,950 –> 00:52:06,070
To really conquer this challenge for companies like you, we’re

782
00:52:06,070 –> 00:52:11,600
destroying data silos. We know isn’t simple but joining forces

783
00:52:11,600 –> 00:52:16,640
to establish more interoperability and data exchange in a safe

784
00:52:16,640 –> 00:52:19,540
and secure way is a step in the right direction

785
00:52:19,540 –> 00:52:24,330
that we’re really excited about. Yeah. So what this chart

786
00:52:24,330 –> 00:52:28,170
shows is the developments that companies would need to see

787
00:52:28,170 –> 00:52:31,470
to be even more active in the data sharing space.

788
00:52:31,620 –> 00:52:34,350
And so 64% say that they would want to see

789
00:52:34,350 –> 00:52:38,570
greater regulatory clarity, what exactly are the rules on data

790
00:52:38,570 –> 00:52:42,750
sharing and then they’d also be looking for the agreed

791
00:52:42,750 –> 00:52:48,030
industry standards on how data can be safely, ethically, legally

792
00:52:48,030 –> 00:52:54,130
shared across organizations. And also if they saw competitors initiatives

793
00:52:54,340 –> 00:52:57,820
to increase the data that they’re sharing, that might be

794
00:52:57,820 –> 00:53:03,110
something that would move them into data sharing. So the next slide,

795
00:53:04,000 –> 00:53:07,580
the next question we ask them is, how willing would you

796
00:53:07,580 –> 00:53:11,610
be to share internal data? And what we saw is

797
00:53:11,610 –> 00:53:15,430
that executives in the America, so Latin America and the

798
00:53:15,430 –> 00:53:20,080
North America are the most enthusiastic globally saying they are

799
00:53:20,080 –> 00:53:25,520
either very willing or somewhat willing to share data. In

800
00:53:25,520 –> 00:53:29,610
Asia Pacific, Europe, the Middle East and Africa, perhaps a

801
00:53:29,610 –> 00:53:32,910
little bit more hesitant too, a little bit more caution

802
00:53:32,910 –> 00:53:38,690
about data sharing perhaps it’s because GDPR is very, very

803
00:53:38,690 –> 00:53:43,590
strict regulations on data sharing and just culturally more conservative

804
00:53:43,590 –> 00:53:49,130
cultures around sharing information and data. So that brings us

805
00:53:49,130 –> 00:53:52,630
to the end of the content that we wanted to

806
00:53:52,630 –> 00:53:55,760
share with you today and leaves us some time for

807
00:53:55,760 –> 00:53:59,200
Q& A, just about five minutes for Q& A. After

808
00:53:59,200 –> 00:54:03,470
that, I will share some key takeaways from this presentation

809
00:54:03,670 –> 00:54:06,360
and then Josh will come back and share some more

810
00:54:06,360 –> 00:54:08,730
of the resources that are available to you after this

811
00:54:08,730 –> 00:54:12,580
webinar. So before we get going on the Q& A

812
00:54:12,800 –> 00:54:15,090
Josh, can I just bring you back in to explain

813
00:54:15,090 –> 00:54:19,410
to the audience how it would work? Yes, absolutely. So

814
00:54:19,610 –> 00:54:22,030
for everybody who’s going to participate in today’s Q& A,

815
00:54:22,740 –> 00:54:25,620
there’s a Q& A chat window below the slide window. If

816
00:54:25,620 –> 00:54:27,820
you put your questions in there, we’ll get through as

817
00:54:27,820 –> 00:54:29,530
many as we can with the short amount of time that

818
00:54:29,530 –> 00:54:32,140
we have. But we do encourage you to go ahead

819
00:54:32,140 –> 00:54:34,430
and throw those questions in there, because even if we

820
00:54:34,430 –> 00:54:36,750
don’t answer them aloud, we will follow up with you

821
00:54:36,750 –> 00:54:40,760
via email within the next few business days. So with that, Claire,

822
00:54:40,760 –> 00:54:43,990
if you want to kick things off, go ahead. Okay.

823
00:54:44,330 –> 00:54:46,280
So also, if you don’t have a question, but you

824
00:54:46,280 –> 00:54:48,890
wanted to make a comment or answer any of the

825
00:54:48,890 –> 00:54:51,560
questions that I’ve got up on the slide, how is

826
00:54:51,560 –> 00:54:54,390
AI making an impact in your business? What challenges have you

827
00:54:54,660 –> 00:54:57,720
seen? Have you seen the examples of data sharing in your

828
00:54:57,720 –> 00:55:00,520
industry? So while we wait for some questions to come

829
00:55:00,520 –> 00:55:03,130
in, if they’re going to, Aarde, I’d like to ask

830
00:55:03,130 –> 00:55:06,590
you, what would you say is your greatest lesson learned

