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[00:01]
Pura Brand Voice
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[00:29]
Liberty Mutual Spokesperson
And Doug, there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty Mutual. Even if it means sitting front row at a comedy show.
[00:39]
Sophie Wine
Hey everyone, check out this guy and his bird. What is this your first date?
[00:42]
Liberty Mutual Spokesperson
Oh no. We help people customize and save on car insurance with Liberty Mutual together. We're married. Me to a human, him to a bird. Yeah, the bird looks out of your league. Anyways, get a quote@libertymutual.com or with your local agent.
[00:54]
Sophie Wine
Liberty Liberty Liberty Liberty
[01:01]
Tech Systems Promo Voice
the Agile Brand.
[01:08]
Greg Kilstrom
Welcome to Season six of the Agile Brand where we discuss marketing, technology and customer experience, trends, insights and ideas with enterprise and technology platform leaders. We focus on the people, processes, data and platforms that make brands successful, scalable, customer focused and sustainable. This is what makes an agile brand. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, marketing operations and CX, best selling author and speaker. The Agile Brand podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information, go to teksystems.com now let's get on to the show.
[01:52]
Podcast Host
You probably feel like you know your customers pretty well, but you can't know everything and there always seems to be a new question that you or your team are unprepared to answer or respond to. So how do you identify those gaps before you get a tough question from your customer? Today we're going to talk about the transformative power of knowledge bases in customer support with Sophie Wine, CEO of Ari Glad. We'll discuss how a well maintained knowledge base can benefit both customers and internal teams and how AI plays a crucial role in optimizing this resource. Sophie, welcome to the show.
[02:27]
Sophie Wine
So excited to be here. Thanks for having me.
[02:30]
Podcast Host
Yeah, looking forward to this. Why don't we get started with you giving a little background on your journey to becoming CEO at Ari Glad.
[02:38]
Sophie Wine
Yeah, so I actually worked in customer success prior to this and worked on the company knowledge base and before that was really just working customer service since I was about 14 and so I know that the customers often want to find their own Answers. And my co founder is very much technical in the AI space. So we combined those two backgrounds to create what is really the first AI tool of its kind to make sure that if customers want to self serve and look for answers in the knowledge base, that answers will be there and they can self serve successfully.
[03:13]
Podcast Host
That's great. Yeah. And you touched a little bit on it, but I wonder, just before we dive in further into the, some of the questions, could you touch a little bit more on, you know, what Ari Glad does, like who are your customers and things like that?
[03:28]
Sophie Wine
Yeah. So we basically integrate with data sources. So we integrate with your ticketing system, your Slack release notes. Basically the AI learns how your product works and the types of questions that you get from customers. And then it integrates with your knowledge base and it identifies gaps so it creates new articles if there should be one, if there's enough questions from customers to warrant it, and updates existing articles, merges, duplicates, basically just cleans up your knowledge base and builds it out where it needs to be.
[04:01]
Greg Kilstrom
Great, great.
[04:02]
Podcast Host
So let's dive in here. And I want to start by talking about the importance of a comprehensive and robust knowledge base and its impact on customer satisfaction. So could you speak to that a little bit? Why is the knowledge base so essential for providing excellent customer support?
[04:23]
Sophie Wine
So there's kind of the, I guess, qualitative and quantitative answer. So I would say from, from both of our experiences, I'm sure we've both been on the consumer side where we have a product and it broke or it's not working or what have you, and then you're trying to find that answer online and maybe you find what should be the right article, but it has the incorrect information or you just can't find an answer. And so you kind of have to go through the rigmarole of reaching out. I've definitely experienced that at least. And then there's more, the quantitative side, which is that there's been quite a lot of research into this space that has produced a lot of data that supports just how critical knowledge bases are. According to a Forrester study, 81% of customers look for their own answers first. So they're actually seeking to self serve before they reach out to support. And there's just a lot of research also supporting just how hard it is to be in customer success and support today. You know, customers have really high expectations. A Zendesk study came out recently that said around 50% of customers feel stressed and exhausted after interacting with support, which is not fair on support. They do an amazing job, but people are just stressed out. 53% of Americans experience high levels of stress on a daily basis. So it's often just the last kind of straw for a lot of people. And so for us, we really just wanna make sure that again, we're reducing just how many customers are reaching out in the first place. Customers are happy and customer success and service has more bandwidth. So it's really a win. Win.
[05:57]
Podcast Host
Yeah, I mean it makes sense because there might be other reasons you reach out to a company, but when you're reaching out to customer support, I mean, generally speaking there's an issue, if not a real problem and sometimes a very pressing problem. So, you know, I know personally and I've said this plenty of times on the show before, I'll do almost anything to not pick up the phone and call someone. So I'm one of those people that self service just, I'll do anything to find it myself and, and everything like that. And I literally yesterday, not even prepping for this show, but literally yesterday, accessed a knowledge base and you know what, it was out of date and it wasn't anything near what it was supposed, like what I was expecting to see and stuff like that. And so, you know, the, the frustration is real and I, I didn't even pick up the phone and call. I just kept hunting to see, okay, well maybe somebody said something on Reddit or somewhere else. So, you know, that's. How does your platform help organizations maintain, you know, effective knowledge bases? Maybe to solve some of the problems that I just described in my own, in my own experience, but, you know, just not only maintaining but also developing in the first place.
[07:13]
Sophie Wine
For sure, yeah. I mean it's so hard to keep knowledge bases updated. I really do have to emphasize that especially for, I mean, tech companies, but even non tech companies. But if we take tech companies as an example, we're working with some customers that are releasing new product releases twice a week. And so the product is just moving so fast and so you really, if you don't have a full time team that's working on maintaining the knowledge base and you know, replacing screenshots and making sure that every piece of text is updated, that every piece of the tool is reflected in the knowledge base, it is just going to become out of date so, so quickly and it can actually create just this habit spiral where if a customer never finds their answer in the knowledge base and they can never find it, they're just going to create a support ticket and that's going to cost more and more Money it costs I think over $13 for a B2B company for every live service interaction. So that just adds up. Super. But yeah, on the technical side of how we really do that. So in this example that you just mentioned where there was an article, there was probably the title was right and then content was incorrect. So the way that Ariagloud works is it basically looks at, okay, first of all, have any customers already reached out about this? Have they said, hey, I have this question and the support agent is able to give an answer. And if the question correlates with the topic of that article, the AI is going to see, okay, are the answers that the support's providing not correlating correctly with what's in the knowledge base? And if not, it's going to basically make those edits in the knowledge base and say, okay, there's 10 support tickets. They've all basically provided an answer that is not what this article is providing. And so we would suggest these edits and then support can basically click approve and say, yes, that looks good and the edits will be made.
[09:09]
Podcast Host
Nice, nice.
[09:11]
Greg Kilstrom
Then
[09:14]
Podcast Host
there's a cost to all of this as far as there's time and effort and even platform costs and things like that. But when organizations are looking at success, and some of that is roi, but also some of it is customer satisfaction, how do you recommend that your customers, you know, measure, measure their investment? Sort of from a, from a customer satisfaction standpoint?
