95% of Support Queries Resolved by AI Customer Service in the Era of AI

95% of Support Queries Resolved by AI Customer Service in the Era of AI

In this episode, Fred Schnell hosts Declan Ivory, VP of Customer Support at Fin, to explore how AI is transforming customer service. Discover how achieving an 83% resolution rate without human interaction is possible, what pitfalls to avoid, and how to shift your mindset toward scalable, value-driven support.

Key topics:

• The reality behind Fin’s 83% AI resolution rate and how it’s achieved
• The importance of customer experience over pure automation metrics
• Challenges faced during AI deployment and how to overcome content quality issues
• Measuring success: Customer satisfaction scores and feedback mechanisms
• The evolving role of support teams and mindset shifts needed for AI adoption
• How AI reshapes the support workforce, costs, and customer loyalty
• Critical factors for starting AI integration successfully
• The significance of quality content and conversation design in AI support
• Future trends: From cost-cutting to value creation in AI-driven support

Resources & Links:

• Fin (formerly Intercom)

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Transcript

SPEAKER_01 0:00

Modi acknowledges the traditional owners of country throughout Australia. We pay our respects to elders past and present and acknowledge Aboriginal and Torres Strait Islander peoples as the first peoples of this land. Welcome to the Modi Playbook. If you've ever felt like your systems are technically in place, but somehow still feel chaotic behind the scenes, you're in the right spot. This is where we share what we're seeing, what's working, what's not, and the lessons businesses learn the hard way. Think of it as practical strategy straight from the trenches. Let's dive in.

SPEAKER_00 0:37

Welcome to the Moti Playbook. One shift, one system, one measurable improvement. I'm Fred Schnell, managing director here at Moti. Artificial intelligence is probably one of the most talk about topics everywhere you go at the moment, whether that be the pub or the boardrooms. Every software platform now has AI features. Every business is trying to work out what's real, what's hype, and where this technology can actually create some real value. One area where AI has very quickly moved from experimentation to reality is customer support. Today's guest is someone sitting right in the middle of that all. And he has taken it to the point where 83% of support tickets are resolved by AI. No human interaction. What I would like to understand from our guest today is first of all, how is this possible? Whether it actually works as well as it sounds, and what it means for a business and the teams thinking about making the same move. I welcome Declan Ivory, VP of Customer Support at Fin, formerly known as Intecom. Declan, for people hearing your name for the first time, do you mind giving a short introduction to yourself about Fin and your role at the organization?

SPEAKER_02 1:54

Thanks, Fred. Very uh good to be on the webinar today and talking a little bit about our journey from an AI perspective. So I'm Declan Ivory, I'm VP of customer support at Fin. Fin is a company that has developed an AI customer agent called Fin, but also has a help desk environment branded Intercom. And the combination of AI agent and help desk really deliver uh exceptional customer support and experience for our customers who are using the platforms. I've been at Intercom just over four years. The majority of the time has been really driving out what we initially called an AI-driven customer support strategy. Uh, we've now kind of rebranded an AI-first customer support strategy, but a bit happy to delve into how we've gotten there, some of the challenges we've met along the way, and you know what it looks like today, and hopefully talk a little bit about the future as well.

SPEAKER_00 2:50

Sounds good. So, Declan, I mean, let's start with the obvious question, right? 83%. I mean, that's that's a huge number. So it's where where's the catch? Does it actually work as well as that statistic actually suggests?

