Speaker 1: 0:34

Today we got another Q&A episode for you, heavily focused on AI. Stay tuned Once again. Welcome to the Digital Customer Experience Podcast with me, alex Turkovich, so glad you could join us here today and every week as we explore how digital can help enhance the customer and employee experience. My goal is to share what my guests and I have learned over the years so that you can get the insights that you need to evolve your own digital programs. If you'd like more info, need to get in touch or sign up for the weekly companion newsletter that has additional articles and resources in it. Go to digitalcustomersuccesscom. For now, let's get started.

Speaker 1: 1:13

Greetings and welcome back to the Digital CX Podcast, the show where we talk about all things digital and CX. My name is Alex Tergovich. You're listening to episode 90 of the show and another solo episode for you. We've had an interesting couple of weeks here just because we had the holidays and I did a couple of additional solo shows over the holidays where we talked about like Notebook LM and having that create podcasts for you, which is super cool, very fun, and I'd love to hear from you if you've played around with it at all and if you've used it. You know one of the things that I realized is that we didn't really talk about you know, specific use cases of that during that episode, so we may dig into that a little bit today. But first and foremost, I do want to extend a really warm welcome to our two show sponsors for the next little while here. First off, I'd like to welcome back Vitaly, appreciate the sponsorship there and keeping this content going. Vitaly has been a sponsor before they're back for another round, and so you'll be hearing from them throughout these episodes. Big difference between last time and this time is that I've actually become a heavy Vitaly user these days, and so I like to talk about stuff that I can stand behind vitally user these days, and so I'd like to talk about stuff that I can stand behind. Also want to extend a big welcome to QueryPal as a second show sponsor, who do amazing things in terms of support, ticket deflection and artificial intelligence. We're actually testing them right now as well. Welcome to our new show sponsors.

Speaker 1: 2:49

So today I have a couple of things that came to my inbox after last week's episode that I felt were worth kind of bringing up in this episode. A couple of questions that came in that are AI related got a couple of non-AI related things as well, but one of the fundamental things that did come through was this question of when is your program ready for artificial intelligence? We talk a lot about the technology, we talk a lot about the benefits that it brings to you. We talk a lot about the use cases and all that kind of fun stuff, but what we don't really spend a lot of time on, and what vendors also don't spend a ton of time on, is what you need in place in order to be ready for artificial intelligence. There are several use cases that I have in mind as I'm talking about this. One would be implementing a chatbot, a customer-facing chatbot, for instance. Implementing a chatbot, a customer-facing chatbot, for instance, you know. Another might be implementing a co-pilot for your internal teams, which are two kind of interrelated things. Another one might be a chatbot to help you generate content, and so the fundamental kind of answer here is it depends a little bit on the type of artificial intelligence tool that you're looking for and shopping for, but fundamentally speaking, one of the things that you are going to likely need in order to be ready is the proper data. I know you've heard us talk about data over and over and over again. We talk about it a lot because it's super important.

Speaker 1: 4:36

In this case, though, I want to raise two specific things in terms of your data, and let's use the use case of a chatbot. The first is you know you don't really need super, super clean customer data, super, super clean customer data per se. If you're implementing that base level of chatbot that answers some product questions, right, you can literally throw it out in a link or however that platform works, to your customer so that they can answer some basic questions, and it won't necessarily need to be trained on the customer-specific data for that foundational level. However, if you want to get more sophisticated with the artificial intelligence that is customer-facing, for instance, you might want it to use some semblance of personalization. Use some semblance of personalization. You may want it to respond in a way that's appropriate for the kind of user the person is. Are they an admin, are they an executive? Are they an end user? Whatever that may be, you may want to personalize the responses based on their role. You may want to include their first name in responses. You may want the chatbot to know their email address so it can email things out. Those kinds of customer-specific personalization elements need customer data. But I think more fundamentally, if you're going to install a chatbot, you got to have the content on the backend to train the artificial intelligence to answer the questions that are coming in appropriately. If you're expecting to install a chatbot and have it be able to answer your product questions without doing much training, then I think you've been led astray by someone, because ultimately, the more data and the more quality data that you can feed the chatbot, the more accurate and the more thorough your responses are going to be when somebody goes to ask it a question.

