While AI is one of the hottest topics in the market, contact centers are still afraid to make the leap to implementing solutions. Join this discussion between two AI leaders to learn how contact centers are actually seeing success with AI solutions and the real ways that ‘buzz-worthy’ technology is actually moving the needle. Come with your questions to ask our panel of AI experts for a robust and in-depth discussion.
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Webinar Transcript
Ryan Van Wagoner:
Okay. Hello everyone. I want to welcome everybody to today’s webinar, Demystifying AI in the Contact Center. Super excited about today’s discussion. We’re going to be diving deep into how contact centers and customer service teams, in general, can really get the most value out of AI. And we’re going to hopefully answer a lot of your burning questions along the way. My name is Ryan Van Wagner. I lead marketing here at Forethought. And again, I’m super excited to be moderating today’s session. Before joining Forethought, I was on a few AI teams at Salesforce. And so AI has been near and dear to my heart for quite some time. And of course, all of us here are consumers. We all have experienced is interacting with customer support. And so when we combine AI with customer service, that’s where this gets really powerful. Really looking forward to digging into these topics today. Before we get started, just a few housekeeping items that I want to go over. First, we will having live Q and A at the end of our discussion.
Ryan Van Wagoner:
And so if you have any specific questions, please post those in the Q and A section of your Zoom window. That should be right at the bottom of your window. And if that question is directed at a specific panel member, please let us know and we can be sure to ask either Deon or Jay. This is a fantastic opportunity to ask the experts so please don’t be shy. And second, this panel discussion is being recorded and this will be sent to all attendees. And so don’t feel like you need to be actively taking notes. Of course, you’re welcome to take as many notes as you want, but know that you will be getting a copy of this webinar. Well, I’d love to give a minute to Jay and Deon to introduce themselves. Jay, let’s start with you. And would love to… I know you’ve been involved with a lot of exciting things over at Talkdesk, and even prior to Talkdesk. We’d love to hear a little bit more about your role there and some of your top priorities.
Jay Gupta:
Sure. So thank you for having me. It’s great to be here.
Ryan Van Wagoner:
I think you’re on mute, Jay.
Jay Gupta:
Can you hear me now?
Deon Nicholas:
I can hear you.
Ryan Van Wagoner:
There we go. Okay.
Jay Gupta:
Okay. Good. Well, I’ll start again. So thank you for having me. So I am the Global Director Product Marketing for AI at Talkdesk based in London. And my role or my team’s role is, well, it involves a lot of things. But essentially it’s about successfully having that product market bit in terms of how we are connecting AI to the contact center market. And this means it’s about really developing the right, the messaging around AI, both as a technology and also as an enabler for the products. And making sure that we have clear ways to explain the values of AI to our market and how AI solves critical problems in the contact center. So it’s a really exciting time to be working in bringing AI into the contact center market. I think it’s an exciting opportunity. There’s a lot of exciting things happening and I absolutely love the job.
Ryan Van Wagoner:
Awesome. Love it. Really looking forward to digging in there. Appreciate it, Jay. Deon, lots going on at Forethought. Do you want to introduce yourself and the company and maybe tell us a little bit about where Forethought is headed?
Deon Nicholas:
Thanks, Ryan. And great to meet you, Jay. Hello everyone. I’m Deon Nicholas. I’m the CEO and co-founder of Forethought. And we transform customer experience with human centered AI. So we’ve been on this journey for about three years since we launched working with large scale companies who are looking to grow and scale their business while creating quality customer experiences for their customers. So very excited to be on this panel and talk about how AI can really help everyone.
Ryan Van Wagoner:
Awesome. Looking forward to it. With no further ado, let’s dive right in. First, want to kind of level set on what AI is and also what AI is not. And more specifically, what that means to both of you. And unfortunately, maybe to the chagrin of perhaps all of us on this call, AI has become quite the buzzword in recent years. And I think at times that’s often misapplied to technology where there’s actually no actual AI. And so Jay, let’s maybe start with you here. Tell us a little bit about how you would characterize AI and specifically how you think about AI over at Talkdesk.
Jay Gupta:
Sure. So I think first of all, I’d like to say that AI is not magic and that’s really important for a lot of people. It’s a technology in much the same way that we have lots of technologies around us today. But I suppose for me really it’s about making sure that we characterize AI in terms of the outcomes that customers get. So making sure that the benefits of AI are transparent and tangible to customers. When we think about AI within the contact center for us here at Talkdesk, it’s really about making sure that you can bring AI into the everyday tools that contact centers are using.
