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

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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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your feedback about what you would like to hear in

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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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00:25:36,330 –> 00:25:39,540
that it can never be, but if you target specific

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00:25:39,540 –> 00:25:44,730
use cases that are tied to a very specific business

384
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outcome around improving web conversions as one example, it doesn’t

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00:25:49,730 –> 00:25:51,730
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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00:25:55,840 –> 00:25:58,810
more and more use cases in this area. I know

388
00:25:58,810 –> 00:26:03,010
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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00:26:05,740 –> 00:26:08,950
more to come and I would predict that 26% is

391
00:26:08,950 –> 00:26:14,050
going to grow. Great. Okay. So at this point, let me

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00:26:14,050 –> 00:26:17,560
hand over to you Aarde. We know that Textile is an

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00:26:17,560 –> 00:26:20,440
online retailer, but we’d love to hear more about your

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00:26:20,440 –> 00:26:26,650
business and about your AI journey. Yeah, absolutely. And before

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we go onto the next slide, what I want to

396
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talk a little bit about is some content on the

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00:26:30,990 –> 00:26:33,410
slide because it is relevant to our customer journey and

398
00:26:33,890 –> 00:26:36,730
how we developed AI to help support that customer journey.

399
00:26:37,110 –> 00:26:39,170
The low hanging fruit that we tackled for us was

400
00:26:39,170 –> 00:26:43,360
of course the operational efficiency and cost savings, reducing of

401
00:26:43,360 –> 00:26:49,190
handle time for our agents, reducing of transactional type conversations

402
00:26:49,190 –> 00:26:54,030
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

404
00:26:57,580 –> 00:27:02,220
efficiently. And of course some gains in the customer experience

405
00:27:02,220 –> 00:27:04,850
based on and I’m going to give you an example of that in the next

406
00:27:04,850 –> 00:27:08,120
couple of slides. But one of the things that is

407
00:27:08,900 –> 00:27:13,170
rarely looked at or often overlooked is since we are

408
00:27:13,170 –> 00:27:17,310
a membership model and E- commerce retailer membership model, we

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00:27:17,310 –> 00:27:21,940
did increase our revenue. And the way that we did

410
00:27:21,940 –> 00:27:26,910
this using AI with using machine learning tool to give

411
00:27:26,910 –> 00:27:30,220
us a lifetime value of the customer as they’re going

412
00:27:30,220 –> 00:27:34,730
through certain processes, of course the obvious processes are contacting

413
00:27:34,730 –> 00:27:39,610
our customer care team. But also as they’re navigating through

414
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

422
00:28:09,700 –> 00:28:14,050
going to talk specifically around our conversational AI and how we

423
00:28:14,050 –> 00:28:18,990
use Genesys to deliver this AI to not only our

424
00:28:18,990 –> 00:28:23,610
agents but to our customers. Zooming into our specific project,

425
00:28:24,150 –> 00:28:27,270
the things we’re looking for we’re a cloud- based product.

426
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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

428
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,

433
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
00:29:28,700 –> 00:29:32,170
to the actual go live. What we found is by

442
00:29:32,170 –> 00:29:34,530
migrating to the cloud, by migrating to a tool like

443
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

445
00:29:45,000 –> 00:29:49,530
we had a huge increase in our online retail or

446
00:29:49,850 –> 00:29:53,090
through Fabletics, which is one of our brands because it

447
00:29:53,090 –> 00:29:55,570
is athletes or at home wear. So everyone wanted to

448
00:29:55,570 –> 00:29:58,910
buy that at home wear as they’re working at home

449
00:29:58,910 –> 00:30:01,500
versus having to go into the office and dressing up.

450
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We also found that we had increased efficiency in managing

451
00:30:06,050 –> 00:30:09,110
our interactions. And I’ll go into that in a little bit

452
00:30:09,110 –> 00:30:11,240
in the next couple of slides. And then we wanted

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to make sure that there was an integration into not

454
00:30:13,940 –> 00:30:17,440
only our current ecosystem and tool sets, but we also

455
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.

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So you’re not going to see a product that will

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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

921
01:00:41,510 –> 01:00:43,860
ends and we’ll be sure to incorporate that feedback in

922
01:00:43,860 –> 01:00:47,650
the future. And also be sure to take a look

923
01:00:47,650 –> 01:00:50,840
at our podcast below. We have a new podcast called

924
01:00:50,840 –> 01:00:54,590
Tech Talks in Twenty where we discuss topics that you

925
01:00:54,590 –> 01:00:57,810
want to hear in just about 20 minutes. So you

926
01:00:57,810 –> 01:01:01,550
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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01:01:12,030 –> 01:01:17,540
Until next time, have a good one everyone. Bye. Thank

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you.