[09:43]
Sophie Wine
Yeah, I mean, again, there's so many different angles to look at. So a lot of our customers will be focusing on, you know, NPS scores and making sure that those are staying high. So if you are focusing for example on tickets being handled really well every time, you're going to need potentially some support for people doing support. So agents with AI tools or even agents looking through the knowledge base, so for them to provide accurate responses, they need the resources that are going to provide accurate responses to customers. And then we have the customers that are not going to want to wait at all. They're going to want an answer as soon as possible. A lot of data has been coming out of how expectations, especially with AI chatbots on the rise, the expectations for super fast responses for support has just been increasing. And if you don't have a robust self serve operation, all of your customers that have questions are going to be going to support and it's just going to completely block you up and you're going to be hiring more and more people to try and put a bandaid on the problem and potentially invest in more AI chatbots, when really a huge amount if not the vast majority of those customers in your support channels looked for those answers first. They tried to self serve and would have been happier to begin with. I mean, I'm sure if we take your example, yes, even if you went to support after not finding answers and you had the most amazing experience, your experience with that product would have been tainted somewhat because you weren't able to find the answer in the way and format that you wanted in the first place. So it's kind of, it's not a great experience for both sides because really support can't do anything about that prior experience you had where you couldn't find that answer. And support also would have benefited if you had just found that answer and then left them to have a bit more bandwidth.
[11:44]
Podcast Host
So let's talk a little bit more about those internal teams because as you rightly mentioned, there's a lot of great customer support teams and agents working really hard. And you know, I'm, as much as I personally don't like picking up the phone and talking to people, you know, nothing, nothing against them. That's my, that's my stuff. We'll just call it that I'd prefer to self serve and stuff. But that said, you know, I'm an optimist when it comes to, I think most people want to feel fulfilled, fulfillment and their work and want to do a good job. You know, I'm sure there's some people out there that don't feel that way, but I believe the vast majority of people want to do a good job. And yet, you know, these teams are, they're, they're stretched thin. Often they're, you know, there's too much information, there's not thorough information and stuff like that. So, you know, looking at the, from the internal team perspective, you know, how does a comprehensive knowledge base help those teams and help some of those things that I touched on there.
[12:46]
Sophie Wine
So we first of all want to make sure that the customers that want to self serve were successful and are not clogging up your support channels. But if we put that aside from an agent perspective, this is kind of the hot topic in a lot of customer success and support leadership verticals where they want to use AI and AI kind of chatbots or AI response assistance to support these agents to provide the best experience and, and as quickly as possible. So, you know, if you, for example, let's take Zendesk, there's a, an app that you can now have in your support channel where when you get a question in you kind of have like a copilot where the AI will provide a what they think should be a great response to this customer question. And you can basically take it, you can make any tweaks that you want and then you can send it off. And so it's doing a huge amount of work, not only from just typing that out, but just finding all of those resources, find those bits of information, sourcing it for you and then presenting it to you. But what happens is these AI chat chatbots as well, separate from support individuals. But AI chatbots and copilots, they actually most of the time need to be connected to your knowledge base. What they really need at the end of the day is an accurate and reliable data source that is up to date with how your product works. And if you don't have that, you're always going to be stuck with either shoddy results from these expensive AI tools that do have a ton of potential but need that kind of basic knowledge and data sources and just playing catch up constantly in the knowledge base and just basically realizing a little bit too late that there's incorrect information because these AI response assistants are not providing the right information. So it's kind of a double, I guess, a whammy of, you know, if you don't have an accurate knowledge base, you are having more people reaching out in the first place, clogging up your support channels. And then even if you are kind of investing in your agents, making sure that they have access to resources and they have access to AI response assistance or what have you, again, if you don't have an accurate knowledge base, it's going to be really hard for them to do their job to the best of their ability.
[15:06]
Podcast Host
Yeah, yeah. And I think there's a few things in there that you touched on that you could use as measurements. But you know, similar to the question that I asked from the customer perspective, it's like, how should an organization measure this? And you know, is it all looking at customer satisfaction? Are there internal measurements that you can use to see things as well?
[15:29]
Sophie Wine
So there's been a couple of different ways I would say customer satisfaction. I would also say there is the ability to see how popular a article is. So if an article was really popular and then it kind of went down in value and now the AI has come in, Arglad has updated your knowledge base and you're starting to see the traction build and build over time within your knowledge base. That's a huge kind of direction of success. And then of course, reducing the amount of support tickets in general, depending on what knowledge base you use, we can actually play around a little bit with the tagging and making sure that we can see that correlation of customers are looking for this question for answers to this question and they found that answer in this article. And so yeah, there's a couple of different ways that you can see that success. And then of course if you're really just looking at agent success and reducing the amount of time you're spending on those support tickets, you're looking at investing in an expensive AI copilot which again actually work really, really well. You are not going to see the results that they're promising and the full potential of those unless you have an updated knowledge base. So that's also a huge parameter for success. So taken altogether, it's really just kind of supercharging your entire support to vertical. But individually it's showing, I would say at the beginning it's really just showing that customers are able to find their answers in the knowledge base and those articles are being really heavily used.
[17:00]
Podcast Host
Yeah, yeah. So even, even things like, like time to solution or is that you know, kind of. Yeah, yeah, got it. Okay, you touched a little bit on, on AI in a few different ways but I wanted to ask just specifically how Ari Glad is utilizing AI and if you could talk a little bit about that.
[17:20]
Sophie Wine
So from a technical perspective and I'm this is going to be the dumbed down version because I'm actually not technical, this is how it's been I've learned through the ringer of being a non technical person in an AI world. Basically let's imagine we bring on a new customer, we integrate with their Zendesk their notion Slack channel. It's basically going to be looking for in the Slack channel questions and answers and then same thing in the Zendesk it's be looking for okay, where is a question and where is an answer? And then once it understands that this is the question, this is the answer, where can we now correlate that data has this information been provided consistently? And so it's going to be looking at that either across channels or maybe there's just a ton of those answers provided over and over in just Zendesk. Either way it's going to be looking to make sure that this is not just a one off answer, that this is actually really can be seen as the source of truth for, for this data that we're looking to embed in the knowledge base. So once it's validated that it's going to Take those answers and it's going to look at your knowledge base and it's going to say, okay, we have all of these answers. These are all answers to questions that have come up enough where there definitely should be data for these in the knowledge base. And so it's going to find those gaps, it's going to create new article, it's going to update existing articles, and it's going to do that on a, you know, either weekly basis or near daily basis.