Jay Gupta:
And we follow the jobs to be done methodology. We have huge fans of it here. And so for us, it’s really about figuring out the critical jobs to be done in the contact center and where AI can provide better solutions to problems that have existed for decades in the contact centers and have been solved by different technologies and products through the years. And today we’re at a stage now in the digital marketplace where AI is proving to be the best solution to tackle a lot of the problems that contact centers are facing.
Ryan Van Wagoner:
Awesome. I love that. And Deon, I know that this is kind of an interesting discussion here because Jay, over at Talkdesk, you’re kind of working on implement AI into your solutions and getting those into the contact center. Deon at Forethought, kind of focusing more specifically on the AI itself. How do you think about AI and how is Forethought approaching AI in maybe a different way?
Deon Nicholas:
So it’s really interesting. I think the broadest definition of AI is technology that can think, act or appear like a human to some degree. And I think one of the most interesting kind of paradigm shifts has been 10, 20 years ago and AI over the past decade or two has always been about appearing like a human, right? So it’s been about chatbots, they’re conversational, which creates a nicer interface, but they’ve been missing the critical piece which is thinking like a human. And so I’m very excited about all the technology, all of the advances that are on the forefront right now, where you can build technology that uses a lot more cognitive technology, information, data to actually start to do things that can actually benefit humans in a very powerful way, rather than just mimicking what a human would say. So that’s my thought for the morning.
Ryan Van Wagoner:
Awesome. And I want to dig a little bit more in there, Deon, because I know AI can often be even a polarizing topic. It can mean many different things to many different people. There’s often a fear of AI. People go to the extremes and think of a robot apocalypse and on the other end of the spectrum, there’s really a fascination with AI where, again, bringing in the robots, I’m not sure, what the fascination is with robots and AI, but there’s this tomorrow land concept of AI with robots doing everything for us. So digging in a little bit more there, Deon, what is the point of AI in our lives and what do you see as the future of AI in general?
Deon Nicholas:
I think Jay said it best, it’s really about what are the jobs to be done, right? We’re all, as technologists, as problem solvers, we’re here to solve problems for people, right? And so in the contact center, there’s many different ways to think about it. But the way I like to think about it is in terms of the customer life cycle, right? If you’re a customer of a business, you’re a consumer, the first thing that you want when you come to a contact center is actually to get your problem solved without any help. So you first start to think about self-service. Oftentimes that’s not the case. Oftentimes you have a more complex issue. Oftentimes you actually need somebody to get something done for you or on your behalf or you just have a question that you actually want to have a human interact with.
Deon Nicholas:
And then when you think about the ways AI can be applied there across this lifecycle. So you’re moving from self-service to things like triaging, right? Understanding what is the customer’s problem, making sure the best agent is working on that problem in the right channel, at the right time. And then what do you do to support the agent as well as administrators so that, say, the problem doesn’t happen again, right? And so there’s actually a full life cycle of when a customer comes to your website or to your contact center, they have a problem that they want solved and there’s this journey that they go through, and you can actually think about all the ways AI can be applied across that journey.
Ryan Van Wagoner:
Awesome. That’s a really good segue into really why we are here, which is the customer experience and specifically the contact center experience and how we bring AI into that. So Jay would love to dig in here a little bit. To give some context, I think one of the unique things about customer service AI really is its relatability. Every single person on this call and elsewhere is a consumer. We’ve all had that experience, getting bad customer service that we love to complain about for years afterwards and hold up as an example of, I will never do business with this company again. There’s really a lot riding on providing a positive experience for customers. And so, Jay, with that context, can you tell us a little bit about how you think AI can fill those gaps in today’s customer service experience?
Jay Gupta:
Sure. So, I mean, just to follow up on the jobs to be done and actually much of what Deon just mentioned there in terms of the life cycle, we kind of, broadly speaking, we have these three categories that we would put our solutions inside, if you like. So the first one would be around increasing the customer self service rate. We are in a world where people want answers just like that, right? We want the instant gratification, but we also want 24/7 customer service as well. And we are in a world where we’re just a few clicks away from getting whatever we want. So self-help, and being able to access information by ourselves is just something that is increasingly becoming… It’s a rising customer demand.
