Analyst Webinar

Unlock the transformative power of AI for contact centers

Deliver 24/7 customer support with bots for CX sustainability

Gc and idc logos v3

Offering 24/7 customer support is more important than ever during these uncertain times. Fortunately, artificial intelligence (AI) is poised to transform contact centers and improve customer experience — without compromising operational efficiency. It can empower leading brands to make customers happier and increase customer lifetime value.

Yet, a 2020 IDC research shows that less than 10% of customer interactions occur via virtual agents. And only one-third of the organizations surveyed were prepared to enable a remote contact center workforce when COVID-19 stay-at-home mandates were issued.

Join IDC, Google Cloud and Genesys panelists as they discuss how AI-powered chatbots and voicebots can achieve customer experience sustainability. You’ll also learn:

  • Top three actions to improve the customer experience
  • Primary business drivers and measured benefits of using AI for customer service
  • How contact center AI can increase customer satisfaction, empower human agents and augment business insights

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

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from the digital events team here at Genesys, and let me be

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the first to welcome you all to today’s webcast, Unlock

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the Transformative Power of AI for Contact Centers. As we

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always do, we’re going to start off with a couple

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of brief housekeeping items. First off, if you experience any

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

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browser and make sure that it’s up to date to

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support HTML5, as it usually fixes any console issues. Also,

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if you’re having trouble seeing any of the windows, either

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the slide window or the webcam window, you can enlarge

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that window by dragging one of the corners and enlarging

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them in real time. And please note that this is an interactive

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experience between you and our three panelists today. Feel free

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to throw questions into the Q&A window, and we’ll answer as

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many as we can at the end of our presentation

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today. However, sometimes as it does, if time gets away

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from us and we aren’t able to read your question

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aloud during our live Q& A, will actually follow up

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with you via email within the next few business days.

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And also note, if you have to jump early or

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for any reason you feel like you’re running out of

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time, don’t worry. We actually are recording this. You’ll receive

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an on- demand recording link via ON24 within the next few

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business days. So just be on the lookout for that. And also,

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we have a resource list here below the Q&A window. You

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can actually access those resources at any time during the

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webcast. It will open up in a new tab in

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your browser, and they won’t take you away from the

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webcast. But these resources expand on today’s topic of AI

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in Contact Centers. Also, we encourage you to participate in

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our brief survey. That’s the last icon on the left of

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your widget bar. We’d love to collect your feedback on

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today’s presentation so that we can tailor these webcasts to

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what you want to hear in the future. And as I

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always say, short and sweet. Today, we have three excellent

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presenters excited to discuss how AI- powered chatbots and voicebots can

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achieve customer experience sustainability. With that being said, I’m actually

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going to hand things off to our moderator today, Chris

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Connolly. Chris, the floor is yours. Thanks, Josh. And good

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morning, good afternoon, good evening, wherever you are in the world. We’ve got a pretty exciting agenda that we’re

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going to share with you today. But first, I want

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to introduce some of our speakers. You might have met them before. If not, you’re going to hear from them today. First

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is, Antony Passemard. He’s the Head of Applied Conversational AI at

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Google, looking after Contact Center AI. He’s presented with us before, and we’re

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very lucky to have him back today to talk about some the innovations and

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evolutions of this space. Including that is, Ritu Jyoti, who’s the Vice President of Artificial Research

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at IDC. Welcome, both of you. And myself, Chris Connolly,

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Vice President of Product Marketing here at Genesys. If we’re looking at

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our agenda, we’re going to look at some Insights from IDC, what the

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world looks like today, and really looking at how we

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unlock the power of artificial intelligence, and apply that in in

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Contact Center specifically. We’re also going to have a panel discussion and please feel

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free to ask us questions now, or if you’ve got something

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that’s on your mind, put under the Q& A window, because

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we’re going to get to it very, very soon. We

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also want to talk about some of the things that you can do right away

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to improve your customer experience using artificial intelligence. And we’re going to

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recap on some of the key takeaways from the research, and some of the

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experience from Genesys and Google in implementing this technology in the

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real world. And then lastly, we want to hear from

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you. So, if there are any questions, please, as Josh

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said, put them in the Q&A window, we will get to them live. So with that, thanks for kicking off. And I’m going to head over to Ritu Jyoti from IDC. Welcome, Ritu. Thank you, Chris. Hello, everyone. Pleasure to be

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a part of this webinar today. Before we delve into some cool

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insights that we got from a joint study we did

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together earlier this year in May 2020, let me first level

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set the stage here. We all know today that customers no

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longer base their loyalty on price or product. Instead, they

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stay loyal with companies due to the experience they receive. Customer

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experience has fast become a top priority for businesses, and

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often 24 by seven customer support is more important than ever,

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especially during these uncertain times. As per our research across

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most industries, brands of all sizes have started to push

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heavily towards increased automation in their customer service, as well as

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employee self- service, sales, marketing, human resources, IT help desk. You might wonder, “What has really changed?”

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Most of these brands are now looking for conversational AI as one

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of the key drivers for that automation. And as conversational AI allows

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brands to use natural language processing and machine learning- based

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tools, to support both their customers and the agents who support these

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customers. And the conversational AI chatbots and voicebots, they’re more sophisticated

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these days. They incorporate bias and explainability, and exploit natural language for general

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question and answer capability. What we did earlier this year

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is that we run a joint study, and the study was

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focused on understanding what is the value of Contact Center AI. And for

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the suggested study, we wanted to validate the benefits of

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Contact Center AI in enterprises, what’s the business ROI, What

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is the improved customer experience metric? How does it help the customer service agent

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efficiency? And before I get into the stack, let me just

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quickly walk you through some of the demographics of this study.

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We run a global study, it constitutes about 407 organizations worldwide. 50% were from

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U. S. and Canada, 25% they’re from France and UK, and the

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remaining 26% were from Australia, India, Philippines, and Japan. We had

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a good mix of the industry. We had folks from

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financial institutions, insurance, telecommunications, and the rest of the industry.

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We made sure that these were polling or surveying, so folks

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who had the decision of quality. We had a good

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balance of companies of different sizes, as well as different

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types of customer handling formats. The people who were having

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internal Contact Center, or have an external Contact Center, as well as some

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internal customer service functions. And they had a broad range

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of respondents from different levels of customer service agents, starting

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from as small as 20 customer service agents. With that, let me, before

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level- set, that what we’re seeing in terms of what

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is the three AI- driven competence that are transforming the Contact Center

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today? There are three competence. The first is the virtual agent, the

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second is the Agent Assist, and the third is the Insight. Virtual agent is basically a platform for creating voicebot

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and chatbot to automate customer interactions with voice or text,

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and the conversation to a live agent when the bot

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is unable to help a customer. So that’s the first one. The

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second one is that an Agent Assist. It is a

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platform that integrates into the agent desktop, which uses AI to

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augment agent interactions with customers in the real time, and

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provide tone- by- tone guidance not to of relevant knowledge

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bases. It really, really help the agent become more efficient, it’s augmentation of AI capability.

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And the third one is Insight. It’s basically a module

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which uses natural language processing to identify the call center. ”

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Why did the customer call? What were the call drivers? What

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was the sentiment?” And this helps the Contact Center managers learn about

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customer interaction, and improve call outcomes. How has CAST 2020 changed customer service?

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We all know what’s going on in the industry today.

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And when we asked them what were the top difficulties that they were facing adjusting to

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the new stay at home mandate, not as surprise, but 39% of the

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respondents, the demographics that I just shared with you, they

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shared that they had higher than usual call volumes, and

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we all know the reasons why. But in addition to

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that, the situation that really compounded the problem was that there

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was fewer agents available. 43% of them reported that they

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had a few number of agents available to this part of this

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higher than usual call volumes. Partly some of them were

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reporting sick, or they were not able to respond to the work because of the changing dynamics, as well as, because of a

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more than usual call volumes. They did not even prepare to that level of number of ratio of the agents needed to support that kind of call volume. This is specifically interesting. With

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that, I’m going to. Yeah, Chris. these are really interesting stats.

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2020 has been an interesting year for all of us.