[18:54]
Podcast Host
Got it? Got it. So we talked a few different ways about measurements and seeing improvements. What improvements have you seen that might be closely tied to using the AI for analysis and things like that? What could customers expect from that?
[19:16]
Sophie Wine
So we've seen a couple of different ones. We're actually working on some case studies to basically show, like you said, what is that one consistent piece of data that you can look to to see that this is working. But something interesting that we've seen is some of the customers that have brought us on had very high expectations for ROI themselves. And we didn't even realize this. And it would be, you know, a few months later after using us, and they would come back and say, look, we were really suspicious and we had really high expectations for, you know, these are the parameters that Ari Glad needed to help with and meet. We were going to continue. And they, you know, we did meet it or exceed them. And so one of them that comes to mind is an organization that basically wanted. They had an individual that was basically in charge of maintaining the knowledge base. Not only the knowledge base, but maintaining the back end of the kind of call center and support channel and all of the data that the support individuals were getting as they were providing answers to customers. So she had just a huge job of making sure that everything was updated at all times. And they wanted to reduce the amount of time she was spending on this in half. So they wanted a 50% reduction in time spent on this. And Ari Glad was able to exceed that. And so basically what she. All she needed to do was log into Ari Glad, look at all the suggestions, you know, ask as you're looking at a new proposed article. You can make any tweaks if you would like, but you're basically approving it, you're sending it out. And so that just reduced the amount of time she was spending on this by, you know, a crazy amount. And they were left with a really, really updated knowledge base. And that organization ended up wanting to roll this out for multiple different departments because it was so successful. So that wasn't Potentially that wasn't exactly the data metric that we were thinking would be the utmost importance, but now we've added it to our roster of different ways. You can see how this is working for you.
[21:17]
Podcast Host
Yeah, yeah. And so, you know, I, I work with a lot of companies on various, you know, AI implementations and there, there can be some challenge. There's lots of benefits, of course, potential benefits, many realized benefits as well. But what are some of the challenges that businesses may face when, you know, AI sounds great to solve every single problem, but, you know, often, often there's some challenges in integrating IT and integrating systems and all those kinds of things. So, you know, what are some of the common challenges that you see and you know, what are, what are some ways to, to overcome them?
[21:58]
Sophie Wine
So I would say on our side, one of the challenges that comes to mind is just not having enough data or not giving access to enough data. You know, the AI, especially for rai, has very high standards as to what data makes the cut to be integrated into the knowledge base and be proposed as a change. And so if we are limited as to how many support tickets we get access to, or if, you know, basically if there's just not enough data, the AI is going to be kind of crippled in its ability to do that to the fullest extent. But we now know what that threshold is. So usually what happens is we can already tell that there's not going to be enough data. So we basically just let the customer know and we work together to make sure that we do get enough access on the other side. I would say, honestly, and this is not to toot our own horn necessarily, but I went to a conference recently for a large ticketing organization and they were really focusing on the AI benefits, you know, the AI bots or co pilots or what have you. And one of the interesting challenges I could just see as they would talk about how much time it's going to save and just how helpful this would be to reducing time spent on tickets or success rates. But the knowledge base still needed to be updated. And I would say, you know, yes, okay, we focus on knowledge bases. I'm a little biased, but I would say this really just comes down to data sources, which even speaks to, you know, if our data sources aren't big enough or if a copilot's data source isn't organized into one space, that is going to be just a kick in the pants to the AI, to be frank, it's not going to be successful. You're not going to be able to see it work at its full capacity. And so I would say having a data source prepared and ready for the AI before you implement is just super important because otherwise you're going to be paying for this AI tool while you're getting the data source together and the AI is not going to be able to work properly.
[24:06]
Podcast Host
Yeah, yeah, I totally agree. I've seen that time and time again. That's one of the things that I recommend to everyone. It feels like it's kind of delaying things because you got to get the data house in order, so to speak. But yeah, to your point, when it works, when you plug it in, so to speak, it works and it works a lot more quickly. So it's worth that setup. Absolutely. Well, to kind of close things out here. And it's been great talking with you about this. Wanted to get your thoughts on future trends here and the future of AI and customer experience and things. So what trends are you seeing that are going to influence AI's role? And some of these things that we've been talking about, the customer support and customer experience.
[24:59]
Sophie Wine
Yeah, I would say that the trends have been pretty clear that organizations want to implement AI to take care of the more basic questions for customers. I would say a trend that I'm starting to see a little bit more is that organizations want to elevate AI's ability to handle a little bit more complex questions and also come across as a little bit more human. A lot of organizations are even asking us how they can change the tone or how the AI is coming across and how they can really kind of mold the AI to be, in a sense, like a customer support agent on their team with the same type of vernacular and way of coming across. So I found, I found that quite interesting. But I do think that there is then on the other side, organizations that are waiting for the AI to get better because they don't want it to be associated with their team specifically. They want there to be a very direct line of this is AI, and we are not completely behind them quite yet, but they will get there. So I think, yeah, I'm definitely seeing in the future that as AI continues to get better and those data sources continue to grow and they can really shine. Because I think AI in a lot of organizations where it's, you know, if you implement a chatbot and it was disappointing a lot of the time, it's just not great data that it had access to. So I think as those gaps close and AI just really is able to shine and grow to its full potential, you're going to see organizations become more comfortable with the AI, really taking on more of a, I guess, human element. And yeah, I'm looking forward to seeing how that goes.
[26:51]
Greg Kilstrom
Great.
[26:51]
Podcast Host
Well again, I'd like to thank Sophie Wine, CEO of Ari Glad for joining the show and sharing her expertise on knowledge bases and AI to enhance customer support. You can learn more about Sophie and Ari Glad by following the links in the show notes.
[27:06]
Greg Kilstrom
Thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.greggilstrom.com. that's G R E G K I H L S t r o m.com While you're there, check out my series of best selling Agile brand guides covering a wide variety of marketing technology topics. Or you can search for Greg Kilstrom on Amazon. The Agile Brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay Agile,
[27:56]
Tech Systems Promo Voice
The agile brand.
[28:05]
Liberty Mutual Spokesperson
And Doug there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty. Even if it means sitting front row at a comedy show.
[28:15]
Sophie Wine
Hey everyone, check out this guy and his bird. What is this, your first date?
[28:18]
Liberty Mutual Spokesperson
Oh no. We help people customize and save on car insurance with Liberty Mutual together. We're married. Me to a human, him to a bird. Yeah, the bird looks out of your league. Anyways, get a quote@libertymutual.com or with your local agent.
[28:30]
Sophie Wine
Liberty Liberty Liberty Liberty.
[28:34]
Tech Systems Promo Voice
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[28:39]
Sophie Wine
the way to to.
[28:40]
Tech Systems Promo Voice
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Summary6 min read