Jay Gupta:
So using AI to help contact centers increase that self-service rate efficiently is something that we focus on a huge amount on. And then the other thing is… The second sort of solution category would be helping or using AI to identify the causes of customer issues. So why are customers contacting customer service in the first place? Are there any unknown friction points? And AI has the ability to mine a lot of data and just really uncover those things that wouldn’t really be easy for a human to find and detect. So having those discovery and sensing tools and being able to identify why customers are calling customer service. And the third would be helping agents resolve issues correctly and quickly. So agent empowerment is something that we are passionate about. We believe agents are a big part of the AI story and the evolution of AI and really critical to the adoption of AI in contact centers. So those are the three areas that we focus on when it comes to our AI product suite.
Ryan Van Wagoner:
Awesome. I love that. And you touched on something interesting. You mentioned the data piece where with AI, we’re able to uncover these insights for what works best for customers and how to help people… What are the most common issues. Is there anything that you can share there, any really interesting insights that you have uncovered in your work at Talkdesk with AI or how AI can best help with specific questions? Are there common themes that you’ve uncovered?
Jay Gupta:
Absolutely. I mean, we focus a lot on automation when it comes to AI and the discovery of friction points, for example. So a lot of it is about analyzing speech and text interactions. And you can really analyze 100% of them. But also having ways to set alerts and sort of sensors based on key words and phrases. So you can be quite proactive as a contact center, as customer service. When you are sensing some things up, you can get there before customers even realize something’s going on. So you can diffuse emerging situations really early on and proactively and effectively try and mitigate problems further down the line.
Jay Gupta:
And so we find that that’s something that a lot of our customers are really interested in is being proactive. Proactive customer service is something that seems to be high up the agenda for a lot of organizations. And being proactive is, well, it’s easy to say I’m proactive, but how do you really in practice do it. And you need to have that automation in place, but that automation needs to be intelligent. And hence AI powered automation is something that we see being critical from the data side of things and having those actionable insights.
Ryan Van Wagoner:
Absolutely. I love that. And I want to circle back as well to something I think both of you mentioned, which is the self-service piece. Deon, let’s have you take this one. So how do you think about customer self-service and how important should it be to give customers the option to self-serve and maybe what does that look like for the customer?
Deon Nicholas:
One of the interesting things about self service that I started to realize recently is that there are actually two key factors in customer self service. A lot of people think about it in terms of deflection rate, right? And for the customer, for the consumer, for the business, that has really good impacts, right? So your customer effectively gets their question answered within minutes rather than in hours. And then you have less workload on the side of the agents. But one of the key critical missing factors in just thinking about deflection rate is customer satisfaction. One of the things that you notice is that sometimes deflection rate doesn’t necessarily correspond to CSAT. Sometimes it’s actually people getting angry. They’re like, hey, why am I talking to this bot when I just want to talk to a human? They’re going around, they’re trying to make a call otherwise.
Deon Nicholas:
And so you’re seeing that deflection doesn’t necessarily correspond with your problem being solved. And so I often actually… When you think about self-service, I actually, don’t like to use the phrase deflection. I like to use the phrase resolution. Is your problem being solved? And that usually corresponds to your customer satisfaction scores actually going up rather than going down when you implement a self-service solution. There’s a lot of ways to implement self-serve from many people think about chatbots. You can think about improving search on your website. You can think about being proactive and reaching out to the customer before they even know they have a problem. A lot of different things there, and those all lead to resolutions rather than deflections.
Ryan Van Wagoner:
That’s really interesting. Why do you think… Because I’ve noticed this as well. There is kind of a true and focusing on deflection and just preventing the customer from ever getting to the contact center, which is nice. There’s a major benefit there. But why do you think there’s almost been more of a focus on deflection than on really CSAT and on providing that top level customer experience?
Deon Nicholas:
My actual take, and I’ve been thinking about the as recently, I think it’s because it’s easy to measure at the end of the day. Deflection is a proxy. What we really care about at the end of the day is our customer’s problem being solved. And everyone knows that. If you ask them, hey, what do you care about? It’s we want our customers to have a good experience and have all their problems solved instantaneously. That would be the holy grail. The problem is, it’s actually hard to measure that. It’s hard to know whether the customer left the website because they’re angry or because they’re happy. It’s hard to get that extra signal unless you throw up a survey or you throw up a CSAT indicator or give me five stars or something like that. And oftentimes even then it’s only the angriest or the happiest customers who fill this kind of thing out, right?