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So, if we look at those stats that Ritu just presented, we’re

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curious about you, what are you seeing in your world, whether it’s in your

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Contact Center, if you operate one or in your clients,

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if you’re a partner of ours or you’re helping operate

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a Contact Center? And are you seeing the increase in

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chat and call volumes, or are you seeing a decline? I’ll give

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that a few more seconds to get a few more

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responses. But I’m particularly curious to see what results we

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get off the back ends here. I’ll give that one

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or two more seconds. All right. Well, Ritu you couldn’t

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be more right. Your research confirms exactly what our audience

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is seeing as well, with I guess unsurprisingly 81% are saying, ” Have you

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seen an increase in call and chat volumes in 2020?” And

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that presents unique business challenges. How do you handle that

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increase in call volume, or interaction volume, or chat volume,

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just with the same resources that you have at the moment? So Ritu, leaving on a little bit here in terms of being prepared for that stay at home mandate

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that a lot of us have seen, what have you seen in the research

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in financial services? Yeah, I think the most important point to level set here is that

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the dynamics are changing dramatically. The survey we conducted was

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in the month of May 2020, and three months has changed quite a

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lot of the situation, but we caught this into right

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in the middle of COVID situation. And when the stay at

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home mandates are down, not a surprise to us, but

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I’m very well prepared with only one respondent. And I’m not

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surprised to see the other part of the results that

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I share this in the slide is that, the financial services in

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U. S. organizations were the best prepared. So you might

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sit and think, ” What led to this?” And IDC as

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a research firm, we spend a lot of time advising our end

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users on what they really need to do. The correlation

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here is that the organizations who were born digitally transformed,

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they’re higher stages of maturity, of digital transformation, they were

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definitely well prepared. And you can see that there was a direct correlation between

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that, and even the financial organization, they have been embarking

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on their journey much longer and earlier. And that’s why

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you could see that correlation. But now I will share in one of

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the upcoming slides here, that what the organizations are doing to

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be better prepared next time. And that’s more important. I would

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like to focus on the forward looking approach and I’ll

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walk you through and back in a couple of slides.

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Awesome. Well- With that if I’m going to… Yeah. Sorry, please go ahead. The next one

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is basically, we had asked them that the process of customer

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interactions that is happening through this newer channels. And again,

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I would like to reiterate on the point here that,

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this is the average that I’m presenting here. So, there

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are a couple of organizations who might be better prepared

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than the other, and the volume and the my team, but this is the mean,

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and the median value. Good sharing here. So, there could

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be a possibility that the U. S. organizations, if I presented

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the data cut just so that it could have been a little bit different. But

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not surprisingly, if I look at it from the mean and median

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perspective, it’s very small percentage. And again, factor it that this is

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May, 2020 was happening to virtual agents. This number, I suspect

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if I’d understand it today, it would be a little

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bit higher than this, but it’s a small percentage. Yeah,

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exactly. And the agents, the voicebots and chatbots percentage will

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also dramatically change. Most of them they’re originally doing idea. But before

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I move on to the next slide, another very interesting

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stat that I want to share here is that we

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spend a lot of time talking to the end users

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and don’t worry, there’s still research as well, that what

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is really coming up and shaping up in addition to just

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the voicebots and chatbots is the computer vision effect to it. The computer vision

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has become more mature with image recognition achieving significant improvements.

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Thanks to deep learning techniques, computer vision, and CRM is

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very early stage. And it’s far from being widely adopted.

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But if I look back, sit back and think that the rate

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at which things are changing, it will be relevant across the

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entire customer journey. It will be a through force multiplier for

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adding more essential insights for customer upsells and cross sells.

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So, that is something that we all need to look at and watch,

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and that, that’s going to shape up big time in the near future. to that next slide is, there’s an obvious question. We’ve seen voice agents and chat agents

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in this case really low at only 8%. And I guess Antony

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I’ll put this question to you. What do you see?

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What do you think this is so low at this

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point in time? Well, I can’t really blame customers on a low

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adoption of virtual agents for voice and chat. If you

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look historically, you look back two or three years ago,

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either you have an IVR that’s forces you down a

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tree, or the chats are really just about routing you

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to the right agent. I can’t say that the bots on

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voice or chat were really good to be mild in

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my assessment. But that is cheapest thing. The technology that

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enables us to deliver very high quality bots, which means

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they understand where you’re saying, they can drive the conversation,

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they can answer questions, do backend fulfillment, actually deliver value

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to a customer. This is more new, in fact, in

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the last two years I would say, and really taking off.

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So on that, 8% makes sense what we’re seeing across

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customers. Some don’t use those channels at all. The ones

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who do use them are around 15 ish, 20% of

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no volume. So, that 8% average doesn’t surprise me. It’s very

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in line with our customers. And that low adoption, if

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you look a year from now, it’s going to be completely different

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graph. Yeah, I totally agree. It might be low today,

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but this is where the growth factor is. And we’re

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expecting to see the same customer base in Genesys. We constantly inundated with inquiries

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on, ” How do we apply the virtualization technology?” Ritu, I guess, again,

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coming back to current times and the research that you’ve

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seen, how will the Contact Centers adapt in to the modern world and the things that are going on today? not an easy thing, but I always

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love to say this, ” There’s no choice.” Customers have to do

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this in order to be innovative, providing, improving on the

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customer experience, but at the same time, not compromising on

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the operational efficiency. And it’s a great balance that customers need

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to do. So, it all starts with me. This is universal

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across all AI initiatives, but it’s very, very prevalent here. Culture, that’s

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transforming the culture, the customer insights is democratized. Every employee

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becomes a change agent for the customer. I cannot emphasize

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this more because in the case of AI sometimes, people are

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a little bit because it’s also another… It’s just getting the

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comfort level, trusting it. And there are lots of advancements

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happening in that area. But once you have that culture, you’re

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willing to understand where the customer needs are. This is

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really going to be day and night for the customer

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experience. And at the same time, you will be playing

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a very active role in building, working with the tools,

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and technologies, and the supplies, and the offerings to meet

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and improve your AI systems as well. So that’s one.

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The second is that, gone are the days when people

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are doing metrics of measurement in isolation. Here people are

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trying to bring them together. There are AI, KPIs, and

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AI metrics have globally aligned with how they’re measuring their customer

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experience metrics. So, they’re both evolving so that there’s huge amount of

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emphasis on improving the experience, and improving the measurements and

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aligning them together. And the third and the last thing is

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that, there’s a huge amount of emphasis on human efficiency.

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And there was a lot of debate going on for some time, that

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AI is going to take over the human jobs and there

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was no experience with this in the foreseeable future. We

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see that it’s making us much more efficient. It is

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helping us to be more empathetic. It is giving us

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real time guidance. So these are the three important things. Of course there are

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other parts of it, but these are that we are

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seeing in customers, how they’re doing what they’re doing to

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adapt to the newer customer Contact Center scenarios today. Awesome. And- point,

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Ritu. Just one point here, Ritu. The survey earlier with

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80 plus percent of people saying they see their volume

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going up, I think that’s where really AI can help.

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Is to avoid having to increase your Contact Center resources

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by 80%, to answer that need. That’s really where we’re seeing

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actually a lot of usage of AI for Contact Centers,

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is to kind of tamper that growth of volume and

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make sure you have operational efficiency across your Contact Center

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without adding more people. It’s not about a reduction of

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people. That’s not what we’ve seen. Exactly. It’s not about replacing

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human beings. It’s making them more efficient, so they address the changing

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dynamics of the industry. Because the volumes will definitely increase, but

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we do not, and cannot adding more and more people to meet their demands.

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Exactly. And so, our audience can maybe discern what other

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customers are doing in the station. And that’s a great example. It’s

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not about replacing people, and maybe that’s one of the

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big business drivers, just handling more. Are you seeing… And

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I guess this question is for both of you. Are you seeing

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other business drivers in the application of artificial intelligence that

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are not correlated, that are maybe not volume related, but other benefits of using

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applied, and machine learning, and AI to deliver a new experience? Yeah, absolutely.

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I think- Yeah, go ahead, Ritu. business driver, what we’re seeing is,

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now the customer will come and first think about operational efficiency, cost reduction are

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the first thing they have in mind. But the reality is,

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when you start doing a Contact Center and put Contact

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Center AI in your Contact Center, you’re really transforming your

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customer experience across all channels. And the business drivers to

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provide a coherent experience when you’re on chat, when you’re

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on voice, when you’re on your web, or your mobile

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app, where the engine behind it, the AI behind is

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able to manage across those channels, switch channels, understand what was

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said, the context, the past history, et cetera, and provide

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you a unique voice for the company to that customer

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or that user, if talk about organizations. I think that

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the business driver is really having that coherence across the

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board, and having a very wonderful experience, no matter the

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channel, no matter the choice that the end user or customer is

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making, they get that experience across all channels in a

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very unified way. I think that’s a big business driver.