The Agile Brand with Greg Kihlström®

Episode #588: Powering up your knowledgebase for better CX with Sophie Wyne, Ariglad
Date: October 14, 2024

Overview

In this episode, host Greg Kihlström welcomes Sophie Wyne, CEO of Ariglad, to discuss the transformative role of knowledge bases in customer experience (CX) and support. They explore how AI-powered knowledge management can improve both customer self-service and internal support operations, the challenges of keeping knowledge bases up-to-date, and how organizations can measure value and ROI. The conversation also looks to the future of AI in customer service, digging into trends, technical approaches, and advice for successful AI implementation.

Key Discussion Points and Insights

Sophie Wyne’s Journey and Ariglad’s Mission

[02:27-03:28]

Sophie started her career in customer service and customer success, gaining firsthand experience of customers’ desire for self-serve solutions.
Ariglad, co-founded with a technical partner, leverages AI to ensure knowledge bases are up-to-date, identifying gaps and automating article creation, updates, and maintenance.

“We combined those two backgrounds to create what is really the first AI tool of its kind to make sure that if customers want to self serve... answers will be there and they can self serve successfully.”
— Sophie Wyne [02:10]

Ariglad’s platform integrates with ticketing systems (e.g., Zendesk), Slack, and other sources, learning product specifics and common customer questions.

The Importance and Impact of a Robust Knowledge Base

[04:01-05:57]

Customers strongly prefer self-service for problem resolution:
- 81% look for answers themselves first (Forrester study).
- Out-of-date or incorrect articles erode trust and drive up support costs.
Customer expectations are high, with 50% feeling stressed or exhausted after support interactions (Zendesk).
Maintaining current, accurate knowledge bases is critical for both customer satisfaction and relieving pressure on support teams.

“Customers have really high expectations... For us, we really just wanna make sure that again, we’re reducing just how many customers are reaching out in the first place.”
— Sophie Wyne [05:33]

Technical Challenges: Keeping Knowledge Bases Updated

[07:12-09:09]

Particularly in fast-moving tech companies, knowledge bases get outdated rapidly due to frequent product changes.
Without a dedicated team, maintaining accuracy is nearly impossible—leading to increased support tickets (each costing $13+ for B2B live service).
Ariglad AI compares customer service answers with knowledge base content, identifies discrepancies, and proposes edits or new articles, streamlining the validation and update process for support staff.

“If the question correlates with the topic of that article, the AI is going to see, OK, are the answers that the support’s providing not correlating correctly with what’s in the knowledge base? And if not, it’s going to basically make those edits.”
— Sophie Wyne [08:21]

Measuring Success: ROI and Customer Value

[09:11-11:43]

Success is measured via multiple metrics:
- NPS and customer satisfaction scores.
- Reduced incident tickets and faster response times.
- Quality and accuracy of AI/chatbot answers, which depend on underlying data.
A seamless self-serve experience elevates the entire support team’s effectiveness.

“If you don’t have a robust self serve operation, all of your customers that have questions are going to be going to support and it’s just going to completely block you up.”
— Sophie Wyne [10:25]

Internal Team Benefits

[12:46-15:05]

A reliable knowledge base supports both external customers and internal agents.
AI copilots (e.g., within Zendesk) can rapidly draft suggested customer responses, but only as accurate as their data source.
Comprehensive, updated knowledge bases are foundational, enabling AI and human support to function at peak efficiency.

“If you don’t have an accurate knowledge base, it’s going to be really hard for them to do their job to the best of their ability.”
— Sophie Wyne [14:37]

Internal Measurement and Agent Efficiency

[15:28-16:59]

Measure article popularity and engagement; increasing usage signals improved knowledge base relevance.
Track reduction in support tickets and time-to-solution.
Updated knowledge bases “supercharge” both the customer and agent support workflows.
AI copilots maximize their potential only with quality, current knowledge data.

