Another Agentforce Guinea Pig has joined the show… She’s here to tell you if you think rolling out AI is as easy as flipping a switch, think again.From early mistakes to surprising wins, Laura Meschi, Customer Experience Manager at Secret Escapes, walks us through what it actually takes to train an AI agent that can truly support customers.We dive into why ROI isn't the best measure of AI success, how customer effort scores skyrocketed after launching Agent Force, and why CX leaders need to start simple and think long-term. Laura also pulls back the curtain on what it really takes to train an AI agent — and why you absolutely shouldn’t DIY your rollout.If you’re a B2B leader wondering how AI agents fit into the future of customer success, this conversation will hit home. Key Moments: 00:00: Laura Meschi’s AI Agent Rollout at Secret Escapes02:45: Secret Escapes’ Agentforce: Lessons from a First-Mover11:14: Surprise Wins and the Underrated Power of Human QA16:41: How Agentforce Reshaped the CX Team (Without Cutting Headcount)22:26: Guardrails, Limits, and Finding the Sweet Spot for AI Use Cases26:30: Why Clean, Centralized Data Is the Real AI Superpower27:18: Don’t DIY Your AI: The Case for Bringing in Experts29:58: How AI Improved CES and Transformed Customer Perception34:50: What’s Next: Future AI Strategies and Upcoming Salesforce Tools37:21: Reimagining CX: Using AI to Build Relationships, Not Just Efficiency40:37: Start Simple, Prioritize Data, and Train for the Long Game –Are your teams facing growing demands? Join CX leaders transforming their AI strategy with Agentforce. Start achieving your ambitious goals. Visit salesforce.com/agentforce Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org
Chapter 1: What challenges did Laura face during the AI rollout?
We were, I think, the first customer in Europe to go live with AgentForce. It's learning by doing. It's a completely revolutionary technology. We're in the very beginning of it. And it's something that you need to take as a long-term investment.
I applaud you for being the guinea pig on such a new technology. This is not really the moment to do it yourself. And I know Mark Benioff talked about this at Dreamforce this year. He's like, don't DIY your AI because Let's leverage the expertise here.
Chapter 2: How did Secret Escapes approach the implementation of AgentForce?
It took longer than expected. And one other challenge was reporting on return on investment. How successful in general was the handling of the conversations with our customers? It's not a quick win deflection journey. It's actually investing into relationship you have with your customers and tackling everything that you can take away from humans.
I'm sure you notice that the reality is far more complex than just turn it on and it's magic.
If you read through some of the transcripts, it is impressive how seamless it is and how the customer didn't even realize they were talking to an AI agent if we weren't declaring it at the beginning of the conversation.
Hello, everyone, and welcome to Experts of Experience. I'm your host, Lauren Wood. The AI revolution in customer experience is everywhere in the headlines and especially on LinkedIn. But what's happening behind the scenes is perhaps another story. What does the implementation actually look like when you move past the buzzwords into the real world application?
Today, we are speaking with Laura Miske, the Customer Experience Manager at Secret Escapes, who has recently rolled out Salesforce's AgentForce, and we're going to talk about what that implementation actually looked like and get her perspective on what really makes it work. In this conversation, we're going to explore how AI is reshaping customer experience.
Of course, it's our favorite topic on this show and really key strategies for a successful rollout and what opportunities are at play here. Laura, thanks so much for coming on the show. Thank you for having me. Hello, everyone. So AI and customer experience is often talked about as an efficiency tool.
It can reduce agent workloads, it can increase automation, and of course, deliver a seamless service. But Secret Escapes, as you rolled this out, I'm sure you noticed that the reality is far more complex than just turn it on and it's magic. Tell me a little bit about why you decided to utilize agentic AI in your customer experience and how did that implementation go?
Yeah, so the reason why we started the journey with AgentForce was mainly around trying to deflect, or at least the initial thought was around deflecting the contacts for the highest reasons for contact that we were getting through our live chat channel.
I have to say that partially one of the reasons why we got on this journey is also because we wanted to be at the forefront of the new technology. So it wasn't just about the efficiency side and the big promises around AI, but also we wanted to be on the AI wagon for everyone else. So we started the pilot with Salesforce. We were, I think, the first company
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Chapter 3: What surprising wins came from using AI agents?
customer in Europe to go live with AgentForce. So it felt really good to be at the forefront of something completely unknown and exciting like AI, but also it was a learning curve and excitement turned into realization that it wasn't exactly what we expected, but also the excitement is not over and there is a long way It's a long-term investment. Of course.
So, yeah, it's really, really fun to be working on this project where the technology is so new and it feels new for everyone. So you don't feel left out in a way. You're not alone. Yeah.
Totally. Well, I applaud you for being the guinea pig on such a new technology, but also a technology that has so much potential and also immediate potential. We're going to get into all of that stuff. But I think that, you know, with any organization, with any CX org, the sooner the better. Right.
The sooner we dive in, even if there's going to be challenges, at least we're learning and we're growing right from the outset.
Probably AI is the new internet smartphone technology revolution. So it's like being in the midst of something that is probably life changing. So it's exciting and scary at the same time.
Of course. Let's talk a little bit about the challenges that you faced. And I know so many people who are embarking on their own AI journey. I have a lot of clients in this space. There's often like, oh, I didn't expect that to be a challenge. Tell us a little bit about what you faced as you started rolling out AgentForce. Yeah.
So as I said, we started with setting up use cases for the usage of agent force based on the highest reason for contact, which in hindsight, it was the only way we could do this being so unknown and, you know, unknown territory for everyone. But in hindsight, I think we probably chose use cases that were a little too complex for So it took a little longer to build the functionality.