Deon Nicholas:
And so understanding customer happiness, if you take it as a broad problem, is actually a hard problem. And so what ends up happening is the simplest thing for contact centers and support teams to do is they just measure, hey, how many interactions did somebody have with our system and how many of those led to a ticket? And that just happens to be the easiest way to measure it. It turns out it can be solved. It takes a lot more energy, it takes a lot more technology, but being able to measure customer happiness is something that’s more of a long term game rather than a short term game. And it just takes a more sophisticated setup to do that.
Ryan Van Wagoner:
Love that. And that kind of… That reminds me as well, one problem or concern that I’ve heard over the years about AI is that it may not provide the same experience. So there’s a concern about AI not providing the same experience to all of the customers. Maybe it focuses on providing a premium experience to a target audience or to a specific language or geographic area. And, Jay, I want to throw this over at you. But how do you think, or do you think AI can be used to actually improve the inclusivity and provide a more inclusive customer experience?
Jay Gupta:
It’s interesting that you should mention that. I actually presented on this very topic this morning. And I actually have some stats actually that I think could help to set the scene. So we just think about the market. We’re in the digital population where 59% of the global population are active internet users, 4.2 billion are active social media users. And what that really means is that you’ve got a platform, you’ve got tools to advertise and promote your brand values to a diverse customer base, globally, crossing borders, et cetera. And also we know that the digital marketplace is enabling you to sell to anyone to anywhere in the world. So there’s a lot you can do because of digital. But what’s interesting is that customer service technology hasn’t really evolved along the way. So the question is, is the customer service set up to provide the same quality of care to every customer, given the fact that you are not only advertising to a diverse customer base, but you’re also now selling to a diverse customer base.
Jay Gupta:
So are you able to support that diverse customer base with existing technology? And the truth is, is that it’s going to really hard. And I think that’s a real challenge that a lot of organizations are facing. And I think the implications of this is that we know that brands… Inclusivity, for example, does matter to brands. Whether it’s the influence of gen Zs or brands be looked upon to fill gaps in society. We also know that purpose driven brands perform better in the stock market, for example, and brand activism is something that is paving the way to growth. But there’s also an all time high of consumer cynicism about brands in terms of are they delivering on their promise? Are they just CSR washing? And I think there’s an opportunity for organizations to look at the customer service function and look at how AI can actually help to engineer some of what needs to be done to deliver on those brand promises and inclusion is a big part of that.
Ryan Van Wagoner:
Absolutely. I really liked what you talked about. Because it’s really not just the technology, it’s also the teams and the policies and the approaches and the priorities of these customer support organizations that has to be at the forefront. And you can’t just rely on the technology to cover any gaps that you may not be missing. So I like that approach. Deon, would love to pull you in here as well. How do you think… I guess, maybe the flip side of that, how do you think the AI models themselves can help or do you think that’s something that people need to be more proactive about? What are your thoughts on the actual technology side of improving this inclusivity with customers?
Deon Nicholas:
When you think about all of the different ways that customers can interact with business, right? We’re starting to see a lot of instances become more omnichannel, Talkdesk included, and many other. Nowadays you can call, you can email, you can text, you can tweet at businesses. And there’s so many different ways to get in touch with business. And I think it’s actually this increasing problem. And I mean, problem in the mathematical sense, not in the bad sense. But like a intellectual problem of how do you meet your customers, where they are, right? And as you see an increase in differing populations coming online and things like that, different demographics, different locations will have different ways of interacting, right?
Deon Nicholas:
For example, WhatsApp is very, very, very popular outside of the US for interacting with businesses, as well as your friends and family, right? And so how do you start thinking about all of these things? And so technology actually has the ability to go and bridge that gap, right? So how do we start creating technology both from the systems of engagement? So the Talkdesks of the world, and also kind of at the system of intelligence side, the Forethoughts of the world, that can understand, right? That this customer who called in is the same customer who sent a text from this other mode, right? And so I do think technology is going to help that. And then the last thing just from… This is something that’s kind of more general when I think about AI and kind of statistics in general is that using diverse data points always, always, always makes your intelligence layer, your intelligence system better, right?
Deon Nicholas:
So being able to take in different kinds of speech patterns, different kinds of data, different kinds of questions will actually create better systems that enable customer service teams and the technology they’re using to then go and serve their customers better and solve the problems better. So I think it’s a win-win all around. And we’re starting to see some really, really cool stuff just across the industry. And I think it’s going to mean better solutions for consumers.