How Ariglad Uses AI

[17:20-18:53]

The platform analyzes support conversations (across Zendesk, Slack, etc.) to identify common questions and authoritative answers.
Validates recurring solutions before adapting them into knowledge base articles.
Automatically drafts, proposes, or updates articles regularly, requiring minimal manual oversight.

Customer Results and Case Studies

[19:15-21:16]

Real-world outcome: some clients halved the time to maintain their knowledge base while improving its relevance and accuracy.
Efficient processes led to expansion of Ariglad's solution to additional departments.
Organizational value extends beyond initial customer-facing metrics to operational efficiency.

“All she needed to do was log into Ari Glad, look at all the suggestions... that just reduced the amount of time she was spending on this by, you know, a crazy amount.”
— Sophie Wyne [20:11]

AI Implementation Challenges

[21:16-24:06]

Biggest issue: insufficient or inaccessible data hampers AI’s ability to suggest meaningful updates.
Successful AI integrations require robust, well-prepared data sources; otherwise, outcomes may disappoint.
Organizations are encouraged to "get their data house in order" before expecting AI to deliver results.

“Having a data source prepared and ready for the AI before you implement is just super important because otherwise you’re going to be paying for this AI tool while you’re getting the data source together and the AI is not going to be able to work properly.”
— Sophie Wyne [23:19]

Future Trends in AI and Customer Experience

[24:58-26:50]

Expectation for AI to resolve not just simple but increasingly complex queries.
Rising interest in making AI responses feel more human and tailored to brand voice.
Some companies remain cautious, keeping clear boundaries between AI and human agents—but overall comfort is increasing as technology matures.
Performance of AI is proportional to the quality and comprehensiveness of the underlying data.

“Organizations want to elevate AI's ability to handle a little bit more complex questions and also come across as a little bit more human... As AI continues to get better and those data sources continue to grow... AI just really is able to shine.”
— Sophie Wyne [25:13, 26:36]

Notable Quotes & Memorable Moments

“I would say from both of our experiences... you’re trying to find that answer online and maybe you find what should be the right article, but it has incorrect information... you have to go through the rigmarole of reaching out.”
— Sophie Wyne [04:23]
“If a customer never finds their answer in the knowledge base... they’re just going to create a support ticket and that’s going to cost more and more money.”
— Sophie Wyne [07:56]
“AI copilots... need an accurate and reliable data source... otherwise you’re always going to be stuck with shoddy results.”
— Sophie Wyne [13:45]

Important Timestamps

02:27 — Sophie Wyne’s journey and founding Ariglad
03:28 — How Ariglad works and customer integration
04:01 — Knowledge base value for CX and key industry stats
07:12 — Difficulty maintaining current knowledge bases (+ tech solution)
09:42 — Measuring knowledge base ROI & customer satisfaction
12:46 — Internal team benefits from improved knowledge management
15:28 — Metrics for internal success and agent enablement
17:20 — Technical overview: How AI updates the knowledge base
19:15 — Customer case studies and time savings
21:57 — Common AI implementation challenges
24:58 — Future AI and CX trends

Recap

This episode provides essential insights for marketing, CX, and customer support leaders, highlighting the linchpin role of knowledge bases in customer satisfaction and operational excellence. Ariglad’s AI-centered approach demonstrates not just how to keep documentation fresh, but how to empower both customers and agents—resulting in happier customers, lower costs, and more effective use of advanced AI tools. The conversation reinforces a critical lesson: High-performing AI is built on high-quality, accessible, and current knowledge data.