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Agree, agree. And it is also reflected in our study

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here, which I’m just going to share the chart, with some

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of the facts on there. But for very first experience we’re all talking about it, but

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because of usage of these as the, it’s not just

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the consistent experience, which is very, very critical, but it’s

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also passive into issue. Because enriching the human agent with

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the Insight in real time, it provides that timely response and a

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more intelligent response for lack of a better word here,

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is really, really being consummated with operational efficiency, consistency, and

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experience. But also getting the fastest response in the fastest time,

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in the more intelligent way, this is what AI is

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known for. It can do volumes and volumes of data,

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to pattern recognition, get Insights, and this advances in natural

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language processing can stop through tons and tons of data, and

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get you the right Insight at the right time. That’s

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another very huge advantage. So, it’s overall including bringing a good … I’m

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IDC research. We also say that there’s a huge correlation between

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customer experience and employee experience. And by usage of AI

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technology, you’re not just making the customer happy, but you’re

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also making the employee happier, employee more efficient. And that has

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a direct correlation on the customer experience. When many times we

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all come to work and if we’re unhappy more, we’re

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excited to serve our customers better. So there’s a direct correlation there, which

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the advancements in AI technology is helping, and that’s why the

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human agent efficiency, and human needs, and empowerment, and augmentation is so,

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so fundamental to the successful adoption of this. And people

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can’t go to the other parts of the chart, but I also

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emphasize that empathy… Because you can understand the… Remember in one of

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the slides before, I was talking about where you could actually understand the

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sentiment. You can understand the rational of why people are

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calling you, and you could react in that particular situation in a much more empathetic

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way, because then you have that kind of intelligence real time. It’s way

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more to the agents. This is very, very much aligned

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to what we are trying to think from a bigger picture

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than the survey insights that also align to that. Before

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I move on to the next slide, I just wanted

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to say that I think Tracy you asked this question, “What are the customers doing?” Of course their primary work, is one of

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the top reactions, but they’d also feed up a little

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bit of more question on the survey that because of

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this crisis… This is not a one time, this could happen again.

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How do you make yourself more resilient organization? So, what

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are the new IT investments that this experience has taught the customers and

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what they’re doing it? The three things that you see

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here is that, in the past, sometimes people sit and decide whether they want to be on the public cloud

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scenario. This has really accelerated that, and people are seriously

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looking into it. The second is that the maturity that Antony also

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mentioned, and I also mentioned, and Chris, you mentioned at

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the start of the presentation is that the conversational AI

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technologies have improved so much in leaps and bounds in

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the last 12 to 18 months. And because of that,

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there is a sophistication in the response. It’s not just, ”

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Rule says yes or no.” There’s a significant amount of depth

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in the answering of the questions and answers. So people are getting

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more comfortable with the chatbot and also an omni experience.

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Whether they’re working for a live chat, or for this chat, or

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through an IVR, everything they want that sense of experience. So, there’s

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much more willingness to embrace it. And that is not

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the least, there are other factors, but digitization. We have been

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00:25:32,980 –> 00:25:36,530
saying this for a long time that accelerated. Two years

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of digital transformation has happened in two months and it

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has accelerated the digital transformation. Increased digital digitization in the

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customer environment is really the investments that people are going to make. IDC has predicted

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that while we are in grim times, the investments in AI

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and the investments embracing of AI technology to make them

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more resilient, and this in the next new normal is something that AI

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will play a very significant part in that. Yeah, absolutely.

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And I want to maybe echo something that you said there, about

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providing empathetic customer experiences. And now more than ever, I

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feel like we need to have empathy for our fellow

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human. We’re all going through something we know this is a generational thing. Something

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we haven’t seen in many generations. So, what we’re experiencing

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now is different, but crisis creates opportunity. And we’re also

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seeing that this is actually being a bit of a

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non dialogue, as you said, Ritu, to some of the

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applications of AI. I want to talk a little bit about how we’re seeing this

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technology start to be applied in three major use cases. I’m going to echo

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00:26:55,220 –> 00:26:59,500
back to something which you said earlier with our voicebots, chatbots, and Agent

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Assist. And let me just take you through very quickly on

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what those are, and how they might be applied and

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why you might apply them in your organization. So the

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first if I could really summarize a lot of that

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upfront processing, is voicebots. And we’re at a point now

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with the technology with natural language understanding the speaker recognition, a

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lot of that powered by Google to really understand phrases

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better than we ever had before, and at least a

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customer intents, so that we can have a much more data experience. And if

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you’ve been around the industry for any length of time

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00:27:36,670 –> 00:27:40,610
like myself, that in the two thousands, it was a single

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00:27:40,610 –> 00:27:43,450
word that was being said, and it couldn’t detect that

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piece of recognition. And then we had to build these complicated

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speech, IVRs to deliver an experience. And really today what we’re

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seeing is your IVR is a voicebot. And that’s the

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evolution of the Contact Center technology, artificial or machine learning

401
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being applied in our space. Which brings the question, ” Why would you do

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that? What is better than my IVR today?” And it really

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is that natural language processing that allows your customers to

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00:28:15,100 –> 00:28:18,910
speak in plain speak, and to get to an outcome

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much more quickly, which ultimately improves their experience. But it’s

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not just that upfront or self service experience that benefits

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from a voicebot, it also means that by the time that

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customer gets to an agent, the agent has a better

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understanding of what’s going on as well. Because we’re using

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00:28:39,150 –> 00:28:43,630
CCAI in this case to intent, fill in the slots,

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and really have a more complete picture of that interaction. And again,

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00:28:48,380 –> 00:28:52,420
overall, it rolls into that improved customer experience. We’re going to go into

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a little bit more about the Genesys and Google partnership in just a

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few seconds. The other major use case that we see

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is chatbots. And I want to really say from the outset

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chatbots doesn’t mean web chat. Chatbots can be applied on

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00:29:06,850 –> 00:29:10,110
any textual medium that Genesys AI is able to route

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using the very same technology, natural language processing that you see in voice,

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as you see in text, and applying that to WhatsApp, WeChat, Apple

420
00:29:19,820 –> 00:29:23,450
Business Chat, of course Web Messaging as well when I

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synchronize messaging. All of that same technology, because Genesys can

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00:29:28,260 –> 00:29:31,860
route that, and we’ve connected that with Google Contact Center AI.

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You can take advantage of the machine learning that’s being

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00:29:35,340 –> 00:29:46,110
applied there. Very similar experiences that we’re seeing in the voicebot world, that in the chatbot world, that maybe with some richer experiences, depending on

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00:29:46,110 –> 00:29:48,560
the medium that they’re operating in. And what I mean

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00:29:48,560 –> 00:29:52,660
by that is, with asynchronous media or Web Messaging, you’ve

427
00:29:52,660 –> 00:29:57,780
got the opportunity to actually insert, and images, and mix

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00:29:57,780 –> 00:30:00,940
that with text. And the bot can actually do some pretty sophisticated

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00:30:02,130 –> 00:30:07,040
things in that, including the complete self- service interactions, but

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00:30:07,040 –> 00:30:10,540
be on your orchestrated platform that brings in the right

431
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technology at the right time, and when it does get to

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an agent that entire conversation is seen, and you can

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actually act on it in real time. And then lastly, and

434
00:30:22,330 –> 00:30:24,800
this is probably the one that I think is the

435
00:30:24,800 –> 00:30:28,510
most leading edge, but one that we probably expect to

436
00:30:28,510 –> 00:30:34,230
see more and more adoption as we go forward, which is Agent

437
00:30:34,230 –> 00:30:38,190
Assist. An Agent Assist, is what it says. It’s providing

438
00:30:38,560 –> 00:30:43,600
artificial intelligence augmentation with the agent in the Contact Center to provide

439
00:30:43,600 –> 00:30:48,090
a better experience. And there’s been, again, those leaps and bounds of

440
00:30:48,340 –> 00:30:53,140
how we integrate with this technology from an agent experience,

441
00:30:53,140 –> 00:30:56,350
from a desktop experience, from a voice experience, so that

442
00:30:56,420 –> 00:30:59,710
we’re providing insights to the agent and surfing through them

443
00:30:59,920 –> 00:31:04,050
with knowledge in real time, and really having a very

444
00:31:04,410 –> 00:31:10,490
augmented conversation. I want to pause on that one because, Antony, this is absolutely in your domain

445
00:31:10,490 –> 00:31:17,240
as something that you talk about every day. So, how is the technology at

446
00:31:17,240 –> 00:31:20,690
Google, that we’ve talked about in voicebots and chatbots being applied

447
00:31:20,690 –> 00:31:26,930
in Agent Assist? An Agent Assist as you said, is

448
00:31:26,930 –> 00:31:29,270
a little newer in it’s technology, but it’s really a

449
00:31:29,270 –> 00:31:33,590
breakthrough that’s been enabled by the advent of voice and speech

450
00:31:33,590 –> 00:31:39,230
to text in particular, speech to text technologies. Google has a

451
00:31:39,230 –> 00:31:44,090
long history of trying to understand speech either through understanding

452
00:31:44,090 –> 00:31:46,700
YouTube videos, to be able to search them and remove

453
00:31:46,700 –> 00:31:50,810
hate speech in YouTube videos. Either through a Google Home

454
00:31:50,810 –> 00:31:53,990
Assistant, and understand all the requests from users, that’s millions