Ryan Van Wagoner:
Awesome. I’m pumped, I’m excited. Love both of those answers. So I want to shift gears just a little bit. We’ve talked about the customer experience, now let’s talk a little bit about the employee experience. I think the employee experience in general, for a lot of the industry and a lot of the country or world was almost more of an afterthought for a lot of companies until the pandemic hit. And so many employees were suddenly finding themselves, whether they liked it or not, working remotely and then kind of adjusting to a new normal. And I hate to use that phrase. I think that’s very overused. But a new paradigm. It was a totally new paradigm for employees. And I think we can all agree there’s some serious perks to that, but that also meant companies had to focus more proactively on the employee experience and providing their employees with the tools they need to succeed from anywhere.
Ryan Van Wagoner:
And especially for customer support teams, this presented kind of an interesting challenge with the pandemic causing a lot of extra strain on support teams as consumers just kind of went all digital with their requests. We saw a record high number of customer requests. I read recently that 82% of consumers expect to continue contacting customer service at pandemic level rates. So this is not something that is going to go back to the previous normal. So my question for both of you. I would love to get both of your takes on this and we can start with you, Jay. How can contact centers use AI to balance customer experience and employee experience? And so in other words, how can they provide a positive and memorable end customer experience while empowering their service agents and giving them a great experience so they can actually stay loyal to the company?
Jay Gupta:
I mean, outside of the customer service world, I mean, there are a lot of stories about AI projects failing. And one of the primary reasons is that frontline staff, frontline management, the people who are really going to be there to operate new technologies, haven’t really been considered in the rollout and the acceptance of new technologies. So it’s important to consider frontline staff. And we often describe AI agents as the ultimate power couple in the sense that agents can help to steer AI and operationalize it and ensure that automation is working, that it’s not going off the rails. So effectively kind of making agents the custodians of automation and being able to operationalize AI. But to do that, there is a kind of a barrier that needs to be lowered and something that we work towards in Talkdesk is how we can use non-technical staff to operationalize AI.
Jay Gupta:
And by that, I mean, fine tuning the AI models, ensuring that AI is maintaining its predictive power. And for a lot of the big tech companies or the deep tech companies out there, they have an army of data scientists that can do a lot of that. But for most regular companies, companies who want to remain regular companies, it’s really out of reach for them to do that. So having a sort of a no code interface to be able to operationalize AI, fine tune it, and making sure that you have your frontline staff having simplified ways to ensure that the output is always reliable and correct, and that you can ensure that the wider organization can trust that AI is delivering on its promise.
Ryan Van Wagoner:
I love that. That’s focusing on kind of the lowest common denominator in that front end. Sorry, that frontline experience. I think that’s where this starts and I think it’s a great answer. Deon, would love your take on this as well. How can AI be used to balance the customer experience and the employee experience?
Deon Nicholas:
So, first and foremost, I think about it in terms of, well, everything starts with people. One of my mentors once told me, it’s kind of a joke, but in business or in real estate, the three most important factors are location, location, location. But in our world, the three most important factors are people, people, and people. Whether you’re building a company or whether you are creating technology to serve your customers. And so I think about it as the employee experience is the strongest leading indicator of your customer experience, right? And so not only do we as businesses have to think about how to empower our teammates or our agents in order to do the best work of their lives, but we also have to think about how they’re going to interact with the technology that’s going to then empower our customers and consumers.
Deon Nicholas:
And so at Forethought we’ve always thought about it in terms of that. And that’s kind of why we’re talking about the phrase human centered AI. That’s really what true AI is about. Starting with the employee experience. And I really enjoyed what Jay said in the sense that your agents can become the biggest inputs to your data model. The problems that they’re solving every single day can then go and help teach or train the AI what is good, what is bad, those sorts of things. And so by having your agents work hand in hand with the technology, you start to see these synergies working really well together. So I think all around, that’s good news for the consumer.
Ryan Van Wagoner:
For sure. And I like both of those answers. I think it kind of ties back what we talked about before, even with the deflection question where this all comes back to people. This is not about improving a metric on some board somewhere. This is really about improving the lives of customers and employees alike. And that’s kind of the purpose of AI. Is that a decent summary?
Jay Gupta:
Yeah.
Deon Nicholas:
Exactly.