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Transcript45 lines

[00:01]
Pura Brand Voice
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[00:29]
Liberty Mutual Spokesperson
And Doug, there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty Mutual. Even if it means sitting front row at a comedy show.
[00:39]
Sophie Wine
Hey everyone, check out this guy and his bird. What is this your first date?
[00:42]
Liberty Mutual Spokesperson
Oh no. We help people customize and save on car insurance with Liberty Mutual together. We're married. Me to a human, him to a bird. Yeah, the bird looks out of your league. Anyways, get a quote@libertymutual.com or with your local agent.
[00:54]
Sophie Wine
Liberty Liberty Liberty Liberty
[01:01]
Tech Systems Promo Voice
the Agile Brand.
[01:08]
Greg Kilstrom
Welcome to Season six of the Agile Brand where we discuss marketing, technology and customer experience, trends, insights and ideas with enterprise and technology platform leaders. We focus on the people, processes, data and platforms that make brands successful, scalable, customer focused and sustainable. This is what makes an agile brand. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, marketing operations and CX, best selling author and speaker. The Agile Brand podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information, go to teksystems.com now let's get on to the show.
[01:52]
Podcast Host
You probably feel like you know your customers pretty well, but you can't know everything and there always seems to be a new question that you or your team are unprepared to answer or respond to. So how do you identify those gaps before you get a tough question from your customer? Today we're going to talk about the transformative power of knowledge bases in customer support with Sophie Wine, CEO of Ari Glad. We'll discuss how a well maintained knowledge base can benefit both customers and internal teams and how AI plays a crucial role in optimizing this resource. Sophie, welcome to the show.
[02:27]
Sophie Wine
So excited to be here. Thanks for having me.
[02:30]
Podcast Host
Yeah, looking forward to this. Why don't we get started with you giving a little background on your journey to becoming CEO at Ari Glad.
[02:38]
Sophie Wine
Yeah, so I actually worked in customer success prior to this and worked on the company knowledge base and before that was really just working customer service since I was about 14 and so I know that the customers often want to find their own Answers. And my co founder is very much technical in the AI space. So we combined those two backgrounds to create what is really the first AI tool of its kind to make sure that if customers want to self serve and look for answers in the knowledge base, that answers will be there and they can self serve successfully.
[03:13]
Podcast Host
That's great. Yeah. And you touched a little bit on it, but I wonder, just before we dive in further into the, some of the questions, could you touch a little bit more on, you know, what Ari Glad does, like who are your customers and things like that?
[03:28]
Sophie Wine
Yeah. So we basically integrate with data sources. So we integrate with your ticketing system, your Slack release notes. Basically the AI learns how your product works and the types of questions that you get from customers. And then it integrates with your knowledge base and it identifies gaps so it creates new articles if there should be one, if there's enough questions from customers to warrant it, and updates existing articles, merges, duplicates, basically just cleans up your knowledge base and builds it out where it needs to be.
[04:01]
Greg Kilstrom
Great, great.
[04:02]
Podcast Host
So let's dive in here. And I want to start by talking about the importance of a comprehensive and robust knowledge base and its impact on customer satisfaction. So could you speak to that a little bit? Why is the knowledge base so essential for providing excellent customer support?
[04:23]
Sophie Wine
So there's kind of the, I guess, qualitative and quantitative answer. So I would say from, from both of our experiences, I'm sure we've both been on the consumer side where we have a product and it broke or it's not working or what have you, and then you're trying to find that answer online and maybe you find what should be the right article, but it has the incorrect information or you just can't find an answer. And so you kind of have to go through the rigmarole of reaching out. I've definitely experienced that at least. And then there's more, the quantitative side, which is that there's been quite a lot of research into this space that has produced a lot of data that supports just how critical knowledge bases are. According to a Forrester study, 81% of customers look for their own answers first. So they're actually seeking to self serve before they reach out to support. And there's just a lot of research also supporting just how hard it is to be in customer success and support today. You know, customers have really high expectations. A Zendesk study came out recently that said around 50% of customers feel stressed and exhausted after interacting with support, which is not fair on support. They do an amazing job, but people are just stressed out. 53% of Americans experience high levels of stress on a daily basis. So it's often just the last kind of straw for a lot of people. And so for us, we really just wanna make sure that again, we're reducing just how many customers are reaching out in the first place. Customers are happy and customer success and service has more bandwidth. So it's really a win. Win.
[05:57]
Podcast Host
Yeah, I mean it makes sense because there might be other reasons you reach out to a company, but when you're reaching out to customer support, I mean, generally speaking there's an issue, if not a real problem and sometimes a very pressing problem. So, you know, I know personally and I've said this plenty of times on the show before, I'll do almost anything to not pick up the phone and call someone. So I'm one of those people that self service just, I'll do anything to find it myself and, and everything like that. And I literally yesterday, not even prepping for this show, but literally yesterday, accessed a knowledge base and you know what, it was out of date and it wasn't anything near what it was supposed, like what I was expecting to see and stuff like that. And so, you know, the, the frustration is real and I, I didn't even pick up the phone and call. I just kept hunting to see, okay, well maybe somebody said something on Reddit or somewhere else. So, you know, that's. How does your platform help organizations maintain, you know, effective knowledge bases? Maybe to solve some of the problems that I just described in my own, in my own experience, but, you know, just not only maintaining but also developing in the first place.
[07:13]
Sophie Wine
For sure, yeah. I mean it's so hard to keep knowledge bases updated. I really do have to emphasize that especially for, I mean, tech companies, but even non tech companies. But if we take tech companies as an example, we're working with some customers that are releasing new product releases twice a week. And so the product is just moving so fast and so you really, if you don't have a full time team that's working on maintaining the knowledge base and you know, replacing screenshots and making sure that every piece of text is updated, that every piece of the tool is reflected in the knowledge base, it is just going to become out of date so, so quickly and it can actually create just this habit spiral where if a customer never finds their answer in the knowledge base and they can never find it, they're just going to create a support ticket and that's going to cost more and more Money it costs I think over $13 for a B2B company for every live service interaction. So that just adds up. Super. But yeah, on the technical side of how we really do that. So in this example that you just mentioned where there was an article, there was probably the title was right and then content was incorrect. So the way that Ariagloud works is it basically looks at, okay, first of all, have any customers already reached out about this? Have they said, hey, I have this question and the support agent is able to give an answer. And if the question correlates with the topic of that article, the AI is going to see, okay, are the answers that the support's providing not correlating correctly with what's in the knowledge base? And if not, it's going to basically make those edits in the knowledge base and say, okay, there's 10 support tickets. They've all basically provided an answer that is not what this article is providing. And so we would suggest these edits and then support can basically click approve and say, yes, that looks good and the edits will be made.
[09:09]
Podcast Host
Nice, nice.
[09:11]
Greg Kilstrom
Then
[09:14]
Podcast Host