SPEAKER_02 3:04

Yeah, I mean, you know, as you say, 3%, it's a big call. Uh, you know, what is the customer experience like? And that's where we're going to ground this. Like ultimately, driving that automation rate is not about automation for the sake of automation, it's really around having a conviction that you can actually deliver a better customer experience through the engagement with a high-class AI agent. And that's what really has allowed us to you know drive that high level of automation because we've put a lot of thought and effort into how do we actually deliver a really valuable customer experience through that interaction. So this is not about like, and I hate these terms, like deflection and containment, et cetera. This is actually about delivering a better customer experience at the end of the day. And that's you know why why we've been able to drive this kind of 83% automation rate, because we're really paranoid about the customer experience. And in fact, you know, our ambition is to actually grow beyond that. Like I have now kind of put in place a strategy to get us to 95% automation rates. So we want to actually you know move beyond the 83%. And like what's limited is like because uh you know, you asked the question like what's the difference between 83 and you know 100%. And if you look at the type of activities, like they really fall into two camps. One camp is where there's an element of support conversations that we can automate if we have the right data connectors and access to underlying operating systems and business systems, and we can actually complete work on behalf of customers. So, for example, in the FIN world, we call that FIN procedures. That's where you're actually carrying out work for customers. And then there's still some elements where we can improve our content. And then there's a third, you know, our part of that second bucket is also what I would call a long tail of edge case conditions that may be worth automating or may not be worth automating. It really depends on can we drive a better customer experience or not? So we're being very kind of pragmatic around like this is not like a around automation for automation's sake, it's not about e hitting some artificial, you know, 100% goal. It's where we can actually add value to the customer experience.

SPEAKER_00 5:03

Gotcha. And so, how how do you define like a resolved case? How is that kind of defined in your in your statistics?

SPEAKER_02 5:12

Yeah, so again, a really good question and one that comes up quite a bit. So at the end of the day, if Finn answers a question for a customer and the customer says, uh, I've had a really good experience, that's one count for resolution. If the customer has engaged with Finn, Finn has provided an answer, and following the customer receiving that answer, they drop from the conversation and don't re-engage with us through some other channel or through some other conversation, then we also count that as a resolution. Gotcha. If Finn hasn't provided an answer, you know, we obviously clearly don't count that as a resolution. Or if the customer asks to talk to a human, we count that as a resolution. And again, just part of our philosophy around how Finn uh actually works for our customers who are using Finn, if Fin answers, let's say, three questions out of four that the customer has, and on the fourth question, the customer says I want to talk to human, then we discount that as a resolution that has you know gone to a human. So irrespective of the fact that Finn has added value through the life of that conversation, you know, we say, okay, it's gone to human, it's not a fully automated resolution.

SPEAKER_00 6:19

Okay, so that makes sense. So so does that mean that the 17% is when it gets escalated to human, or what happens with the the last kind of remaining 17% that they say?

SPEAKER_02 6:28

Yeah, that that is effectively escalated to human at this point in time. So I'd say when we look at that and break it down, it's a combination of stuff where we've got to complete work on behalf of a customer, so we need to access some underlying operating system or business platform. So that's one element of work, and then there's another element of work where I say it's a long tail of uh educational issues, and there's also some other, you know, minor information gaps. It's always hard to get content and knowledge 100% accurate across the entirety of your business. Of course. So every so often we do surface some gaps that you know we need to fill and address.

SPEAKER_00 7:00

Makes sense. So let's talk a little bit about the journey to get to the 83%, right? What did failure look like?

SPEAKER_02 7:10

Obviously, yeah, it does take a lot of effort to get to 83%. Yeah, uh, don't want to undermine what's involved in that. Like it was a long journey and a lot of effort, particularly as we were trailblazing at the time, there was no blueprint or kind of best practice. So we were trying to evolve that as we went along. But there were a couple of things that you know didn't go well for us, probably the best way of describing it. So, one example is around the structure of your content, right? So we have a situation where we have multiple price plans that our customers can be on. And one thing we discovered not long after launch was that our knowledge articles weren't clear enough to specify the particular answer that will be relevant for a customer depending on the plan they were on. So all of a sudden, we were potentially giving incorrect information from a pricing point of view, which is pretty critical. Yeah, that's not a great experience. So we actually had to kind of back off using Finn for pricing questions for some time until we really thought through how can we structure our content in a way that were removing any possibility that fin won't actually present the right information to the customer based on the pricing plan that they're on. So that's just that's the one example of where you know we definitely got it wrong. We exposed our pricing questions too quickly to Finn. And yeah, we had a couple of situations where we just gave the customer the wrong information. But again, I think if we thought through it or if we maybe tested a little bit more easily, we we probably could have addressed that. Um, second one is a really interesting one. Like we began to analyze conversations after we deployed Finn, and we discovered that a lot of our customers were interacting with Finn as if it was a typical chatbot environment. And if you think about the chatbot world, like you actually tried to be you know very succinct in how you describe whatever your problem was. You try to maybe use keywords to get it to do something for you, right? Uh, and if you interact like that with a large language model, you know, and conversational AI, you don't really get the best experience. So we actually just we changed our prompt to our customers basically, encouraging them just to treat it like a natural conversation, like just talk to the agent. And when we did that, we straight away saw a 5% uplift in uh customer satisfaction with the agent technology. So again, that was a real eye-opener for for us. You can't legislate for human behavior, and like while we were all you know buzzed about this new technology, and we thought you know, people will interact with it in a very conversational way. The reality was they didn't, and you know, we really had to think carefully about how do we prompt them to interact with it in the right way so that they actually get the most benefit out of the experience. That was another kind of you know uh misstep we made. We we didn't think about that human behavior piece uh well enough.