455
00:31:53,990 –> 00:31:59,770
and millions of requests every year. And also through transcription

456
00:31:59,770 –> 00:32:03,110
of voice messages on phone. You have Google voice and

457
00:32:03,110 –> 00:32:07,430
you can do your voicemail transcription. So all that technology…

458
00:32:07,430 –> 00:32:10,310
And voice search, obviously. So, Google has put a lot

459
00:32:10,310 –> 00:32:13,740
of effort in R& D into understanding voice, and thanks

460
00:32:13,740 –> 00:32:17,900
to that, we can now propose real time, voice transcription

461
00:32:18,280 –> 00:32:21,650
and understanding of a conversation between an agent and a

462
00:32:21,650 –> 00:32:26,810
user, and really provide in real time, the right suggestions,

463
00:32:26,810 –> 00:32:30,710
the right responses, the right document, the right flows that

464
00:32:30,770 –> 00:32:34,130
the agent needs to do their job faster. And that’s

465
00:32:34,130 –> 00:32:37,240
really changing the game for agents. Ritu was talking about

466
00:32:37,240 –> 00:32:39,860
agent satisfaction, we’re seeing that go up. We’re seeing average

467
00:32:39,860 –> 00:32:43,140
handling time go down, we’re seeing call summary going down

468
00:32:43,280 –> 00:32:46,070
in terms of how much time they spend typing after

469
00:32:46,070 –> 00:32:49,480
the call, the call disposition, we see first call resolution

470
00:32:49,620 –> 00:32:53,560
improving. On and on, the Agent Assist is really now the

471
00:32:55,120 –> 00:33:01,080
little coach or assistive technology for human agents in the Contact

472
00:33:01,080 –> 00:33:07,580
Center, enabling new experiences for the end user. Absolutely. So I

473
00:33:07,580 –> 00:33:10,410
want to come to another poll question in interest of

474
00:33:10,410 –> 00:33:15,740
time. From your perspective, what are you looking to implement? Any

475
00:33:15,740 –> 00:33:19,880
of these technologies, voicebots or chatbots specifically in the next three

476
00:33:19,880 –> 00:33:25,980
to six months, or maybe six to 12 months? Is that a

477
00:33:25,980 –> 00:33:28,750
new thing? Is it something that you want to act on now? Is it something that

478
00:33:29,350 –> 00:33:32,240
is more future- looking for you? I’m guessing the fact

479
00:33:32,240 –> 00:33:33,940
that you’ve turned up to this webinar today that means

480
00:33:34,860 –> 00:33:38,220
all interest to you, but we want to get a bit of a gauge to

481
00:33:38,220 –> 00:33:42,800
see if that deals with our research on how people

482
00:33:42,800 –> 00:33:45,620
are starting to adopt this technology. I’ll give that a

483
00:33:45,620 –> 00:33:50,160
few more seconds. Eyeballs on screen, and then I’ll go

484
00:33:50,160 –> 00:33:52,650
to the poll results. Do we want to take a guess

485
00:33:52,650 –> 00:33:58,620
anyone on where people are going to land? All right. Yes. Three

486
00:33:58,620 –> 00:34:02,170
to six months, in six to 12? Okay, I think

487
00:34:02,170 –> 00:34:07,880
the call out here is actually the people that said no, that are in the minority. So, the majority-

488
00:34:08,470 –> 00:34:11,020
Maybe because they already have something. I don’t know. Maybe,

489
00:34:11,100 –> 00:34:14,920
no. Because we saw 8% of people already have it. So maybe the nos are people

490
00:34:14,920 –> 00:34:18,720
who have something in place. now we should have put that option in. We should

491
00:34:18,720 –> 00:34:20,760
have said” I’ve already got it.” That no would have been on 1%.

492
00:34:24,870 –> 00:34:28,300
That’s wonderful. I did see a question that came through

493
00:34:28,300 –> 00:34:32,850
as we’re going, here from Rick. And Rick, I hope

494
00:34:32,850 –> 00:34:36,780
I’m going to touch on some of your question, and the question is, ”

495
00:34:36,780 –> 00:34:42,360
How does Google and Genesys differentiate and add some capability

496
00:34:42,360 –> 00:34:46,150
and value?” I want to talk about that, but also when we get

497
00:34:46,150 –> 00:34:49,550
into Q&A, I’m going to come back to your question and answer it in a bit more detail as well.

498
00:34:50,320 –> 00:34:52,870
When we think about Google and Genesys, these are two industry

499
00:34:52,870 –> 00:34:57,350
leaders. Genesys being the only channel contacted a platform that really allows you

500
00:34:57,350 –> 00:35:02,160
to do journey orchestration, bringing multiple technologies from different vendors

501
00:35:02,380 –> 00:35:05,760
and really orchestrate all of those together in a cohesive

502
00:35:05,820 –> 00:35:09,650
manner, to deliver the very best experience that is out

503
00:35:09,650 –> 00:35:13,240
there. But we can’t do it alone. And when we’ve looked across

504
00:35:13,910 –> 00:35:18,280
the marketplace, hope Google has done the same, it’s really providing

505
00:35:18,280 –> 00:35:22,790
the best technology to our customers that integrates really, really neatly

506
00:35:23,010 –> 00:35:28,280
to deliver artificial intelligence capability inside the platform that maybe

507
00:35:28,280 –> 00:35:34,280
you have today, or the platform that you think of moving to in the future. What that means is that

508
00:35:34,710 –> 00:35:38,750
if you look at artificial intelligence technology in isolation, you

509
00:35:38,750 –> 00:35:41,070
can do a lot. You can use APIs, you can

510
00:35:41,070 –> 00:35:44,320
build what you need to build, and that might take

511
00:35:44,320 –> 00:35:49,930
time, but there’s a depth of capability within Genesys in our platform, in the

512
00:35:49,930 –> 00:35:54,480
cloud, but also in our multicloud platform for everyone else,

513
00:35:54,760 –> 00:35:58,540
that allows you to plug in these different technology components,

514
00:35:58,540 –> 00:36:03,200
and take advantage of all of the routing of the orchestration services, all of

515
00:36:03,700 –> 00:36:08,610
the reporting, the analytics, the entering conversation view, the widgets,

516
00:36:08,610 –> 00:36:13,220
the desktop. That complete Contact Center package that you have,

517
00:36:13,460 –> 00:36:18,750
strapped on with artificial intelligence technology as well. So Antony,

518
00:36:19,040 –> 00:36:22,540
I know this is a long running partnership for us, at

519
00:36:22,540 –> 00:36:25,630
least from 2018. Do you want to add any commentary

520
00:36:25,630 –> 00:36:31,670
around how Google Cloud and Genesys put together? This is

521
00:36:31,670 –> 00:36:33,820
a very exciting partnership we’ve been at. I think you were

522
00:36:33,820 –> 00:36:36,770
one of the very, very first partners that we talked

523
00:36:36,770 –> 00:36:40,360
to, and we made that partnership. The goal here for

524
00:36:40,360 –> 00:36:45,230
us was really to also leverage, not only the innovation

525
00:36:45,230 –> 00:36:48,480
that Genesys is bringing to the market, but also leverage

526
00:36:49,150 –> 00:36:51,960
a lot of customers that have Contact Centers with Genesys

527
00:36:51,960 –> 00:36:55,220
in place and wanted to bring more AI, more capabilities.

528
00:36:55,250 –> 00:36:59,160
And we’re seeing the opportunity here to avoid a rip

529
00:36:59,160 –> 00:37:04,240
and replace, of your implementation, and really upgrade whatever you

530
00:37:04,240 –> 00:37:07,830
had from Genesys. Genesys upgrading a lot of their own

531
00:37:07,830 –> 00:37:11,210
capabilities into their customer experience, but I think the Agent

532
00:37:11,210 –> 00:37:15,960
Assist, the virtual agent, and those capabilities are really things

533
00:37:15,960 –> 00:37:20,730
that we feel strongly about our capabilities, and having that

534
00:37:20,730 –> 00:37:25,770
as part of the Genesys platform is really powerful. I feel

535
00:37:25,770 –> 00:37:30,410
so super exciting to have this in place. I’m going

536
00:37:30,410 –> 00:37:33,960
to come to another question that’s being asked in real time here

537
00:37:33,960 –> 00:37:36,940
about some of the joint customer use cases that we

538
00:37:36,940 –> 00:37:41,120
see from Google and Genesys. And I know, just by coincidence,

539
00:37:41,120 –> 00:37:45,030
that is the very next slide that Antony is going to talk to, but I’ll give

540
00:37:45,030 –> 00:37:47,650
you an anecdotal example of a customer that’s gone live

541
00:37:47,650 –> 00:37:53,220
with a Google Contacts Center AI and Genesys recently, where they reduced 26