Speaker 1: 6:56

Now, what does that look like? That can be any number of things and these are all kind of platform specific. Some do it, you know they all do it a little bit differently. Most chatbots will allow you to train it using your knowledge base, right? So if you have written a bunch of articles, that is prime territory for you know having training a chatbot. Another is your support tickets. You can elect to have a chatbot trained on your support tickets and what responses that you've provided to customers previously that may not be reflected in your knowledge base. Some chatbots allow you to train it on Slack channels and I think the important call out here is that you do have to be a little bit careful about, um, what information you train your chatbot and what you don't.

Speaker 1: 7:53

And you know, for instance, you don't you don't want to release the hounds, so to speak, on your entire slack instance, because then you'll effectively be training that chatbot to respond to things that you may not want your customers to know about. So you do have to be pretty guarded in in what you're training that chatbot. So those are really the two fundamental things Like, fundamentally speaking, what content are you feeding it? What content are you training it on? The more the merrier, but not at the sacrifice of quality. And so when people ask me about data readiness or readiness to start using AI especially if it's content generation on a product and those kinds of things my advice is always start writing. Start getting your team writing articles. If you're sitting on a knowledge base with 50 articles in it, it's probably not going to cut the mustard. In fact, 100 may not be enough, 200 may not be enough. It just depends a little bit on your product complexity as well and how many different topics that are potentially you know going to be asked by your customers. Now there are some pretty cool tools that can help you with that sort of content generation.

Speaker 1: 9:18

A couple of things, a couple of ones that kind of stand out. There are a few solutions out there which help you specifically with knowledge base. One in particular is called Ariglad A-R-I-G-L-A-D. We've been testing them out a little bit as well. So, basically, you train it on your existing content, you train it on your support tickets, you train it on specific Slack channels, and what it will do is it will suggest updates to existing articles as well as suggest new articles and write them for you. Very cool tech and does a really good job of uncovering additional articles you may want to write some things about. And there are other solutions that do that. In fact, some chatbot vendors do that intrinsically so, like Maven. Agi, for instance, has this kind of feature where it will suggest articles and suggest updates to articles based on. You know some of the conversations that have been happening with customers using the chatbot. So there's some cool things out there that can help you essentially maintain your knowledge base.

Speaker 1: 10:32

Some others that are worth mentioning something like Clueso is really cool. We've been checking out Clueso recently as well. Clueso I could say Clueso one more time. Clueso is a platform that allows you to either record video using the platform or upload video of like product demos and screen shares and those kinds of things, and it'll turn it into basically AI regenerated, rewritten video snippets with professional sounding voiceovers, as well as articles. So by uploading one kind of karate demo, you can spit out videos and articles without having to write them all from scratch. You can even do things like you know create GIFs out of the images and things like that that it creates. So there's all kinds of cool tools. I'm just scratching the surface. There is a tech stack page on the website that you can go check out if you want to go see what other platforms I've recommended in the past or you know are out there to help you with some of that content creation.

Speaker 1: 11:35

Another thing that came out of last week's episode is Mike and I had a somewhat lengthy back and forth about being behind on AI and if you are behind on AI, what you should kind of do about it. We didn't really get into the specifics there, so I wanted to provide a couple of specifics off the back of that conversation, because I did get a couple of questions about okay, well, you know, what should I do to really increase my, I guess language in artificial intelligence. The first thing I would just recommend blanket you know blanket statement is start using chat, chat GBT, start using perplexity and I've talked about this, the difference between the two on the, on the channel or on the podcast before. But essentially chat GBT it's changing a little bit just in terms of its access to the internet, but ChatGPT tends to be kind of like a companion, more so than Perplexity, which is basically a research engine. It's kind of like you know, a lot of times I'll use Perplexity over Google just because it does, you know, really good internet research and summarizes things for you really nicely, kind of like Google search results do today, but just better. Kind of like Google search results do today, but just better. So my first piece of advice would be just start using that stuff on a regular basis.

Speaker 1: 13:05

Anything you would Google normally, just pulling up Google real quick, use ChatGPT or Perplexity to do that and start getting in the habit of using it and seeing what it comes back with, because one of the other benefits there is that you can follow up with it and essentially have a conversation with artificial intelligence that way, so that it learns a little bit about how you want to learn and how you think, and then you know you in turn get the details that you're looking for and get the accurate answers that you're looking for, because we all know a Google search typically doesn't return what you want it to the first time around. These next few are a couple of suggestions just for leadership in general and some of the tooling that you might want to look at implementing if you are looking to essentially get your organization into AI when it's any number of like ticket deflection, ticket co-pilot systems the sponsor of the show, for instance, querypal, is a good example of that but essentially systems that will suggest answers to questions that come in in real time so that when your agents or when your CSMs go to respond, they'll have a baseline or maybe even a draft of an answer ready to go that they can just edit and fire off. That can save a ton of time and really increase the amount of throughput that your teams can handle, just because some of that grunt work is being done for them and research is being done for them. So, basically, like a co-pilot situation. Another is just in the realm of content creation. There are so many cool tools like HeyGen, like Cluso that are really focused on content creation and especially video content creation.