831
00:55:06,620 –> 00:55:12,690
having been on this AI journey in your business? Yeah, this is a great

832
00:55:12,690 –> 00:55:16,530
question. And the lessons learned kind of change over time

833
00:55:16,530 –> 00:55:20,500
and depending on where you are in the journey either

834
00:55:21,140 –> 00:55:23,750
just deploying your first one or about to deploy your

835
00:55:23,750 –> 00:55:29,250
first AI or bot or tool or where we’re kind

836
00:55:29,250 –> 00:55:32,600
of we are now where we’ve had bots in place

837
00:55:32,600 –> 00:55:36,110
for more than a year, and now we’re trying to optimize

838
00:55:36,110 –> 00:55:37,840
or see what the next step is. I think the

839
00:55:37,840 –> 00:55:42,360
biggest challenge is getting executive buy- in. You have to

840
00:55:42,360 –> 00:55:44,460
get buy- in at the highest level. It has to

841
00:55:44,460 –> 00:55:47,210
be a company initiative. I know it’s very easy to

842
00:55:47,210 –> 00:55:50,970
start in a specific silo like customer service or sales

843
00:55:51,960 –> 00:55:56,370
and you can see some traumatic results, but it took

844
00:55:56,370 –> 00:55:59,290
for it to get the highest impact, it’s really important

845
00:55:59,290 –> 00:56:01,020
to get it all the way to your C- suite,

846
00:56:01,020 –> 00:56:07,990
your COO, your CEO or even the director of IT

847
00:56:08,330 –> 00:56:12,470
or CIO. I think that’s the biggest challenge, and it’s

848
00:56:12,470 –> 00:56:15,910
not necessarily a challenge because they’re not interested in investing

849
00:56:15,910 –> 00:56:18,380
in it, it’s a challenge in articulating how it could

850
00:56:18,380 –> 00:56:22,990
probably support some sort of business obstacle that they have

851
00:56:23,710 –> 00:56:25,830
that’s in front of. I would say that’s our biggest

852
00:56:26,190 –> 00:56:30,960
challenge. Yeah. Okay. So we have a very interesting question that’s come

853
00:56:30,960 –> 00:56:35,670
in and Janelle, I think it’s one that we can

854
00:56:35,670 –> 00:56:38,630
put to you a question from a gentleman called Ignacio.

855
00:56:38,960 –> 00:56:41,890
So in a few words, what is the company missing if

856
00:56:41,890 –> 00:56:45,200
they are not using AI on their customer service model?

857
00:56:47,730 –> 00:56:49,800
Well, if I could say two words, I would say

858
00:56:49,800 –> 00:56:54,350
business benefits. So hopefully of what you’ve seen from the

859
00:56:54,350 –> 00:56:57,960
examples that we’ve shown and what Aarde described as well

860
00:56:57,960 –> 00:57:03,240
as is that, in order to achieve efficiencies at scale,

861
00:57:03,360 –> 00:57:07,940
to achieve improved customer experience at scale to increase revenue,

862
00:57:08,560 –> 00:57:12,410
there’s real power with the use of artificial intelligence to

863
00:57:12,760 –> 00:57:16,460
be able to real time provide insights and actions that

864
00:57:16,460 –> 00:57:20,510
drive business results and business outcomes. So that I would say

865
00:57:20,510 –> 00:57:23,750
you might have great customer experience already, you might be

866
00:57:23,750 –> 00:57:26,780
missing an opportunity to take that to the next level

867
00:57:26,780 –> 00:57:35,020
if you don’t incorporate AI. Okay. So a comment from

868
00:57:35,020 –> 00:57:38,570
a lady called Carra, so we use Chatbots in place and

869
00:57:38,570 –> 00:57:41,600
have seen some positive results and are looking to take

870
00:57:41,600 –> 00:57:44,280
it to the level of full integration with our live

871
00:57:44,280 –> 00:57:48,320
chat. So Janelle, what would you think that their business

872
00:57:48,320 –> 00:57:53,570
needs to consider as they’re making that next step? Well,

873
00:57:53,570 –> 00:57:56,840
it’s hard to say not knowing how they’re using Chatbots

874
00:57:56,840 –> 00:57:59,910
today honestly but maybe Aarde I could ask for your

875
00:57:59,910 –> 00:58:03,800
opinion on this one having really been very close to

876
00:58:03,800 –> 00:58:10,290
the implementation at Textile. Yeah. So I would say if

877
00:58:10,290 –> 00:58:12,700
you’re just starting out with Chatbots, you’re probably doing an

878
00:58:12,700 –> 00:58:17,640
FAQ bot that’s learning the intent and then reporting back some

879
00:58:17,640 –> 00:58:21,510
sort of basic information from an FAQ guide. I would