542
00:37:53,390 –> 00:38:00,010
speech IVR applications down to one. And if you ever

543
00:38:00,010 –> 00:38:03,620
had to operate these or build these, the significance of

544
00:38:03,720 –> 00:38:06,760
every single one of those applications. We’re able to use Dialogflow

545
00:38:06,760 –> 00:38:12,060
in this instance with Genesys new gauge. This customer, which was a telecommunications customer,

546
00:38:13,980 –> 00:38:19,440
was able to greatly reduce their managed applications down to

547
00:38:19,620 –> 00:38:24,680
essentially one application that’s orchestrated by Genesys. And the way

548
00:38:24,680 –> 00:38:28,640
they did that, this is using some of the technologies that are on this slide, which

549
00:38:28,690 –> 00:38:31,250
Antony, I might hand over to you to talk about in a bit more detail. I

550
00:38:31,250 –> 00:38:36,680
know I will add something on that telecom customer. It was

551
00:38:36,680 –> 00:38:39,930
not in English, which is also one of the power

552
00:38:39,930 –> 00:38:43,700
the Google platform, is we support quite a few languages for

553
00:38:43,940 –> 00:38:46,950
CCAI, and this is a very big success story that

554
00:38:46,950 –> 00:38:51,840
was not only for English speakers. I’ll just maybe do

555
00:38:51,840 –> 00:38:59,000
that. So, yes, as Chris mentioned, we have three main

556
00:38:59,000 –> 00:39:02,650
products as part of our CCAI portfolio. To build virtual

557
00:39:02,650 –> 00:39:05,510
agents, there’s is a product called Dialogflow, we’ll talk a little

558
00:39:05,510 –> 00:39:10,770
bit more about that. Our Agent Assist capability named Agent

559
00:39:10,770 –> 00:39:14,460
Assist, very creative, will be there to help your agents

560
00:39:14,750 –> 00:39:18,330
both on chat and voice channels. And our newborn Insights

561
00:39:18,490 –> 00:39:23,300
Platform just launched now factually yesterday, and will go GA

562
00:39:23,300 –> 00:39:28,940
pretty quickly. After that there’s looking all of the data that’s

563
00:39:28,940 –> 00:39:32,340
coming in, in your Contact Center, on chat and voice

564
00:39:32,340 –> 00:39:36,850
channel and using all that data to help you derive

565
00:39:36,950 –> 00:39:43,960
insights, trends, search, help QA managers and training, review calls

566
00:39:44,510 –> 00:39:47,250
and really drive insight sentiment, et cetera, is all in

567
00:39:47,250 –> 00:39:50,020
the Insights Platform. So, those are the three products that you

568
00:39:50,020 –> 00:39:53,560
will see as part of the portfolio called TCI at

569
00:39:53,560 –> 00:40:03,460
Google, and they’re available on the Genesys platform. is putting the elephant on

570
00:40:03,460 –> 00:40:06,860
the table, so to speak. Which is my way of

571
00:40:07,110 –> 00:40:10,470
saying, address the elephant in the room. Have you tried

572
00:40:10,470 –> 00:40:14,020
natural language processing with bots? And if so, which are

573
00:40:14,020 –> 00:40:22,790
the ones that you’ve tried? Because there are multiple choices out there that you can work with, and maybe the answer is no, you’re not there yet, and you’re maybe

574
00:40:22,790 –> 00:40:26,930
evaluating, or you’ve experimented, or maybe you already live. So

575
00:40:28,080 –> 00:40:30,190
I’ll give that a few seconds. The options here are

576
00:40:30,190 –> 00:40:36,120
Amazon Lex, Google Dialogflow, Microsoft LUIS, IBM’s Watson, something else?

577
00:40:36,120 –> 00:40:40,760
Maybe homegrown, or a third party, or you haven’t tried

578
00:40:40,760 –> 00:40:47,650
any of them. I know this is probably a multiple choice answer, but let’s see where people land. And we’ve got to put in an even

579
00:40:47,650 –> 00:40:58,560
split. Oh, that’s. I try which is amazing. But also

580
00:40:58,560 –> 00:41:01,710
the call out here for me is the majority, and

581
00:41:01,830 –> 00:41:06,700
the majority haven’t tried natural language understanding yet. And really,

582
00:41:06,880 –> 00:41:09,640
I would encourage you to maybe look at it. This is what we’re talking

583
00:41:09,640 –> 00:41:13,810
about. It’s the fundamental technology behind all of these cases that

584
00:41:13,810 –> 00:41:19,850
we’re talking about today. It is a good time to

585
00:41:19,850 –> 00:41:24,550
try because this slide actually is relevant too. You probably haven’t

586
00:41:24,550 –> 00:41:28,420
tried because you’ve had very bad experience calling Contact Centers,

587
00:41:29,610 –> 00:41:33,690
and you really think those things don’t work. In terms of NLU,

588
00:41:36,070 –> 00:41:37,980
Google is really… It’s quite a lot of paper. And actually

589
00:41:38,050 –> 00:41:41,700
Google even open sourced a very large transformer called BERT,

590
00:41:42,240 –> 00:41:45,640
which is transforming the NLU space right now. And Google

591
00:41:45,640 –> 00:41:48,500
has its own version of BERT, which uses a lot

592
00:41:48,500 –> 00:41:55,130
more data. But that’s where NLU has drastically changed in,

593
00:41:55,240 –> 00:42:01,070
let’s say, 12 to 18 months. Drastically change, entities traction, intent detection,

594
00:42:01,470 –> 00:42:04,240
the courtesy of intent detection and matching is much, much

595
00:42:04,240 –> 00:42:06,440
higher than what you would get in two or three

596
00:42:06,440 –> 00:42:08,570
years ago. So I think it’s a good time to

597
00:42:08,570 –> 00:42:12,630
test it out. Obviously Google, as I mentioned earlier, is

598
00:42:14,000 –> 00:42:19,420
understanding speech. And NLU for Google, as a whole Google

599
00:42:19,420 –> 00:42:23,210
search even, is a core technology that is needed for

600
00:42:23,210 –> 00:42:28,440
Google to function. So the investment we’re making there is

601
00:42:28,510 –> 00:42:31,230
obviously massive because it’s not just investment that’s made for

602
00:42:31,230 –> 00:42:33,770
CCAI, it’s investment that’s made for Google as a whole.

603
00:42:34,200 –> 00:42:37,170
Obviously we’re very, very careful about what data we use.

604
00:42:37,180 –> 00:42:40,560
We use public data, Google Assistant, YouTube, Voicemail, et cetera.

605
00:42:40,880 –> 00:42:44,590
We’re not using any other cloud customer’s data, because that would completely

606
00:42:44,590 –> 00:42:50,650
breach our privacy and confidentiality. But the customers do benefit

607
00:42:50,650 –> 00:42:53,180
from a lot of that research and investment that Google

608
00:42:53,180 –> 00:42:57,750
is doing in the space. Antony, I know people are

609
00:42:57,750 –> 00:43:00,000
really curious about some of these things. So, I want

610
00:43:00,000 –> 00:43:02,320
to flash some of the stats and ask you to talk

611
00:43:02,320 –> 00:43:06,020
through them on conversational AI, which is what you lead

612
00:43:06,020 –> 00:43:12,230
the people. Those are some of the numbers that are a

613
00:43:12,250 –> 00:43:15,060
reflection, a bit of that investment that we’re making, and

614
00:43:15,060 –> 00:43:19,810
they turn into vast adoption from customers. Thousands and thousands

615
00:43:19,810 –> 00:43:24,610
of customers are using a Dialogflow out of price customer. Dialogflow has

616
00:43:24,610 –> 00:43:27,750
reached a big milestone last year. I think it was last,

617
00:43:27,750 –> 00:43:30,940
next, it was around eight or 900, 000 developers on

618
00:43:30,940 –> 00:43:34,160
the platform. We actually reached 1. 4 million developers on the platform

619
00:43:34,160 –> 00:43:36,360
the last two months. And it’s still growing very, very

620
00:43:36,360 –> 00:43:40,780
fast. So Dialogflow has a massive public community that really

621
00:43:41,110 –> 00:43:46,320
helps people with creating bots. We support 32 languages for

622
00:43:46,320 –> 00:43:49,580
voice and a lot, lot more. I think it’s 80 or something

623
00:43:49,580 –> 00:43:54,270
in chat. We have full integration with Genesys platform, which

624
00:43:54,270 –> 00:43:57,120
is the partnership here, which is awesome, but things like…

625
00:43:57,380 –> 00:44:00,620
Our bots can handle 20,000 intent for example, in a

626
00:44:00,620 –> 00:44:04,540
single agent. In a single agent. That means one entry

627
00:44:04,540 –> 00:44:08,190
point, and you can do 20, 000 in intent detection

628
00:44:08,190 –> 00:44:11,150
and route people throughout the entire company. You don’t have

629
00:44:11,150 –> 00:44:13,610
to ask people to press one for service, and two

630
00:44:13,610 –> 00:44:16,300
for support. This is all part of the conversation get

631
00:44:16,300 –> 00:44:19,650
down and it all use case in switch between each of them.