SPEAKER_00 9:44

Right. You mentioned customer satisfaction in your kind of explanation. So, how how do you measure that?

SPEAKER_02 9:51

So there's two ways. We obviously want to give the opportunity for customer to provide us feedback. So we we have a CSAT survey that historically would have issued at the end of the complete conversation with the customer, but now we treat the FIN phase as a phase of that interaction where we also want the customer to be able to give us a survey. So after we launched FIN, we got the product team to change so that we could actually issue a CSAT survey at the end of the FIN phase. So that was our first kind of view into what was the customer experience like with Fin. But again, that's reliant on someone responding to a survey, and we all know not that many people will respond to a CSAT survey.

SPEAKER_00 10:26

So must just tap out, right?

SPEAKER_02 10:28

Yeah, unfortunately, yeah. But at least it gave us some lens early on to kind of know we were at least on the right track uh in terms of that customer experience. But I was pretty paranoid around well, how do I definitively know for every single conversation whether we've done a good job or not? So internally we conceived this idea of a customer experience score. So we worked with our research team and we actually built out a set of probably six characteristics within that. Like, so for example, we use AI to generate an inferred CSAT. So, what would the how would the customer score this conversation if they had responded to a survey? We also inferred the quality of the resolution. Did we actually resolve the issue that the customer presented with? You know, we looked at some more deterministic things like did we hit our first response time? Was there a bug associated with this ticket, et cetera, with this conversation? So we actually built up this concept of a customer experience core, which we could then rate every single conversation against these attributes and make a determination as to whether it was a good experience or not for our customers. And that kind of customer experience score is now being built into the product. So if you get Fin today as uh part of the product, you get this customer experience score and it's it's available now to all the customers. But that was kind of my paranoia around like I really want to know it's just a good experience for every single customer. And we put a lot of time and effort into building it out, but it paid huge dividends and that we were very easily then able to surface where there were potential problems or issues in terms of that experience. It could be down to like for a particular topic, we may not have had all of the right knowledge or content uh available. And again, that that surfaced pretty quickly as we we ran through this process of building out the customer experience score. Gotcha.

SPEAKER_00 12:02

Very nice. And I remember that you you saying that you've seen cases where um the customer actually thanks the AI agent for their response, right? So, but how is this possible? Because I mean, to me, that seems a bit counterintuitive, right?

SPEAKER_02 12:17

Yeah, I mean it it is intriguing, but again, it's down to the kind of the human behavior piece. Like if humans trust what they're dealing with, it's almost like they forget about the fact that it's an AI agent. And that trust is really around speed of interaction, quality of the interaction, quality of the resolution provided. And if that's all there, then they're almost quite naturally saying thank you. And they're almost forgetting that it's uh you know, it's a an AI agent, not a human. Yeah. And I think when when you get that level of interaction and engagements, to some extent, then you know you're on the right path because you know you've built up a lot of trust with your customers if they're willing to go to that kind of effort to say, yeah, thank you, Finn, or whatever. Like it's and it's not just us seeing it, like a lot of our customers are seeing the same thing uh and and referencing that when they cross that boundary, they kind of really know that they're it's they know they've done something right. Yeah, exactly.