Ryan Van Wagoner:
Okay. So we talked about some of the fears of AI, we talked about the customer experience and employee experience. I think there’s a lot of people maybe even on this call who maybe completely bought in to the idea of AI. They’re sold. They just may not know where to actually begin. And so, Jay, what advice do you have for somebody who is evaluating AI for their business and just is at a loss of where to begin?
Jay Gupta:
So I think it’s about positioning and messaging AI appropriately to the different stakeholders. So when we think about the top management, what’s top of mind for them? Digital transformation and accelerating digital transformation is definitely top of mind. And they are becoming increasingly aware and [inaudible 00:30:37] AI as a technology. But some of them might be you thinking of it more from sort of having some sort of a data driven mechanism around AI. For others it’s more about what would make them win market share. So aligning the outcomes against the different stakeholder priorities is important. So if we go further down to the frontline side, agents and frontline managers don’t necessarily care about AI.
Jay Gupta:
I don’t think they really need to know about AI. What they do care about is being able to efficiently do their jobs. So efficient performance is critical for them. And so when we kind of communicate to frontline staff, let’s think about how we can make their jobs better, how they can really be relieved from the mundane, repetitive tasks that a lot of agents have to deal with on a daily basis. So basically making sure that your message aligns appropriately to the different stakeholders.
Ryan Van Wagoner:
Awesome. I love it. Thank you. Deon, same question. Advice for somebody who is considering AI.
Deon Nicholas:
This is definitely a biased answer, but I think it’s the right one. Is come talk to us, right? And so what I mean by that is AI as we’ve talked about has become a buzzword in so many ways. And so it’s very hard to separate fact from fiction in many cases. And so going to a credible source, whether it’s the people working on the technology or the people who’ve implemented this across many different customers is a good way to just educate yourself, right? To learn about the different ways.
Deon Nicholas:
For example, hey, many people don’t know that AI isn’t just a catch all phrase, but there are these many different components. Whether it’s the self-serve experience or the triage experience or the agent assist experience, right? And so depending on your type of business, you might actually be in need of a different kind of solution. And so sometimes the easiest way is to, I would say, just dive in. So my biased answer, but I actually think is the correct one is talk to… Sign up at forethought.ai, hit a demo or shoot myself or Jay or Ryan, an email or a LinkedIn message and we can learn more about what your needs are and where to start.
Ryan Van Wagoner:
Awesome. Awesome. Love it. And I have to agree with Deon. It’s a great suggestion. Come talk to us. So I do want to leave a few minutes for some Q and A. I saw a couple questions come in. So one question that can be for both of you is kind of a spin on what we just talked about, but what advice would you give to companies who are not yet sold on AI for their contact center? So a little bit of kind of a similar question, but they’re not totally sold. How do you go and make that case internally for AI? Jay, let’s start with you.
Jay Gupta:
Sure. So I think I would probably start off with, well, actually have a conversation, but understand what are their concerns, what are their… Are there fears about AI and what are they? And then I think trying to break down any kind of fears and perhaps some misconceptions that people might have about AI. So just having a conversation, getting an understanding of what they know about AI and then connecting the dots to the tools that can provide some practical assistance to both agents but also to kind of the wider contacts center staff, but also looking at the top management as well and how they benefit from having AI in the contact center. So it’s about having that conversation and trying to really kind of uncover what are their fears.
Ryan Van Wagoner:
That’s really a great point. Kind of digging into the why behind those concerns and that hesitation. Deon, anything to add there?
Deon Nicholas:
I mean, just echoing what Jay said and adding a bit of a tongue in cheek answer here as well is just maybe you don’t need AI. And so what I mean by that is forget AI, again, the buzzword, right? And go back to, I think Jay said this earlier, what is the job that needs to be done? What is your problem? And so understanding, again, the why around why maybe do you have these concerns about the solution, but also why do you need a solution in the first place? What problem are you trying to solve? And usually when you go back to that, back to the basics, what problem am I trying to solve, you’ll find maybe there’s a scale of company that doesn’t need AI. If you’re very small business, for example, maybe that’s not actually the solution for you. You may not have the deflection rate problem. Or maybe you have the deflection rate problem, but you don’t have the customer experience problem yet and the CSAT problem.