632
00:44:19,840 –> 00:44:21,640
You call for billing first, and then you want to

633
00:44:21,640 –> 00:44:25,640
change plan. All that is done in a single agent,

634
00:44:26,250 –> 00:44:30,230
thanks to that kind of capabilities. WaveNet is interesting. WaveNet is the

635
00:44:30,230 –> 00:44:32,850
ability to create… It’s text to speech. It’s the reverse.

636
00:44:33,050 –> 00:44:34,720
You’re speaking to the bot, the bot has to speak

637
00:44:34,720 –> 00:44:37,950
back to you. And Google has released and developed, and

638
00:44:37,950 –> 00:44:42,360
actually released the WaveNet technology, to create voices that are

639
00:44:42,420 –> 00:44:48,370
very human sounding. And what we’ve realized is the more

640
00:44:48,370 –> 00:44:51,710
human sounding, the more engaging those voices are, the more

641
00:44:51,710 –> 00:44:55,680
the customers or users of the platform are actually engaged. So, getting

642
00:44:55,680 –> 00:44:58,700
to a point where those voices are very, very human,

643
00:44:59,560 –> 00:45:05,050
will help adoption of voice bots in the field. And

644
00:45:05,250 –> 00:45:07,750
without ever deceiving customers, you never want to make them

645
00:45:07,750 –> 00:45:09,910
think they’re talking to a human when they’re talking to

646
00:45:09,910 –> 00:45:13,040
a bot. But having that engaging voice is very, very

647
00:45:13,040 –> 00:45:15,440
important to deploying a bot, and that’s available today. There’s

648
00:45:17,740 –> 00:45:21,700
150 or 60, I can’t remember exactly, WaveNet voices available

649
00:45:21,700 –> 00:45:27,950
to you on the Google platforms in many, many languages. It’s funny. I’m

650
00:45:28,490 –> 00:45:31,390
going to pick up on something about WaveNet, which again, for

651
00:45:31,390 –> 00:45:35,310
a Contact Center audience, this is quite transformative. If you think of

652
00:45:36,890 –> 00:45:40,250
the thousands of different prompts, they recorded as part of

653
00:45:40,250 –> 00:45:50,640
your flows today, and having to take that to a recording artist or somebody in the Contact Center, just to record that quick message. Imagine what

654
00:45:50,640 –> 00:45:55,140
that means for you when you can start to type

655
00:45:55,140 –> 00:45:57,510
them and allow the system to use brand then use… Sorry, to use a

656
00:45:58,960 –> 00:46:02,420
voice that is on brand for you, and choosing one of

657
00:46:02,420 –> 00:46:06,530
those waves in their voices. That’s a huge operational efficiency that is just built

658
00:46:06,530 –> 00:46:11,240
into the platform. And that brings me to the peanut

659
00:46:11,240 –> 00:46:14,310
butter and jelly, the better to get a slide. And

660
00:46:14,310 –> 00:46:19,560
the reason for that is, when we think about all of the things that go into operating and managing a

661
00:46:19,630 –> 00:46:25,340
Contact Center operation, Genesys has user experience. Unlike where we

662
00:46:25,340 –> 00:46:29,250
put a bot in the front, and have that as isolated experience,

663
00:46:29,490 –> 00:46:33,440
really what we advocate at Genesys is let your customer experience platform

664
00:46:33,530 –> 00:46:37,950
orchestrate that entire end- to- end customer conversation. Genesys has

665
00:46:37,950 –> 00:46:41,350
some artificial intelligence technology on its own with something we call Predictive

666
00:46:41,350 –> 00:46:44,810
Web Engagement, that allows you to engage customers based on

667
00:46:44,810 –> 00:46:47,230
all of the data that we know and drive them to an

668
00:46:47,230 –> 00:46:50,500
experience that is very best for them. Sometimes that’s a

669
00:46:50,500 –> 00:46:56,440
human. Sometimes that’s a piece of content. Other times that’s a bot. And using that upfront decision

670
00:46:56,440 –> 00:46:59,660
logic in an orchestration, we can really optimize that end

671
00:47:00,370 –> 00:47:03,520
to end customer experience and bring in the technology at

672
00:47:03,520 –> 00:47:06,730
the right time. I’m going to come back to Ritu

673
00:47:07,060 –> 00:47:08,220
here for a second, because we’ve got a couple of

674
00:47:08,650 –> 00:47:11,210
key takeaways and then we want to get into your

675
00:47:11,770 –> 00:47:25,840
audience Q& A. So, Ritu, why don’t you just recap for us some of the research? all dimensions. That’s fantastic.

676
00:47:26,220 –> 00:47:30,810
And so, if I actually walk you through, in the

677
00:47:30,880 –> 00:47:34,830
study that we just talked about, the 407 different organizations worldwide,

678
00:47:35,630 –> 00:47:38,390
and across the different industry, there are folks who are

679
00:47:38,390 –> 00:47:41,910
early adopters of this technology. So, kudos to them and we

680
00:47:41,910 –> 00:47:44,910
ask them, ” What are your metrics and what are the

681
00:47:44,980 –> 00:47:48,240
rate of improvements that you’re actually seeing today, and also what

682
00:47:48,520 –> 00:47:50,610
you envision it to be in the next three years?”

683
00:47:50,960 –> 00:47:53,880
So, very heartening to see, and I saw one question

684
00:47:53,880 –> 00:47:56,900
in the chat here that someone was asking that, ” What is

685
00:47:56,900 –> 00:48:01,470
the ROI of this investment? Is it just the employee, human

686
00:48:01,470 –> 00:48:04,310
agent productivity?” And if you think the earlier adopters, of

687
00:48:04,310 –> 00:48:07,590
course they have seen improvement in efficiency and that is

688
00:48:07,590 –> 00:48:10,650
one of the metrics, but the top seat, is not just

689
00:48:10,650 –> 00:48:14,480
the efficiency of the human agents. It’s actually better customer

690
00:48:14,480 –> 00:48:20,100
experience. They might be measuring it either through NPS, or that they’re measuring it through customer lifetime

691
00:48:20,190 –> 00:48:24,390
value. There are different measurements. The customers might be doing it, but

692
00:48:24,930 –> 00:48:30,580
that’s just number one. They feel that they actually can reduce the

693
00:48:30,580 –> 00:48:33,960
average handling time, from someone who’s actually giving you a

694
00:48:33,960 –> 00:48:37,940
call, to making it more sufficient than us, either just

695
00:48:38,020 –> 00:48:42,770
being answered by the voicebot, or the chatbot, textbot. All

696
00:48:42,770 –> 00:48:45,540
tapping is on, in a reasonable amount of time, at

697
00:48:45,540 –> 00:48:49,440
the right time, with the right level of messaging to the human agent, to

698
00:48:49,440 –> 00:48:51,850
be answered in the most diligent and the most efficient way.

699
00:48:52,440 –> 00:48:55,770
So the end- to- end handling time is greatly improved, only

700
00:48:56,250 –> 00:49:00,020
24% of them say. And improved call deflection. In certain

701
00:49:00,120 –> 00:49:03,540
situations, if you see that the bot is not equipped to answer that, then

702
00:49:04,230 –> 00:49:07,070
it can be rightly moved on to a different means

703
00:49:07,070 –> 00:49:12,450
of communication. Those are the three most important rate of improvements the customers

704
00:49:13,080 –> 00:49:17,900
are reporting. Of course improvement in efficiency, and all of that is

705
00:49:17,900 –> 00:49:22,570
extremely important and improvement in operational efficiency. But I hope that answers

706
00:49:23,000 –> 00:49:25,860
the question that I saw in terms of the ROI, but

707
00:49:26,290 –> 00:49:29,540
this is something that we’ll be constantly watching and running

708
00:49:29,800 –> 00:49:34,380
more extensive amounts of research over the next couple of months, and over the course of

709
00:49:34,380 –> 00:49:36,570
a year. And we’d be happy to report in more

710
00:49:37,100 –> 00:49:40,280
further interesting insights. So with that, I’ll pass it on

711
00:49:40,280 –> 00:49:56,100
to you, Chris. Chris? the use cases. The answering might be coming off mute there.