there's a cost to all of this as far as there's time and effort and even platform costs and things like that. But when organizations are looking at success, and some of that is roi, but also some of it is customer satisfaction, how do you recommend that your customers, you know, measure, measure their investment? Sort of from a, from a customer satisfaction standpoint?
[09:43]
Sophie Wine
Yeah, I mean, again, there's so many different angles to look at. So a lot of our customers will be focusing on, you know, NPS scores and making sure that those are staying high. So if you are focusing for example on tickets being handled really well every time, you're going to need potentially some support for people doing support. So agents with AI tools or even agents looking through the knowledge base, so for them to provide accurate responses, they need the resources that are going to provide accurate responses to customers. And then we have the customers that are not going to want to wait at all. They're going to want an answer as soon as possible. A lot of data has been coming out of how expectations, especially with AI chatbots on the rise, the expectations for super fast responses for support has just been increasing. And if you don't have a robust self serve operation, all of your customers that have questions are going to be going to support and it's just going to completely block you up and you're going to be hiring more and more people to try and put a bandaid on the problem and potentially invest in more AI chatbots, when really a huge amount if not the vast majority of those customers in your support channels looked for those answers first. They tried to self serve and would have been happier to begin with. I mean, I'm sure if we take your example, yes, even if you went to support after not finding answers and you had the most amazing experience, your experience with that product would have been tainted somewhat because you weren't able to find the answer in the way and format that you wanted in the first place. So it's kind of, it's not a great experience for both sides because really support can't do anything about that prior experience you had where you couldn't find that answer. And support also would have benefited if you had just found that answer and then left them to have a bit more bandwidth.
[11:44]
Podcast Host
So let's talk a little bit more about those internal teams because as you rightly mentioned, there's a lot of great customer support teams and agents working really hard. And you know, I'm, as much as I personally don't like picking up the phone and talking to people, you know, nothing, nothing against them. That's my, that's my stuff. We'll just call it that I'd prefer to self serve and stuff. But that said, you know, I'm an optimist when it comes to, I think most people want to feel fulfilled, fulfillment and their work and want to do a good job. You know, I'm sure there's some people out there that don't feel that way, but I believe the vast majority of people want to do a good job. And yet, you know, these teams are, they're, they're stretched thin. Often they're, you know, there's too much information, there's not thorough information and stuff like that. So, you know, looking at the, from the internal team perspective, you know, how does a comprehensive knowledge base help those teams and help some of those things that I touched on there.
[12:46]
Sophie Wine
So we first of all want to make sure that the customers that want to self serve were successful and are not clogging up your support channels. But if we put that aside from an agent perspective, this is kind of the hot topic in a lot of customer success and support leadership verticals where they want to use AI and AI kind of chatbots or AI response assistance to support these agents to provide the best experience and, and as quickly as possible. So, you know, if you, for example, let's take Zendesk, there's a, an app that you can now have in your support channel where when you get a question in you kind of have like a copilot where the AI will provide a what they think should be a great response to this customer question. And you can basically take it, you can make any tweaks that you want and then you can send it off. And so it's doing a huge amount of work, not only from just typing that out, but just finding all of those resources, find those bits of information, sourcing it for you and then presenting it to you. But what happens is these AI chat chatbots as well, separate from support individuals. But AI chatbots and copilots, they actually most of the time need to be connected to your knowledge base. What they really need at the end of the day is an accurate and reliable data source that is up to date with how your product works. And if you don't have that, you're always going to be stuck with either shoddy results from these expensive AI tools that do have a ton of potential but need that kind of basic knowledge and data sources and just playing catch up constantly in the knowledge base and just basically realizing a little bit too late that there's incorrect information because these AI response assistants are not providing the right information. So it's kind of a double, I guess, a whammy of, you know, if you don't have an accurate knowledge base, you are having more people reaching out in the first place, clogging up your support channels. And then even if you are kind of investing in your agents, making sure that they have access to resources and they have access to AI response assistance or what have you, again, if you don't have an accurate knowledge base, it's going to be really hard for them to do their job to the best of their ability.
[15:06]
Podcast Host
Yeah, yeah. And I think there's a few things in there that you touched on that you could use as measurements. But you know, similar to the question that I asked from the customer perspective, it's like, how should an organization measure this? And you know, is it all looking at customer satisfaction? Are there internal measurements that you can use to see things as well?
[15:29]
Sophie Wine
So there's been a couple of different ways I would say customer satisfaction. I would also say there is the ability to see how popular a article is. So if an article was really popular and then it kind of went down in value and now the AI has come in, Arglad has updated your knowledge base and you're starting to see the traction build and build over time within your knowledge base. That's a huge kind of direction of success. And then of course, reducing the amount of support tickets in general, depending on what knowledge base you use, we can actually play around a little bit with the tagging and making sure that we can see that correlation of customers are looking for this question for answers to this question and they found that answer in this article. And so yeah, there's a couple of different ways that you can see that success. And then of course if you're really just looking at agent success and reducing the amount of time you're spending on those support tickets, you're looking at investing in an expensive AI copilot which again actually work really, really well. You are not going to see the results that they're promising and the full potential of those unless you have an updated knowledge base. So that's also a huge parameter for success. So taken altogether, it's really just kind of supercharging your entire support to vertical. But individually it's showing, I would say at the beginning it's really just showing that customers are able to find their answers in the knowledge base and those articles are being really heavily used.
[17:00]
Podcast Host
Yeah, yeah. So even, even things like, like time to solution or is that you know, kind of. Yeah, yeah, got it. Okay, you touched a little bit on, on AI in a few different ways but I wanted to ask just specifically how Ari Glad is utilizing AI and if you could talk a little bit about that.
[17:20]
Sophie Wine
So from a technical perspective and I'm this is going to be the dumbed down version because I'm actually not technical, this is how it's been I've learned through the ringer of being a non technical person in an AI world. Basically let's imagine we bring on a new customer, we integrate with their Zendesk their notion Slack channel. It's basically going to be looking for in the Slack channel questions and answers and then same thing in the Zendesk it's be looking for okay, where is a question and where is an answer? And then once it understands that this is the question, this is the answer, where can we now correlate that data has this information been provided consistently? And so it's going to be looking at that either across channels or maybe there's just a ton of those answers provided over and over in just Zendesk. Either way it's going to be looking to make sure that this is not just a one off answer, that this is actually really can be seen as the source of truth for, for this data that we're looking to embed in the knowledge base. So once it's validated that it's going to Take those answers and it's going to look at your knowledge base and it's going to say, okay, we have all of these answers. These are all answers to questions that have come up enough where there definitely should be data for these in the knowledge base. And so it's going to find those gaps, it's going to create new article, it's going to update existing articles, and it's going to do that on a, you know, either weekly basis or near daily basis.