Deon Nicholas:
And so as you start to scale and as your business actually changes, depending on your workload, depending on your ticket volume, depending on how many agents, how many BPOs you’re working with and so on, you might actually need a different solution. So again, just peeling apart the buzzword from… If you have a hammer, not everything is necessarily a nail, right? And so, again, going to the basics, what is the problem you’re trying to solve? What are the fears you have? And then you’ll realize whether, hey, maybe I don’t don’t need AI, or in many cases, you’ll find that AI is exactly the thing for you. And both are good options. And so really thinking about what problem are we trying to solve, what fears do we have and then tackling it from there from first principles.
Ryan Van Wagoner:
Awesome. Love that. And this next question is somewhat similar. This person says, so we have started implementing AI in our contact center and we’re having trouble getting agents to use it. How do you drive adoption with AI in the contact center?
Deon Nicholas:
Can I take this one? I’ll go first. Something that’s near and dear to me after building a human centered AI for the past few years. But I think it, again, depends on what the AI is doing, right? So there’s AI that’s for automation. And a lot of that is actually… The user of that is really the administrators and, or the end customer, not necessarily the agent. What that’s doing is reducing the amount of mundane work the agent has to do. And so adopting the AI or not is actually maybe irrelevant. What you care about is are those agents lives getting better. But there are also tools. For example, Jay mentioned some of this, and we talked about this earlier, there are actual AI tools for the agent.
Deon Nicholas:
This can be coaching products, suggestions, auto complete. There’s a whole many, many different things there we’re working on. Product we call Assist Anywhere for exactly that problem. And so for something like that, if you’re not seeing adoption, again, it kind of goes back to what are the problems you’re trying to solve, why are they not using it? And there can be a variety of reasons, maybe the data isn’t working or something like that. But just separating out automation AI from, call it, augmentation AI, and they actually have different purposes.
Ryan Van Wagoner:
No, I love it. Trying to find the right fit and not forcing something if it’s maybe not the right. That makes a lot of sense. Jay, anything to add there?
Jay Gupta:
I mean, it’s interesting because sometimes I go to TikTok for research to understand what is it like to be an agent. And there’s a really funny guy that [crosstalk 00:38:14] Edgar. And Edgar, I think, perfectly depicts the life of an agent. There’s a lot of admin, there’s a lot of mundane, repetitive tasks that agents have to go through. So I think it’s about positioning AI, not by talking about AI, but actually kind of as a kind of a helper, if you like. Almost like a genie that kind of knows what to advise the customer next or doing some of that pre-call or after call work. Just the stuff that is just really time consuming for agents. So I think it’s about positioning the benefits of AI appropriately and against what are the real pain points of an agent. So we talk about customer pain points, let’s think about the agent pain points as well.
Ryan Van Wagoner:
Yes, yes. Love that. 100% kind of getting back to that balancing the customer experience with the employee experience, focusing on the people. So that’s a great insight. Well, wrapping up here, thank you both for your insights and your wisdom and your expertise. I think this has been beneficial for everybody on the call. And maybe just before we wrap up, Deon, anything, any last words of wisdom you would like to share before we wrap up and maybe also share how people can connect with you and learn more about Forethought?
Deon Nicholas:
Just echoing what we talked about earlier, don’t hesitate to reach out. I’m on LinkedIn, Deon Nicholas. Or shoot me an email, [email protected], D-E-O-N. And we’re always here to help, right? Whether you’re looking to actually adopt AI right away, or just learn more about the different kinds of AI, the different problems you can solve, that’s really where it starts is just understanding your data, your workflow, and having those conversations. So we’re friendly people here at Forethought, so don’t hesitate to reach out and we’re happy to help.
Ryan Van Wagoner:
Excellent. Thanks, Deon. Jay, same question. Any parting words from you and how can our viewers connect with you and learn more about Talkdesk?
Jay Gupta:
Sure. So I think the most important thing is this talk, this webinar is called Demystifying AI. And I think my biggest advice is AI, don’t fear it, don’t be fascinated by it, just really focus on the everyday real life problems that contact centers have, and focus on the outcomes. And just if you want to contact me, I’m on LinkedIn or my email address is [email protected]. And happy to help and have a conversation with anyone about AI, contact centers or TikTok even.
Deon Nicholas:
Love TikTok. Love TikTok.
Ryan Van Wagoner:
Awesome. Thank you, Jay. Thank you, Deon. Thanks everybody for joining us today. One quick reminder, the recording again will be shared out with everybody. So please watch out for that in your inbox. And we hope to see you all soon. Thank you.
Jay Gupta:
Thank you. Bye, bye.