712
00:49:56,440 –> 00:49:59,920
But as he’s coming out- Sorry. There we go. I

713
00:49:59,920 –> 00:50:07,360
lost my track. I lost my trail and I saw here the

714
00:50:07,360 –> 00:50:13,030
window was answering questions. The way you want to implement that

715
00:50:13,030 –> 00:50:16,170
is, and we’ve seen that mistake happen in many customers, is they’re

716
00:50:16,170 –> 00:50:20,590
trying to think about all that customer experience transformation and start

717
00:50:20,820 –> 00:50:24,450
trying to want to change an entire BU. They take

718
00:50:24,970 –> 00:50:27,440
a floor organization in their company and try to do

719
00:50:27,440 –> 00:50:30,040
a full change of that. And that’s usually not the

720
00:50:30,040 –> 00:50:32,060
right way to do it. It’s good to have that

721
00:50:32,060 –> 00:50:34,470
as the silver lining of all your transformation, but you

722
00:50:34,470 –> 00:50:36,690
can start with simple things. And we have seen those

723
00:50:36,690 –> 00:50:40,380
simple things being deployed. We launched about three months ago

724
00:50:40,380 –> 00:50:44,440
in April, I think, a Rapid Response VA because of

725
00:50:44,440 –> 00:50:47,420
the situation. We had massive demand for Rapid Response VA.

726
00:50:47,550 –> 00:50:49,580
We launched that and you could deploy a bot in two

727
00:50:49,580 –> 00:50:53,840
weeks for simple use cases, no return processing in retail,

728
00:50:53,840 –> 00:50:57,920
hours of operation in healthcare, balance and query in financial

729
00:50:57,920 –> 00:51:00,460
services. Those are simple use cases you can launch very, very

730
00:51:00,470 –> 00:51:04,560
quickly and get going with your customers, and tamper a

731
00:51:04,560 –> 00:51:07,480
lot of that load that did increase actually with the

732
00:51:07,480 –> 00:51:10,560
current situation through the use of bot. And then you

733
00:51:10,560 –> 00:51:13,240
expand from there adding more use cases, as you go

734
00:51:13,840 –> 00:51:18,840
to fit into your broader customer experience transformation journey. But

735
00:51:18,840 –> 00:51:23,880
start small. You can’t start fast, that’s the idea. Wonderful. And

736
00:51:24,210 –> 00:51:28,300
lastly, before we get to Q&A, from a Genesys perspective,

737
00:51:28,300 –> 00:51:31,490
from a Genesys vision, we want to provide the most flexible AI

738
00:51:31,490 –> 00:51:35,080
powered CX solution for the digital age. And really if

739
00:51:35,080 –> 00:51:37,640
I break that apart, as you see, that’s whether you

740
00:51:37,640 –> 00:51:41,140
deploy on prem, or in the cloud, depending on the business

741
00:51:41,140 –> 00:51:44,470
outcomes that you want to optimize for breaking those silos across

742
00:51:45,760 –> 00:51:48,760
your organization, whether it’s sales, marketing or service, and really

743
00:51:48,760 –> 00:51:53,580
being able to orchestrate those experiences together. So now we

744
00:51:53,580 –> 00:51:56,580
get into questions. And so I’m going to start with the question, that I’m going to answer myself. It comes from Susan, and it’s about call deflection. “What is call deflection?” Call deflection at the end of

745
00:52:07,360 –> 00:52:11,810
the day means taking a customer that might otherwise consume a

746
00:52:11,810 –> 00:52:15,410
high cost resource like a human talking to an agent,

747
00:52:15,710 –> 00:52:17,950
to something that they could maybe self- service. And you’re

748
00:52:17,950 –> 00:52:21,990
deflecting them from one experience to something that you will satisfy them

749
00:52:22,260 –> 00:52:26,340
well. And increasingly we’re seeing that as a virtual agent

750
00:52:26,420 –> 00:52:31,830
experience, where, for a customer, they want to get an answer quickly, virtual

751
00:52:31,830 –> 00:52:34,900
agents can provide accurate, timely advice. You don’t have to

752
00:52:34,900 –> 00:52:37,680
wait in queue for five minutes to get to someone that’s going to tell

753
00:52:37,920 –> 00:52:46,010
you exactly the same thing. That’s called deflection. I’m going

754
00:52:46,010 –> 00:52:50,970
to go to another question here, and this is going to be, Antony, it’s for you- Yes. which is, ” Is there

755
00:52:50,970 –> 00:52:55,170
a white paper on Google Contacts Center AI for virtual

756
00:52:55,170 –> 00:53:01,130
agents on how to deploy it?” There isn’t a white

757
00:53:01,130 –> 00:53:07,300
paper, no. That said, we do have quite a few

758
00:53:07,980 –> 00:53:11,000
SI partners that are not certified, but trained on CCAI and

759
00:53:12,030 –> 00:53:15,220
can deploy that very, very well. I can name a

760
00:53:15,220 –> 00:53:20,220
few, Quantiphi, SpringML,, et cetera, are all very, very good at

761
00:53:20,220 –> 00:53:23,430
size, that have done many deployments and are targeting many.

762
00:53:25,480 –> 00:53:29,080
And can really help you through that journey creating the

763
00:53:29,080 –> 00:53:32,750
bots and deploying them, and also helping you with the agency’s experience.

764
00:53:33,650 –> 00:53:36,820
Absolutely. And I will mention there is a video out

765
00:53:36,820 –> 00:53:40,080
there that you can check out which Genesys Group created

766
00:53:40,080 –> 00:53:45,310
together, which articulates some of the experiences that you can

767
00:53:45,310 –> 00:53:48,590
achieve using this technology. Ruti, next question is for you.

768
00:53:49,270 –> 00:53:52,410
It comes from Mary Ellen and it says, ” Ruti, mentioned

769
00:53:52,410 –> 00:53:56,780
the democratization of data and changing culture.” What tools do

770
00:53:56,780 –> 00:54:02,500
you see customers using to achieve the democratization of data, getting

771
00:54:02,500 –> 00:54:07,300
data out to the right people at the right time?” Oh, that’s

772
00:54:07,300 –> 00:54:11,820
a great question. The very interesting thing today is there’s

773
00:54:11,820 –> 00:54:16,390
no… It’s not a few figures of the structured data

774
00:54:16,390 –> 00:54:19,840
now. There’s a lot of unstructured data that is being used today.

775
00:54:20,030 –> 00:54:24,010
There’s semi structured data. So, there’s a cost of new set

776
00:54:24,010 –> 00:54:26,650
of breed of tools and technologies coming into place in

777
00:54:26,650 –> 00:54:31,860
terms of consolidating one common data platform. But that’s just

778
00:54:31,860 –> 00:54:36,050
the starting this, specifically Facebook, because how do you transform

779
00:54:36,050 –> 00:54:39,470
the data, how you make sure the right set of

780
00:54:39,470 –> 00:54:43,780
metadata is available around them. It’s a little bit fragmented market

781
00:54:43,780 –> 00:54:47,930
right now. There’s something one separate for unstructured data, something for

782
00:54:47,930 –> 00:54:51,810
structured data, but there are a couple of tools and technologies, including

783
00:54:51,810 –> 00:54:55,180
from Google and the other incumbents that we talked, about

784
00:54:55,240 –> 00:54:58,100
that they can reach out to me separately to get

785
00:54:58,100 –> 00:55:01,010
the specific names, but there are things coming into for

786
00:55:01,010 –> 00:55:05,530
consolidation of different datasets, having a common metadata catalog. And

787
00:55:05,530 –> 00:55:11,170
also there are some advancements happening in transformational technology to improve

788
00:55:11,290 –> 00:55:14,320
the using Insights. AI is being used to even assess

789
00:55:14,320 –> 00:55:17,560
the quality of the data, to eliminate the biases in the

790
00:55:17,560 –> 00:55:21,920
data, to do the whole automation data pipeline. So there are

791
00:55:21,960 –> 00:55:24,300
a whole bunch of tools, but essentially it’s a little

792
00:55:24,450 –> 00:55:26,640
bit fragmented right now if I have to say, but

793
00:55:26,640 –> 00:55:30,240
it’s changing very rapidly. All right. So we have more

794
00:55:30,240 –> 00:55:33,410
questions that I think we’re going to be able to answer in five minutes. So, we’re going to get to do rapid

795
00:55:33,410 –> 00:55:38,290
fire. Antony, next question is for you, which is, ” where are we seeing the

796
00:55:38,290 –> 00:55:46,820
ROI on chatbots versus increased staffing?” There’s a few areas

797
00:55:46,820 –> 00:55:48,500
where you could get that. So the first one is

798
00:55:50,800 –> 00:55:55,480
when the chatbot reaches the agent, usually there’s a conversational chatbot

799
00:55:55,480 –> 00:55:59,420
that did a lot of the data gathering, trying to

800
00:55:59,420 –> 00:56:01,500
solve the answer for. So, when the agent gets it,

801
00:56:02,010 –> 00:56:04,270
a lot of that information is already gathered, passed to

802
00:56:04,270 –> 00:56:06,840
the human agent, and then the call is obviously shorter,

803
00:56:06,840 –> 00:56:09,060
because you don’t have to redo all that data gathering.