SPEAKER_00 13:10

So do you think that the customer behaviors actually have changed over time?

SPEAKER_02 13:15

I think customer behaviors are changing because a lot of us are dealing with AI tools now on a personal basis as well as on a work basis, and all of a sudden I think people are a little bit more comfortable with this conversational interface and they're a little bit more comfortable with that level of engagement. So that has certainly helped things quite a bit, like the the wider environment where people are all of a sudden beginning to deal with AI tools in one form or another and have kind of embraced that conversational interface. So that's definitely helped quite a bit in terms of getting people over that hump of dealing with AI agents.

SPEAKER_00 13:47

Well, I guess also, I mean, we live in a world where people kind of expect everything instantly, right? Everything's instant. So maybe customers just really because more fast and accurate answers as opposed to you know just humans just going, oh, let me just check this for you. And um, so that human interaction becomes kind of less relevant to a certain extent, right?

SPEAKER_02 14:10

Yeah, I well sorry, maybe less rather than not the way of putting it. I I think the AI experience is really good. And you know, for the the vast majority of what a customer wants to get answered or get completed, it's done autonomously by an AI agent. When they get through to a human, then that touch point is actually much more critical than it was in the past because it's all about now handling the more complex, nuanced, critical activities that the customer has. So it does change the nature of human support work from a support point of view as well. Like it's it's really important that you recognize that, that it's not the same type of work coming through through human support team. It's actually quite different, and you need to think differently then about the team. So that's definitely one of the dynamics here. Like the human phase is almost more critical than it ever was, albeit it's much lower volume of the overall interactions.

SPEAKER_00 15:04

Which kind of leads me to kind of the elephant in the room, right? So we've seen major technology companies like Salesforce publicly announcing that they're reducing the support headcounts and replace them with AI. So I think a lot of business owners sitting here and listening to this are wondering: is this really where things are heading? So, Declan, if AI is resolving 83% of your queries, uh what what happens to the support team, right?

SPEAKER_02 15:33

I thought a little bit about my own support team, then I can talk about some kind of wide wider trends that I'm seeing, like talking to customers and prospects. So in our case, like we have seen huge growth in our business. So we have seen probably a 300% increase in demand for support over the last three years. Wow. Yeah, and like without AI, that would have meant phenomenal scaling from a human support point of view. Like we would have you know probably have to more than double the size of our team to accommodate that growth. So we haven't actually downsized our team. And in fact, we're marginally larger than we were at the start of this project, right? Just because of the growth of our business and some new roles that we put in place. So the big benefit for us is that we've had huge cost avoidance, right? Because we haven't at the scale of the human support team. But at the same time, we're now delivering a really good customer experience irrespective of the scale. Because to some extent, for the stuff that Finn is handling, like even if you were seeing a huge spikes in volume in that, it just absorbs it, it scales to take and absorb all that all that spike. So it's a much more stable environment from a customer point of view. Uh, even though, as I said, we haven't we haven't grown the team, but we're actually you know delivering much more comprehensively for the customer. Now, what we have been able to do is free up some bandwidth on the team. So they're not as absorbed in handling kind of conversations or tickets on an ongoing basis, and we've been able to free up some bandwidth on the team to be a little bit more proactive with our customers. So we actually encourage them to think beyond the issue that the customer presents and actually spend a little bit more time with them. And we actually call this kind of consultative support. So sometimes it's in the moment when the customer engages with us. Sometimes we proactively go out to the customer and have a consultative support engagement with them. And all of a sudden, we're adding more value for that customer in terms of how they're using our product and the value they're getting out of it. That's also a mindset shift. But we we made that very intentional decision that we wanted to add more value to the customer through the humans that we had in our team. And this was definitely not about automation for automation's sake, it was about giving the best service possible through the Fin phase and through the human phase, adding as much value as we can to that customer with whatever then got presented uh beyond FIN. And that's been our mindset from day one.

SPEAKER_00 17:46

So it's kind of it's it's so you you you're increasing the value that you provide on both sides, the customer that really wants short, quick, and just resolve that issue for me with the AI agent, and then the more consultative side of things when it's more complex, so you're adding value there by having a deeper conversation with the customer.