Speaker 1: 14:59

That has come a long way in a very, very short amount of time and there really isn't an excuse anymore for your knowledge base articles to not have videos in them and for you not to have really thorough product walkthroughs and demos and things like that that you can then go and install either in product or have, you know, send out via guides or there's any number of things that you can do once you have that content created, but a lot of times, as you know, content creation tends to be, you know, the root of of a lot of problems, just in terms of getting off the ground with self-serve motions. So look for tools, look for artificial intelligence tools that can help you create that content. And I'm not saying that the content should be solely created by artificial intelligence, because while it's good, it's not there yet. I can tell you right now I would not want to sit down and listen to 30 minutes of AI-generated video, because the voiceover still you can tell, you can just tell it is AI-generated. Do I mind listening to that for a minute or two or three, for a quick walkthrough of something? Absolutely not. I think that is a prime use case for AI video generation, those really short walkthroughs that you can just pump out one to the next Great use case for AI.

Speaker 1: 16:31

Another place to look at implementing artificial intelligence is via your integrations. I will use Zapier as an example for right now. Makecom also has something similar. A lot of tools are now launching artificial intelligence configuration help. In other words, you prompt what you want to accomplish and the tool will then spit out a configuration suggestion based on what it is you wanted to do. Useful, especially when you're building very complex integrations and when you're wanting to do a lot in terms of connecting different tools and data sets and those kinds of things. It can really take the grunt work out of building integrations between systems. So definitely look into you know how AI can help you with those kinds of things. And then you know.

Speaker 1: 17:30

Another thing that we mentioned in the last question was just around chatbots in general. There are tons of vendors out there. Now A couple that come to mind Maven, agi, intercom is interesting because Intercom has a standalone AI chatbot that you whether you use intercom for support tickets or Zendesk for support tickets or whatever you use for support tickets um, intercom and their AI chatbot can actually um, you know it can. It can plug into any any system there. So that is definitely something that is relatively easy to stand up, but again, you need to have the content to train the chatbot.

Speaker 1: 18:17

It's kind of a vicious cycle and, as I mentioned at the beginning of the episode, another thing I wanted to make sure that we touched on today was in response to the two holiday episodes that I published, all about using Google's Notebook LM to prompt and then to have it pump out podcast episodes for you. Now, there were a couple of responses that I got that were like well, why would you wanna do that? And it's a fair question, because it seems, at first glance it seems a little bit random that you would want to produce a podcast episode from two personas that seemingly had nothing to do with you. You'd never like. These are just, you know, a female and a male persona. They're chatting about whatever. Whatever it is you want them to chat about via your prompt persona. They're chatting about whatever it is you want them to chat about via your prompt.

Speaker 1: 20:40

But a couple of things did come to mind as I was going through that exercise. The first is if you are driving a specific type of let's call it thought leadership. One example would be if you are in vertical SaaS and you serve a very specific industry, it is quite likely that you have a blog out there with articles related to that industry. But maybe the industry is underserved in podcast land and so you might want to produce a podcast that is specific to that industry. It might get a little bit old if you're producing this podcast using solely Notebook LM, but if you want to do you know you were writing that could be a really good use case of using Notebook LM to produce that professional sounding podcast for your industry. And very similarly, if you're not serving a particular industry you're serving multiple industries, but you a software platform that is focused on I don't know e-commerce or accounting or whatever across you know multiple industries. That could be another instance where you do want to produce some thought leadership about the services and the space that you are in. So considering using Notebook LM for that sort of thing and you know you might even toy with having it answer questions, answer customer questions via audio that could be an interesting use case, though you know you may want to vet that it is actually answering the question correctly.