880
00:58:21,510 –> 00:58:25,030
say the next level to that is integrating denigrations into

881
00:58:25,030 –> 00:58:28,980
your CRM or whatever tool sets that you have so

882
00:58:28,980 –> 00:58:32,330
that instead of just reading back some content, you could

883
00:58:32,330 –> 00:58:35,710
actually perform actions for your customers and truly give them

884
00:58:35,710 –> 00:58:39,870
self service. Think of it as like, ” Can you reset

885
00:58:39,870 –> 00:58:42,960
my password?” And instead of it saying, ” You could reset

886
00:58:42,960 –> 00:58:45,900
your password by logging in and clicking the reset password

887
00:58:45,900 –> 00:58:48,710
button.” Instead, it would say, ” Sure. I could reset your

888
00:58:48,710 –> 00:58:52,180
password. Let me go ahead and send you an SMS

889
00:58:52,180 –> 00:58:55,660
text message, click that link.” And it’ll reset it. It’ll

890
00:58:55,660 –> 00:59:01,100
expire in 10 minutes, something like that. So actionable, we

891
00:59:01,100 –> 00:59:04,940
call this virtual assistants versus just a Chatbot, that would be

892
00:59:05,000 –> 00:59:08,470
the next level. And then I guess kind of piggybacking

893
00:59:08,470 –> 00:59:12,080
off the first QA question, what you would miss by

894
00:59:12,080 –> 00:59:16,660
not implementing AI. And I think Janelle hit this on

895
00:59:16,660 –> 00:59:21,160
the head, it’s business insights, it’s KPIs and metrics that

896
00:59:21,160 –> 00:59:22,880
you can now measure. It’s going to open up a

897
00:59:22,880 –> 00:59:26,400
whole new area for you to have insights into and

898
00:59:26,400 –> 00:59:29,700
then that could help you create your customer experience and

899
00:59:29,700 –> 00:59:34,420
customer journey. I think we’re at time now. So I

900
00:59:34,420 –> 00:59:37,710
better hand it to Josh who will wrap the call

901
00:59:37,710 –> 00:59:40,700
and give us some resources that build on these topics.

902
00:59:42,720 –> 00:59:46,320
Sounds good to me. So just as next steps for

903
00:59:46,320 –> 00:59:50,210
everybody, you should see the resource list just next to

904
00:59:50,210 –> 00:59:52,770
the Q& A window. Make sure you click on these

905
00:59:52,770 –> 00:59:55,330
before we end today’s webcast. You can get the full

906
00:59:55,330 –> 00:59:58,680
MIT global AI agenda report. You can learn more about

907
00:59:58,680 –> 01:00:01,330
the AI power context center, and you can even get

908
01:00:01,330 –> 01:00:04,670
started today by building AI capabilities into your call center.

909
01:00:05,470 –> 01:00:07,520
So again, make sure that you click on that before we

910
01:00:07,520 –> 01:00:12,050
close out today. Also to wrap up, make sure you

911
01:00:12,050 –> 01:00:14,070
continue to throw questions that you still have, even though

912
01:00:14,070 –> 01:00:15,510
we ran out of time, we do want to make

913
01:00:15,510 –> 01:00:17,690
sure that we answer any questions. So throw those into

914
01:00:17,690 –> 01:00:20,320
the Q& A window before we end today. And I’ll be

915
01:00:20,320 –> 01:00:23,740
sure to answer as many as we can via email

916
01:00:23,740 –> 01:00:27,470
within the next few business days. Also after today’s webinar,

917
01:00:27,470 –> 01:00:30,760
you’re going to see a short survey pop up. We

918
01:00:30,760 –> 01:00:33,420
make sure that we tailor webinars towards what you want

919
01:00:33,420 –> 01:00:38,210
to learn more about. So be sure to throw your

920
01:00:38,210 –> 01:00:41,510
responses into the survey and click submit before today’s session

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ends and we’ll be sure to incorporate that feedback in

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the future. And also be sure to take a look

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at our podcast below. We have a new podcast called

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Tech Talks in Twenty where we discuss topics that you

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want to hear in just about 20 minutes. So you

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are welcome to listen today. So with all that being

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said on behalf of Janelle, Claire and Aarde and the

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entire Genesys team, we thank you again for joining today’s

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webcast AI in the Contact Center: The Promise, Reality and Future.

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Until next time, have a good one everyone. Bye. Thank

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01:01:17,870 –> 01:01:18,020
you.

Meet the Speakers

Claire beatty webinar image

Claire Beatty
Editorial Director for International Markets
MIT Technology Review Insights

Aarde webinar image

Aarde Cosseboom
Sr. Director of GMS Technology, Product, & Analytics
TechStyle

Janelle

Janelle Dieken
Senior Vice President, Product Marketing
Genesys

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