SPEAKER_02 18:04

Interesting. Can just touch on some trends, like you know, because we're not the only organization going through this transformation, and many organizations are making the same kind of decision point that they really want to start using any bandwidth they free up on their teams through driving this automation to actually deliver more value to customers, and that's been quite a shift because the first year after AI kind of became available, you know, uh after generative AI became a thing, people focus a lot on the costs and the potential for cost savings and headcount reduction. Now they focus more on how can I actually more add more value to my customers through the resources that I have, or even if I'm not adding value through the support lens, how can I redeploy those resources elsewhere in my business to actually add more value for the customer? And that has become the primary mindset. Now, that mindset is very pervasive where customers are trying to grow their business and you know they're in a pretty healthy state. Reality is there are some businesses who need to trim costs, who need to maybe optimize how they deliver support. And yes, in those cases, there may be the opportunity to tune the size of your team, but I don't think that's the goal for the vast majority of people here. Like people are really trying to drive a better customer experience, deliver more value, retain customers longer, drive loyalty, you know, be able to scale the business. And in that mindset, the vast majority of customer support teams are not changing size, they're just maybe changing, as I say, a little bit of the focus, a little bit of the emphasis, uh moving as I say, changing the things that we're gonna do.

SPEAKER_00 19:29

Right. Yeah. So when you talk about you know the transformation at Fin, did all the people thrive in that kind of shift, or or was there some that really struggled with that kind of transition?

SPEAKER_02 19:42

Yeah, I mean, there are definitely some people who struggled in the transition because all of a sudden the work that's coming through the humans is radically different, right? There's none of this kind of very simple, mundane, routine kind of questions that you answer. And while most people embrace the change as this is actually good, because now all of a sudden I'm getting work that's far more fulfilling. I'm really getting to hone my problem solving skills, I'm really getting to know the product at a very deep level and bring that expertise to the table. There were some people who kind of said, like, this is not really for me. Like I actually prefer that old world where I had more simple work to handle. Like, I don't really want to go down that road of developing my kind of deep technical skills. So definitely we we lost a small number of people who kind of self-selected to say, look, it I don't think this is a role for me in the longer term, and you know, they uh achieved roles outside. And to some extent, when you go through transformation, that's the reality. Some people will want to buy into that transformation, will see it as valuable to them, and some others may not. So, in our case, very small numbers decided this wasn't for them. They didn't like the way that the role was changing. Vast majority have embraced change, you know, they've seen it as an opportunity to really upskill themselves. Uh, even within the support role, there's more opportunities opening up for them because, as an example, we've built a whole separate AI support team who manage Finn and you know look at things like conversation design, knowledge management strategy, systems analysis around like how we're integrating real backend systems, and people are moving into those roles as a natural progression. Even within our standard support team or human support team, we have a specialist role and an engineering role. More and more of a people are actually moving to the engineering role because they're handling more complex work. And we're enabling that with a program called TSE Academy, where we're putting in the effort to actually skill people in the right way to make that transition. So, again, these are all kind of some of the implications you need to think through in any kind of project for AI deployment. It's not just about the technology. You've got to think very critically about the people. You know, how can you make sure that you're allowing them to grow and foster in this environment as well, and enabling them to be successful as you make this change and the nature of the work that they undertake changes as well.

SPEAKER_00 21:49

So, really saying like the support role is really not disappearing, but it's actually evolving to something completely different.

SPEAKER_02 21:56

Absolutely. Okay, it's it's really a much more value proposition. And in fact, some of the work now being done in support is almost more akin to what would have been traditionally called success work. And the problem with your customer success organizations, they tended to be focused on your top-end customers. There was always a large portion of your customer base where it was difficult to give them a customer success experience. Now with support, we can do that at scale. So we're undertaking some very specific programs and activities that historically would have been considered success activities, but we can do them at scale again because we're enabling the team via the great automation rate that we're driving with Finn. And also we're using AI to provide some of those insights to know where we need to go at the customers, where we can do that outreach with best impact.