Speaker 1: 22:26

I think, ultimately, when it comes to all of these artificial intelligence tools, the thing that you're going to want to watch out for is just kind of over-rotating on it, because you know, as I mentioned in that episode with Mike, you know there are people out there, I know, that are listening, where you feel kind of behind the times and you kind of feel guilty about that a little bit. And I'm here to tell you that it doesn't really take a whole lot to get yourself fully comfortable with what artificial intelligence is today versus what it can also be, because it is really only limited by our imagination, honestly, and you know we'll see where all of that goes. But fundamentally, it can be equally, I guess, intimidating as well and it can be confusing as well. So I would, I would, I would strongly encourage you to be super crisp on what your use cases are going into it. If you know that you want to leverage artificial intelligence for helping your support agents, or for a phone tree or something like that, or for a chatbot or a content creation or process automation, whatever the use case is, my recommendation is to go in with a really strong use case and then work backwards from there.

Speaker 1: 23:56

A lot of people make the mistake of purchasing the tool or purchasing the solution, or getting into a tool only to find out that it kind of doesn't really do what you imagined it might do. But if it's you just like literally wanting to explore what the possibilities of artificial intelligence are, my recommendation is just to dig in. Do some chat, gpt, do some perplexity. Replace your Googling with those kinds of tools. There are also some really great courses out there. Coursive is one, but it's kind of like clickbaity. I don't know. I've been through some of it C-O-U-R-S-I-V. You've probably seen it in your TikTok feed or something like that. If you're on TikTok, linkedin Learning, there's a lot of good stuff on there. Coursera has some great stuff around artificial intelligence. Just educate yourself on this stuff and I guarantee you if you spend a concerted week or two on this stuff, and I guarantee you if you spend a concerted week or two on this stuff, you'll be in good shape. So don't let your lack of current knowledge detract you from adopting it tomorrow To do simple things, because there are very simple and powerful things that you can do.

Speaker 1: 25:08

A couple of things that I use ChatGPT for constantly is if I have a long document and I don't have time to read through it. I'm going to upload it to ChatGPT and I'm going to ask it to summarize it for me. Images, things like that. I have it help me with my son's homework. My son is in algebra and it's been 30 years since I've done 20, 25 years I'm not that old 25 years or so since I've done 25 years. I'm not that old 25 years or so since I've done algebra. And so literally I'll pick up ChatGPT, I'll take a picture of the problem that he's working on and it'll solve it for me and explain how it's done, so that then I can explain how it's done. Like you know, the use cases are just insane.

Speaker 1: 25:54

The other favorite, favorite, favorite thing that I love to do and I'll leave you with this one is I've been walking a ton. I've been trying to do like 10, you know the 10,000 steps a day, three miles kind of situation, some days more successfully than others. But sometimes, when I'm working on a specific problem or thinking through something specific and I'm just a little bit stuck, what I will do is pull up ChatGPT and go into conversation mode, where you can literally talk to it and just have a conversation about whatever it is I'm talking about and it will help me organize my thoughts and it'll have a conversation with me about that thing until I have a clear mental image or you know a picture of what that is and what my next steps should be. I've used it so many times to unblock myself about certain things where either I just didn't have that little spark of creativity or that little bit of knowledge, and that's what one of the things that ChatGPT is really really good at. It's great at unlocking that kind of thought process. So I guess that's your top tip for the episode go talk to chat gpt literally. Uh, you won't regret it, uh, though it gets a little creepy sometimes.

Speaker 1: 27:19

Anyway, um, this episode has been a little bit all over the place, but I hope you've taken, uh, something away and I've hope I I hope I've answered some of the questions that I've gotten in from you from recent episodes. Please keep the questions and the engagement coming. I love, love, love to hear from you, because a lot of times I put these episodes out into the wild and it just kind of it's out there, you don't know who's listening, who's not listening, and all that kind of fun stuff. So please keep it coming. We have some awesome conversations coming up in the next few weeks with some great people that are directly involved in digital Some not so, but tangentially, so I'll keep you guessing on that. But, you know, look forward to having you with this next series of four episodes and then I'll see you for episode 95, another solo episode in a few weeks' time.

Speaker 1: 28:14

Have a great week ahead and thanks so much for listening. Thank you for joining me for this episode of the Digital CX Podcast. If you like what we're doing, consider leaving us a review on your podcast platform of choice. If you're watching on YouTube, leave a comment down below. It really helps us to grow and provide value to a broader audience and get more information about the show and some of the other things that we're doing at digitalcustomersuccesscom. I'm Alex Trigovich. Thanks so much for listening. We'll talk to you next week.