[18:54]
Podcast Host
Got it? Got it. So we talked a few different ways about measurements and seeing improvements. What improvements have you seen that might be closely tied to using the AI for analysis and things like that? What could customers expect from that?
[19:16]
Sophie Wine
So we've seen a couple of different ones. We're actually working on some case studies to basically show, like you said, what is that one consistent piece of data that you can look to to see that this is working. But something interesting that we've seen is some of the customers that have brought us on had very high expectations for ROI themselves. And we didn't even realize this. And it would be, you know, a few months later after using us, and they would come back and say, look, we were really suspicious and we had really high expectations for, you know, these are the parameters that Ari Glad needed to help with and meet. We were going to continue. And they, you know, we did meet it or exceed them. And so one of them that comes to mind is an organization that basically wanted. They had an individual that was basically in charge of maintaining the knowledge base. Not only the knowledge base, but maintaining the back end of the kind of call center and support channel and all of the data that the support individuals were getting as they were providing answers to customers. So she had just a huge job of making sure that everything was updated at all times. And they wanted to reduce the amount of time she was spending on this in half. So they wanted a 50% reduction in time spent on this. And Ari Glad was able to exceed that. And so basically what she. All she needed to do was log into Ari Glad, look at all the suggestions, you know, ask as you're looking at a new proposed article. You can make any tweaks if you would like, but you're basically approving it, you're sending it out. And so that just reduced the amount of time she was spending on this by, you know, a crazy amount. And they were left with a really, really updated knowledge base. And that organization ended up wanting to roll this out for multiple different departments because it was so successful. So that wasn't Potentially that wasn't exactly the data metric that we were thinking would be the utmost importance, but now we've added it to our roster of different ways. You can see how this is working for you.
[21:17]
Podcast Host
Yeah, yeah. And so, you know, I, I work with a lot of companies on various, you know, AI implementations and there, there can be some challenge. There's lots of benefits, of course, potential benefits, many realized benefits as well. But what are some of the challenges that businesses may face when, you know, AI sounds great to solve every single problem, but, you know, often, often there's some challenges in integrating IT and integrating systems and all those kinds of things. So, you know, what are some of the common challenges that you see and you know, what are, what are some ways to, to overcome them?
[21:58]
Sophie Wine
So I would say on our side, one of the challenges that comes to mind is just not having enough data or not giving access to enough data. You know, the AI, especially for rai, has very high standards as to what data makes the cut to be integrated into the knowledge base and be proposed as a change. And so if we are limited as to how many support tickets we get access to, or if, you know, basically if there's just not enough data, the AI is going to be kind of crippled in its ability to do that to the fullest extent. But we now know what that threshold is. So usually what happens is we can already tell that there's not going to be enough data. So we basically just let the customer know and we work together to make sure that we do get enough access on the other side. I would say, honestly, and this is not to toot our own horn necessarily, but I went to a conference recently for a large ticketing organization and they were really focusing on the AI benefits, you know, the AI bots or co pilots or what have you. And one of the interesting challenges I could just see as they would talk about how much time it's going to save and just how helpful this would be to reducing time spent on tickets or success rates. But the knowledge base still needed to be updated. And I would say, you know, yes, okay, we focus on knowledge bases. I'm a little biased, but I would say this really just comes down to data sources, which even speaks to, you know, if our data sources aren't big enough or if a copilot's data source isn't organized into one space, that is going to be just a kick in the pants to the AI, to be frank, it's not going to be successful. You're not going to be able to see it work at its full capacity. And so I would say having a data source prepared and ready for the AI before you implement is just super important because otherwise you're going to be paying for this AI tool while you're getting the data source together and the AI is not going to be able to work properly.
[24:06]
Podcast Host
Yeah, yeah, I totally agree. I've seen that time and time again. That's one of the things that I recommend to everyone. It feels like it's kind of delaying things because you got to get the data house in order, so to speak. But yeah, to your point, when it works, when you plug it in, so to speak, it works and it works a lot more quickly. So it's worth that setup. Absolutely. Well, to kind of close things out here. And it's been great talking with you about this. Wanted to get your thoughts on future trends here and the future of AI and customer experience and things. So what trends are you seeing that are going to influence AI's role? And some of these things that we've been talking about, the customer support and customer experience.
[24:59]
Sophie Wine
Yeah, I would say that the trends have been pretty clear that organizations want to implement AI to take care of the more basic questions for customers. I would say a trend that I'm starting to see a little bit more is that organizations want to elevate AI's ability to handle a little bit more complex questions and also come across as a little bit more human. A lot of organizations are even asking us how they can change the tone or how the AI is coming across and how they can really kind of mold the AI to be, in a sense, like a customer support agent on their team with the same type of vernacular and way of coming across. So I found, I found that quite interesting. But I do think that there is then on the other side, organizations that are waiting for the AI to get better because they don't want it to be associated with their team specifically. They want there to be a very direct line of this is AI, and we are not completely behind them quite yet, but they will get there. So I think, yeah, I'm definitely seeing in the future that as AI continues to get better and those data sources continue to grow and they can really shine. Because I think AI in a lot of organizations where it's, you know, if you implement a chatbot and it was disappointing a lot of the time, it's just not great data that it had access to. So I think as those gaps close and AI just really is able to shine and grow to its full potential, you're going to see organizations become more comfortable with the AI, really taking on more of a, I guess, human element. And yeah, I'm looking forward to seeing how that goes.
[26:51]
Greg Kilstrom
Great.
[26:51]
Podcast Host
Well again, I'd like to thank Sophie Wine, CEO of Ari Glad for joining the show and sharing her expertise on knowledge bases and AI to enhance customer support. You can learn more about Sophie and Ari Glad by following the links in the show notes.
[27:06]
Greg Kilstrom
Thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.greggilstrom.com. that's G R E G K I H L S t r o m.com While you're there, check out my series of best selling Agile brand guides covering a wide variety of marketing technology topics. Or you can search for Greg Kilstrom on Amazon. The Agile Brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay Agile,
[27:56]
Tech Systems Promo Voice
The agile brand.
[28:05]
Liberty Mutual Spokesperson
And Doug there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty. Even if it means sitting front row at a comedy show.
[28:15]
Sophie Wine
Hey everyone, check out this guy and his bird. What is this, your first date?
[28:18]
Liberty Mutual Spokesperson
Oh no. We help people customize and save on car insurance with Liberty Mutual together. We're married. Me to a human, him to a bird. Yeah, the bird looks out of your league. Anyways, get a quote@libertymutual.com or with your local agent.
[28:30]
Sophie Wine
Liberty Liberty Liberty Liberty.
[28:34]
Tech Systems Promo Voice
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[28:39]
Sophie Wine
the way to to.
[28:40]
Tech Systems Promo Voice
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