SPEAKER_00 22:41

Okay. Now you touched on knowledge base, knowledge management, and you know, the one key point when it comes to AI is it's only as good as the data behind it. So if a business is messy underneath, I guess the AI just kind of systemizes the chaos, right? So is that what you see as well?

SPEAKER_02 23:02

Absolutely. Like it's a real case of garbage in, garbage out. Like there's no doubt about that. It applies here as much as it applies anywhere. Like, no matter how good a large language model or any kind of other model is, like it can only really work with the information and content that's, I say, relevant for your business and relevant for your customers. And if that isn't in a good place, then the answers that the AI provides isn't going to be good for your customers. I have a couple of examples of that. So a really interesting one. Said, Yeah, we just opened up Finn to our knowledge center as it was. We didn't do any sanity check on the quality of the content, you know, uh, how up to date it was. And we actually delivered a really bad experience. So we just turned off fin for two weeks, spent a bit of time updating our knowledge center articles, you know, make sure they were kind of you know uh up to date, relevant, etc. Opened up Finn again, and all of a sudden they achieved, I think they achieved something like 35% resolution out of the box, like uh and again with a good customer experience. So that was one example of where you know they had not done any kind of review or audit of their content, uh, and you know, they hadn't taken the right steps to up-level that content, and it was a bad experience. So that's one really good example where if you put the effort in up front and you think about the structure of your content, the scope of it, and you think about uh how up to date it is, and then it's not a one and done thing either. Like you've got to build the process of managing that content on an ongoing basis. So, one example that in our case, we've designed a new product introduction process where we work very collaboratively with our engineering teams, product marketing teams, you know, enablement teams to agree a consistent way of talking about a new product or feature. We make sure that it gets fed into Fin and it gives Finn the best opportunity possible to answer questions when that product is launched. And we had this aspiration of we would have content good enough at time of launch to always get a 50% resolution rate, or typically getting 70% plus resolution rate on the day of product launch, again, because of the rigor we've put around kind of knowledge and product management. Uh, sorry, knowledge and content management. So that's absolutely critical. I referenced the other kind of challenge as well, like it's not just about knowledge, but the conversation design piece is really important as well. Like if you introduce an agent into that life cycle with the customer, at one level it it's it's another level of friction if the agent doesn't answer the question. So you got to think quite critically. So, what does it feel like from a customer point of view to deal with an agent? Then maybe the agent having a hand over to a workflow, workflow handing over to a human. Like you got to think critically around is that as good as it can be in terms of maintaining context to that um journey uh and ensuring that you're enabling whoever does get that from a human perspective has as much information as possible to allow them to add value immediately to the customer, like they're not going through another level of triage. And that all has to be designed in, you've got to think critically around what is the context that I need to make available. And you know, we have built capability within Fin to enable that thing called Fin attributes, where you can actually pull the context across so that a human has a lot more information available to them when they engage post-fin. So again, just thinking about that conversation design is is really critical as well. Uh, you know, and if you don't get that right, it is, you know, as you say, the it's only as good as the information it has, or it's only as good as the conversation flow that you designed, yeah. So it's you know uh that's that's where you need to put the effort. Yeah.

SPEAKER_00 26:35

I think it's I mean, that's it's a really, really important point here, I think, because I think there's quite a bit of a misconception in the marketplace that well, it's artificial intelligence, right? So I I just load my knowledge base into that thing, and then magically like that thing is going to respond to to all the questions, it's gonna learn, right? It's gonna just adapt and and change. And so I think that's a really, really critical kind of point that no, you need to think through what the right content is. So it's more quality over the quantity that you put into fin and provide to your AI agent to really, you know, work with. Plus, then you need to design well, what do you want Fin to, how do you want your AI agent to actually interact with your customers, right?

SPEAKER_02 27:17

Yeah, absolutely. And to that point, the quality over quantity, like if we go back to m mistakes made, that was actually another mistake we made. Actually, we assumed on day one it would be better to provide more quantity than quality from a knowledge and content point of view. And we discovered quite quickly that that could lead to ambiguity of answers, it could uh enable hallucination, etc. Like so we actually backed off quite quickly to say, you know, it's quality content rather than quantity, it's much more important in this LM world.