804
00:56:09,580 –> 00:56:12,990
There’s a gain there. Second gain is a smart reply,

805
00:56:12,990 –> 00:56:16,410
which is something that we have in our technology, which actually

806
00:56:16,410 –> 00:56:23,480
suggests answers directly, and the human agent can click that

807
00:56:23,480 –> 00:56:26,740
answer, and there’s these choices, there’s usually two or three answers.

808
00:56:26,740 –> 00:56:28,550
They can click it, just edit it if they want,

809
00:56:28,550 –> 00:56:31,970
or just send it. So faster typing, more accurate responses,

810
00:56:32,130 –> 00:56:34,750
less typos. If you look at chat agents, they’re always

811
00:56:35,560 –> 00:56:39,560
switching back to fix a typo, that doesn’t happen. And

812
00:56:39,560 –> 00:56:44,700
then also the documents suggestion, knowledge suggestion help the human

813
00:56:44,700 –> 00:56:47,250
agent not have to search and put somebody on post

814
00:56:47,250 –> 00:56:50,500
search, either on Google or in their own internal systems

815
00:56:50,500 –> 00:56:54,340
to find a document. Those documents are automatically suggested through

816
00:56:54,340 –> 00:56:56,800
the Agent Assist. So that goes faster. And at the

817
00:56:56,800 –> 00:56:59,560
end of the call, the call dispositions also shorten because

818
00:56:59,560 –> 00:57:02,020
all that conversation, the intents that were detected in the

819
00:57:02,020 –> 00:57:05,130
conversational, are put in a little summary that can be put

820
00:57:05,130 –> 00:57:08,100
on the case. So you’re gaining efficiency in every step

821
00:57:08,100 –> 00:57:12,430
of the way there. Wonderful. I’ll take the next question

822
00:57:12,430 –> 00:57:16,250
from Christine, which is, ” Where can I find more information about

823
00:57:16,250 –> 00:57:19,460
Agent Assist, how it works, what does the implementation look

824
00:57:19,780 –> 00:57:24,970
like, what can you do with it? And how it’s being used by other customers currently?” Genesys. com.

825
00:57:25,500 –> 00:57:28,500
So we would love to talk to you about that as an implementation partner with

826
00:57:28,500 –> 00:57:34,390
Google, about how real world customers are using it, the benefits,

827
00:57:34,740 –> 00:57:38,140
the timelines, the whole lot. So very happy to engage

828
00:57:38,140 –> 00:57:41,100
with you on that. There is additional information on google.

829
00:57:41,320 –> 00:57:44,830
com if you looked at Contact Centre AI, which takes you

830
00:57:44,830 –> 00:57:50,140
through the Contact Center AI Agent Assist capabilities as well. My

831
00:57:50,140 –> 00:57:54,730
next question, Antony, I’m going to come back to you

832
00:57:54,730 –> 00:57:58,420
on this one. This is from Darrius which is, ” What level

833
00:57:58,420 –> 00:58:02,880
of effort does it take to tune the AI for multi-

834
00:58:02,880 –> 00:58:07,990
tone responses, for it to be conversational?” Just for the

835
00:58:07,990 –> 00:58:11,360
audience benefit, the multi- tone response is really when you’re

836
00:58:11,360 –> 00:58:15,130
going back and forth in the conversation, so it feels

837
00:58:15,130 –> 00:58:19,640
natural. But Antony what’s your answer there? There’s a few

838
00:58:19,640 –> 00:58:22,130
things in that question. First thing I would say the

839
00:58:22,190 –> 00:58:27,150
model itself, the understanding model does not need to be

840
00:58:27,150 –> 00:58:30,640
retrained, or trained, or do anything special. But one we provide out of

841
00:58:30,720 –> 00:58:32,810
the box for you is actually very powerful and can

842
00:58:32,860 –> 00:58:35,520
adapt. So how you’re going to tune it, is based

843
00:58:36,730 –> 00:58:39,290
via courtesy of the training phrases and the appropriate training

844
00:58:39,290 –> 00:58:42,960
phrases to tell our NLU what they need to search for

845
00:58:43,240 –> 00:58:47,860
in those sentences, that’s on a one tone basis. Multi-

846
00:58:47,860 –> 00:58:53,410
tone Dialogflow supports many, many bot returns, conversation, and can

847
00:58:53,410 –> 00:58:55,890
do all the back end calls, have different paths based

848
00:58:55,890 –> 00:58:59,190
on the responses from those web hooks that you’re doing

849
00:58:59,190 –> 00:59:05,060
the back end. It’s not technically complicated, obviously you need

850
00:59:05,060 –> 00:59:07,540
to adapt to conversation. So there is a level of effort,

851
00:59:07,830 –> 00:59:09,430
as I said, you can start in a couple of

852
00:59:09,430 –> 00:59:11,610
weeks and get one or two bugs going and then

853
00:59:11,850 –> 00:59:17,200
add more as you go. I would say overall, it’s

854
00:59:17,200 –> 00:59:19,980
going to be a journey because you probably have hundreds of

855
00:59:19,980 –> 00:59:23,570
use cases, but you can get started pretty quickly, and

856
00:59:23,650 –> 00:59:28,570
you do not have to retrain the model. This is

857
00:59:28,570 –> 00:59:34,280
done automatically by Dialogflow, even in a multi- tone environment.

858
00:59:34,280 –> 00:59:39,330
Awesome. It’s hard to answer a short statement on this one. It’s really

859
00:59:39,330 –> 00:59:42,900
encouraging to see that we have so many questions. Just because we didn’t answer

860
00:59:42,910 –> 00:59:44,610
them right now, it doesn’t mean that we’re not going to. I’m going to

861
00:59:44,610 –> 00:59:50,100
answer one last question and hand back to Josh, our moderator here.

862
00:59:50,290 –> 00:59:55,810
So the question is from HCL, “Is there a specific version of Genesys that is

863
00:59:55,810 –> 00:59:59,070
required for CCAI to work?” Guess what the good news

864
00:59:59,200 –> 01:00:05,720
is, we’ve integrated Google CCAI all on our various capabilities

865
01:00:05,970 –> 01:00:11,260
across Genesys, PureConnect, Engage and Genesys Cloud. So no matter

866
01:00:11,260 –> 01:00:13,700
what platform you’re on, we have something for you. We

867
01:00:13,700 –> 01:00:18,510
can look at your specific requirements and give you a path

868
01:00:18,510 –> 01:00:21,640
forward. So with that, Josh, I’m going to hand it

869
01:00:21,640 –> 01:00:25,940
back to you to take us through what’s next. Awesome.

870
01:00:26,260 –> 01:00:29,650
Thank you very much, Chris. Like you mentioned, unfortunately, that

871
01:00:29,650 –> 01:00:31,410
is the end of our Q& A, but don’t freak,

872
01:00:31,820 –> 01:00:33,870
even though we didn’t answer it live, we will follow

873
01:00:33,870 –> 01:00:36,790
up with you via email within the next few business days. So just be

874
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on the lookout for our prompt responses to you. So

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with all that being said, to wrap up, first, don’t

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forget to take advantage of the additional resources within the

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resource list. Specifically the Contact Center AI success kit, the

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IDC spotlight and the Genesys and Google Cloud partnership page

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on genesys. com. Be sure to click those before today’s

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session ends, and they’ll open up in a new tab

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in your browser and give you an additional information on

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today’s topic. Also after today’s session concludes, you’ll have the

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opportunity to fill out that brief survey that I mentioned

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before. We’d love and appreciate your feedback. And lastly, be

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sure to check out our new podcast Tech Talks in Twenty, where

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we sit down with Genesys experts to discuss the topics

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that you want to hear, in about 20 minutes. You

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can listen on our website as well as anywhere you

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get your podcasts, iTunes, Google Play, Spotify, and Stitcher. So

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with all that being said on behalf of Chris, Ritu and

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Antony, as well as the entire Genesys team, we thank

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you for joining today’s webcast titled, Unlock the transformative power

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of AI for Contact Centers. Until next time, have a

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01:01:39,670 –> 01:01:42,540
good one, everyone. Thank you.

WATCH NOW

Meet the Speakers

Antony p webinar image

Antony Passemard
Guest Speaker
Applied Conversational AI and CCAI Lead
Google Cloud

Ritu jyoti webinar image

Ritu Jyoti
Guest Speaker
Program Vice President, Artificial Intelligence
IDC

Christopher connolly

Chris Connolly
Vice President, Product Marketing
Genesys

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