As it wasn't just answering FAQs questions, we started off by asking the virtual agent to actually perform some actions. So it took longer than expected. And one other challenge was reporting on return on investment, which felt a bit more complicated than what I expected. within the functionality itself.
So it took a little bit of a DIY solution to try to get an understanding of how successful the deflection rate was for the agent and how successful in general was the handling of the conversations with our customers. Yeah, I would say those two. And probably the setup process, it's fairly easy, especially if you're looking into setting up a use case that is an FAQ-based type of use case.
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Chapter 4: How does AI affect customer experience metrics?
And for anyone listening, if you want to go and hear more about agentic AI and Salesforce's journey, of Agentic AI. Check out our episode with Bernard Sloey, who's the SVP of digital customer success at Salesforce. But I think it's really fascinating to hear your take on it because it is different for every company, the types of things that you run into.
And what you were just sharing about how it was harder to show the ROI. I'd love to go a level deeper on that piece. And why is that? Because everyone's saying, okay, let's roll this out because it's going to drive cost savings. And there's It is yes. And and so I'd love to hear a little bit about what was challenging about that.
It was mainly the fact that the deflection rate we were expecting to achieve has not been as high as we expected. Partially, it could have been an unreasonable expectation to have, because, as you said earlier, you know, we thought it would be much more immediate, whereas this is something completely brand new and you kind of have to learn by doing.
So it's part of the challenge and part of the beauty of this is that you just learn as you go. So that was the challenge. And yeah, because the, um, deflection rate is not what we expected. We kind of have to try different ways of achieving and maximize the power of the technology that we have. Um,
Chapter 5: Why is centralized data crucial for AI success?
And that's also why this is such an interesting journey, because you have to kind of learn while you're on the ground. There isn't much more we could have planned around this apart from the choice of use cases. So I think it's also about changing the perception on the metrics that you want to look at to measure success. So with technology like this, probably ROI is,
Chapter 6: What are the pitfalls of DIY AI implementations?
is not the best matrix to give it justice. People could consider also the impact on CISA and satisfaction of customers in the interaction with the virtual agent. The easiness of how It changes from a menu-based bot, which is what we had before, to an LLM that can literally pick up your tone and can have a conversation with you like a human.
So it can pick up the context, it can resend the information. So it's also shifting the focus from ROI to a range of wider metrics. It's possibly something companies have to take into consideration when deciding to embark into an AI journey.
Yeah. I love that you say that. I think a lot about how the real benefit to AI, of course, there are those cost savings, but we need to play with it and tweak it and find how can we use this in a way that's both preserving the quality of our customer experience and and allowing us to speed things up. And it takes experimentation. It's not an out of the box, you just save 20%.
We need to work on it a little bit. But the real benefit here is the customer lifetime value. If we think about creating a great experience using AI and the trust it can build with the customer, I'm more interested in that metric, in what drives customer lifetime value. How do we extend that? How do we improve that through AI technology? So I think you're totally right.
ROI isn't the straight up metric that we should be looking at. There's more to it that goes beyond the simple return on investment. In another context or flipping the script a little bit, were there any unexpected benefits that you found when rolling out AgentForce?
I was impressed by how easy it is to set up the actual facility, if you're not asking the agent to perform any actions, it's literally like typing a narrative and it's like you're talking to a human explaining a new employee what they're supposed to be doing. and telling them what they should and shouldn't do and how easy it gets on with it in a way, how fast it learns.
And if you read through some of the transcripts of communications with our customers, it is impressive how fast
seamless it is and how the probably the customer didn't even realize they were talking to an ai agent if we were in declaring it at the beginning of the conversation yeah um so that's something that i knew was going to happen but every time i see it i'm like super impressed wow this is happening Yeah. So the easiness of setting it up, I was really impressed with it.
Being used to coding and in the backend, you have to be an engineer and developer to set this up or to even test. This was kind of my bread and butter because you actually need someone that knows the business and how the customers are asking questions in order to set this up effectively.
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Chapter 7: How has AI changed the role of customer service agents?
Chapter 8: What is the future of AI in customer success?
seamless it is and how the probably the customer didn't even realize they were talking to an ai agent if we were in declaring it at the beginning of the conversation yeah um so that's something that i knew was going to happen but every time i see it i'm like super impressed wow this is happening Yeah. So the easiness of setting it up, I was really impressed with it.
Being used to coding and in the backend, you have to be an engineer and developer to set this up or to even test. This was kind of my bread and butter because you actually need someone that knows the business and how the customers are asking questions in order to set this up effectively.
So I felt it was a very rewarding adventure for someone like me that is not a technical person, but got out of it quite a lot and learned a lot about technology. Amazing.
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I mean, that's the fascinating thing about what we're doing here is that literally anyone can go in and train. But I'm sure there was a learning curve to that as well. Like, how do you actually train an AI agent? must be different than a human. What did you learn as you started that adventure?
Well, it takes a lot of time to quality check the interactions. And it takes longer than I expected, I have to say. It's quality checking and testing and troubleshooting and changing the instructions as you go. In terms of training it, it's I feel the key here is to use historical interaction, which is something that is coming up with Salesforce.
So being able to base the testing and the quality assessment of the interaction with the virtual agent with historical interactions, it will be a game changer. Because now it was a lot of manual work for a human, funnily enough, to be able to be on top of the quality of the interactions and tweaking what needed to be tweaked in the back end to be able to improve the quality of the interactions.
So what did you use to initially train the agents?
So we wrote up the use cases and we did a lot of testings based on different iteration of the same questions. And we used previous interactions with customers that we entered into the system and tried to test the reaction of the LLM.
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