SPEAKER_00 27:46

So let's just assume like there's a business out there listening to this podcast. What's your honest checklist? What do you need to put in place to get started with with AI?

SPEAKER_02 27:56

So I think the the very first thing above everything else is it's not AI for AI's sake. You've got to know what is the business problem you're trying to solve or the business outcome you're trying to drive. Like that is fundamental for me. Because other than that, all you go through is kind of experimentation, proof of concept, but no real view of are you actually driving business value. So knowing that problem or business outcome, that's number one, right? Um the second thing that's really, really important is that this is about driving a much better customer experience, right? Ultimately, I think that should be your goal. Like even if you're trying to drive cost savings, etc., it shouldn't be at the expense of customer experience. Like this is an opportunity to achieve whatever your goal is, but to actually deliver a better customer experience. Um again, you know, you really got to focus on that and understand that and be willing to make sure you're measuring it and monitoring it and responding to it. And that means putting in place certain processes around ensuring that you're looking at the quality of that customer experience. We've mentioned the the content as well, like that's pretty critical, you know, for as a checklist. Like, have you spent time to understand do you have content and knowledge across all of your uh products and services and across all of the associated products? Is it readily available so you can jest it into your agent? Is it up to date? Is it accurate enough? Like, is it structured well enough? Like so that it can be read by an AI engine. So that's the other thing. Like, think about an AI engine reading a document versus a human reading a document. You will probably structure some of your documents differently if you think about it's a machine reading it versus the human. Like an example that a very simple example is FAQs. Like in an FAQ list, you can have question and you can say yes or no as your answer to the question. And visually, a human will uh understand that and associate the yes or no with the right question. There's no guarantee that a machine reading it, where there's multiple questions, each with a yes or no answer, will automatically equate. So you're better off calling it out like does your product do X? Yes, my product does X, or no, my product doesn't do X. So just changing that is it's just it sounds simple. Yeah, but even that simple thing can avoid a whole lot of ambiguity, a whole lot of hallucination, a whole lot of quality issues. Um ensuring that you know when you're thinking about going on a journey, what are you going to do in terms of the context of the handover? Like what you need to have available to hand over to a human when an AI agent uh needs to escalate. So again, thinking through all that up front actually sets you up for success and allows you to actually deploy in a far more effective way.

SPEAKER_00 30:30

Now, I mean, we we've talked about it quite a bit already, but the one thing I would like to know from you is in your experience, what tends to go wrong when a business rushes into AI supports?

SPEAKER_02 30:43

I think I mentioned already it's really not knowing the business problem you're trying to solve or the business how come you're trying to drive. I think that is absolutely the biggest mistake. And it's where, like, I think there may be a McKinsey report that says X number of AI proof concepts are failing, right? And when you actually scratch through the surface and you look at the the information that's in that report, the main reason why most of them are failing is they don't have a clearly declared business outcome or problem to be solved. It is very much AI for AI's sake, and I think that's the biggest mistake that you can make here.

SPEAKER_00 31:16

Which then means it's it's not a technology problem, but it's more a business process problem.

SPEAKER_02 31:21

It is the business, yeah. But sometimes like if if your first foray here uh kind of doesn't work out well, people immediately jump to a different technology and then to a third technology. I've I've I've known lots of organizations that try three or four different technologies, and the issue isn't the technology, the issue is they haven't defined exactly what they're trying to achieve, they haven't set out those uh objectives up front. I did mention around you know the content and knowledge, you know, a big mistake. And I gave the example of the customer on stage basically saying, Yeah, you know, we gave a terrible customer experience so uh because that they hadn't actually put the time and effort into the content and knowledge. That that's the other big mistake that people make. They assume that their content is good enough. Uh and at times, as I mentioned, right?

SPEAKER_00 32:05

That there's the perception of well, it's AR, it's artificial intelligence. You just throw that database in and it's just magically going to work with it, right? Um, yeah.

SPEAKER_02 32:15

Oh, absolutely. Yeah. So content readiness is how I'd characterize characterize it. Like that's a big kind of area where people need to focus. Yes.

SPEAKER_00 32:23

So Declan, when it's done really, really well, what what does the customer experience feel like from the moment someone really types in the first message all the way to a positive resolution?

SPEAKER_02 32:35

So I think that like this is really where the magic is. Like so the ideal customer experience, like the the the agent is recognizing up front the context of that customer and the intent of that customer. It's immediately clarifying via questions if necessary, if there's something that's just not clear. And it then knows enough information to either find the answer straight away and produce it, or carry out the task if it's a task. And if not, AI then knows exactly who to route it to. Like that's what the ideal customer experience is. And you know, when that magic happens, it's available 24 by seven, multilingual support. It's kind of infinitely scalable. So it doesn't matter whether it's Super Ball day or whether it's kind of the lowest day of the year from a demand point of view, it's a consistent experience for the customer. And that consistency will drive the customer to come back more and more to ask more questions. And this is another dynamic we're seeing that when this works well, customers engage with support more. And some people view that well, that's a bad thing. No, it's not a bad thing if that engagement is helping them to use your product and service at a deeper level and is generating better loyalty for you as a business and you know, avoiding churn down the road. Yeah, this is exactly what you want to do. Like you want to make it easy for your customer to use the product or service, and if they need to ask questions, you want to make that as frictionless as possible. And that's what you know, this looks like when it's really, really good. It's a frictionless experience. Customers getting an immediate high-quality answer. And if they're not, they're getting passed in a very comprehensive way to a human so the human can add the value that's necessary beyond that. And that's, I think, ultimately what is differentiating an organization that has gone down this road, thought about it intentionally, and has scaled it out. They are transforming that customer experience because I think ultimately that's what this is about. It's not about technology, it's not about the flexion, containment, automation, right? It's actually about delivering a really, really positive customer experience at scale.

SPEAKER_00 34:35

Love it. Now, before we wrap up, the final question we ask every guest on Demoti Playbook. If a business owner is listening to this right now, they're curious about AI and customer support, but they still haven't made the move because they're a bit hesitant. What's the one mindset shift that you would hope they take away from today's conversation?

SPEAKER_02 35:02

What I'd really love to see from a mindset shift point of view is a business owner embracing the opportunity that AI presents to really transform the customer experience as opposed to looking at it through the lens of it being some kind of efficiency play or cost savings play. Once you embrace that mindset and you see the opportunity, I think that is a really strong spur then to actually try something. Yes, start small by all means of your testing and ensuring that you're the right customer experience, but start quickly once you make that mindset shift. And then also once you verify that everything is working as you expect it, deploy quickly as well. Like, why deny yourself the compounding value that you can get as you scale out AI? That's the one kind of mindset shift transformation. Start quickly and verify scale quickly as well. Uh, like there is no need to hesitate here, uh, is really the key point I'm making. There are lots of organizations that are beginning to do this at scale. If they're your competitors, they're actually gonna do a lot better job for their customers or your prospects as well. Yeah, uh ultimately hesitating here is not the right long-term business decision.

SPEAKER_00 36:17

I think for me, it's probably along the same or similar lines. Um, the one mindset shift that AI AI is not replacing the fundamentals of of running a good business. In if anything, I think AI is actually making it even more important. That that would be my kind of mind shift that I would like business owners to think about. Well, Declan, that was fun. Really appreciate your insights today. And for anyone listening who wants to better understand what AI in customer support could actually look like in practice, please feel free to reach out to any of the Moti team. Also, all episodes of the Moti Playbook are available on our website under moti.co slash playbook. Thank you so much, Declan, for joining us. And well, to all our listeners, hope to see you in the next one.

SPEAKER_02 37:10

Thanks, Fred. Appreciate the opportunity to be on the uh the webinar. Thank you.

SPEAKER_00 37:14

Thank you.

SPEAKER_01 37:17

That's it for another episode of the Modi Playbook. One shift, one system, one measurable improvement. All information shared in this podcast is general in nature. For tailored advice specific to your business, visit Modi.co slash playbook and book a momentum call with our team. See you next time.