Overcoming AI Hesitancy in Legal Intake | The Scalable Law Blueprint Ep. 1

Are legal intake bottlenecks holding your firm back?

In this inaugural episode of The Scalable Law Blueprint, host Julien Emery sits down with Jamie Park, Legal Operations Consultant at Superpanel.io, to uncover why so many law firms hesitate when it comes to AI, and what actually works when they finally get it right.

Jamie shares hard-won insights from working inside leading plaintiff firms, including how poor onboarding and overpromised tech left teams burned out, and what it takes to truly modernize intake. This episode is a must-listen for any law firm leader looking to scale smarter and build systems that don’t break under pressure.

Key Takeaways
• Why legal consumers now expect instant answers and self-service
• The real reason early AI tools failed law firms
• What separates successful AI rollouts from the rest
• How onboarding and accuracy drive trust in automation
• The top conversion killers inside most intake workflows
• Why robotic voice isn’t the problem, it’s accuracy
• Common intake bottlenecks that drain revenue
• The mindset shift needed to scale with digital teammates
• How Superpanel helped one firm break its growth ceiling
• What “training an AI teammate” really looks like

Best Moments
00:04:18. “We’re living through a moment where tools like ChatGPT change how people buy.”
00:06:16. “Intake is a very important thing. And I'm just curious where that hesitation comes from.”
00:08:57. “They put the wrong qualifying criteria. It was a complete typo.”
00:10:27. “You wanted to prioritize making it easy for intake to use and to not disrupt their process.”
00:11:20. “You're constantly checking on how the AI is doing. That was very different.”
00:17:08. “The thing that separates AI from a human is sustainability and capacity.”
00:33:50. “It’s not this tool that just jumps in and serves whatever you need done.”

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Transcript

0:30

Consumers are starting to expect a genic AI. They're not scrolling through Google. They're not trying to make phone calls. The common way to find information is search in Google and see what Gemini pulls up. It's come to the point where it's not surprising them anymore. They're almost expecting it. That's Jamie Park, a legal operations consultant who's seen firsthand how client expectations are shifting from intake calls to automation. She's helping firms catch up with the new standard of speed and self-service. Usually when law firms approach me, it's because they're interested in automating in some capacity. In this episode, Jamie breaks down why so many firms have been burned by early AI tools and why the right systems and the right support can turn hesitation into big wins. You learn what we were doing, and then you wanted to also prioritize making it easy for and takers to use. She shares how collaboration and a smarter onboarding process rebuilt trust in automation, and why the future of legal work depends on human insight. Guiding digital teammates. Stick around. I'm Julian Emery and this is the scalable law blueprint. Welcome to the Scalable Law Blueprint. The show where we explore how modern law firms scale smarter, automate workflows, and deliver five star client experiences without burning out their teams. Each episode brings you impactful conversations with the innovators, operators, and legal tech leaders. Building the future of plaintiff law practice. From intake to impact, you'll learn how to build the systems and strategies that drive real growth. I'm your host, Julian Emery. Let's dive in. You're now helping with some, you know, go to market activities and content and things like that with us. But you also have a background working in law firms. Maybe just share a little bit of detail about that. So I've worked at two different lemon law firms here in California, and I've largely focused on the operational side. Primarily an intake. So whether it's CRM management, just day to day operational stuff for the intake team or providing support for the CIO. It's been all things intake operations. So I've really seen the back end of how these firms work, how they qualify leads, how they try to scale and that's where, most of my experience lies. I would say that's awesome. You're also working in some, like, employment law firms and P.I. firms as well, right. Helping set up operations and things there, too. Yeah. So my last company, we had a few different firms, and a couple of them were, referral based. So we were constantly working with partners going into their office, and then, we wanted to provide them with more and more clients and we found some operational and efficiency is on their end. So it became a routine of me going to their office, figuring out their issues. And, then I started, you know, dipping into employment law. Some personal injury stuff. Yeah, it just started expanding into all areas. Yeah. Makes sense. Of course, I guess the thing that you and I were talking about was like, this sort of hesitancy around AI and using AI to do intake or intake related work, maybe, maybe doesn't do all the intake for like, aspects of the intake. And there's this kind of market shift that's taking place right now, in absence of law firms, which we can talk about. But then I wanted to kind of dive into this hesitancy and how to overcome that. Maybe I'll talk first is about the market shift that I'm seeing. Essentially, we're we're living through a moment in time where tools like ChatGPT or the AI results in Google search that you're getting now, and anthropic, it's training consumers to search for things and purchase things and make buying decisions on the internet in a completely different way than they ever have before. And so when you think about legal services, I look at it as like a form of e-commerce in general, like how do you make decisions on what what law firm you're going to go with? And, and how do you get answers to your questions? And so, because we're living in this world now, our search is like you're getting answers and results quickly. Consumers are getting trained to get answers and results and resolutions quickly. And so this notion of like, hey, I think I might have a problem or I think I have a legal problem. This notion that I have to like, contact a law firm to get any answers, I think is going to go away because consumers are expecting resolutions in a self-serve manner. And so I think the firms that provide a self-serve resolution process are as much self-serve resolution as they can accurately. Are the firms that are ultimately going to win, because that's what the increasing demand from consumers is. And I don't think this is just legal. I think it's all online commerce in every category of of services and products. And that excites the heck out of me. I don't know if you're sort of seeing the same thing. No, I'm definitely seeing the same thing. Consumers are starting to expect a genic AI. They're not scrolling through Google. They're not? Yeah, trying to make phone calls or wait to hear back. They you know, the common way to find information is search and Google and see what Gemini pulls up. Or you go on a website and a chat bot comes on. It's come to the point where it's not surprising them anymore. They're almost expecting it. And if you're not onboard, it's going to start seeing seeming more and more outdated. When we talk about this market shift and this increasing demand and expectation from consumers to get self-serve resolutions, and we we look at legal like there is this hesitancy among law firms to try some sort of AI powered solution for some aspect of intake. And, and I kind of get that right, like intake is a very important thing. And I'm just curious, like what you've seen, where you've seen that hesitation come from, maybe what bad experiences you've seen and how you saw a super panel as different to that. Given your exposure to all that on the law firm side? Yeah. So usually when law firms approach me, it's because they're interested in, you know, automating in some capacity. So they're already have this idea in their head that they want to modernize, they want to make their process better. And a lot of times they don't know where to turn. Or they have tried AI and it just they got burned by it. And actually, before finding you guys at my most recent company, we did try a different couple of AI products, and it was just a completely different experience. So I understand where this hesitancy is coming from, from people who have tried AI, and they were left kind of in the dust of not sure, like they felt like they spent money and they created a bigger issue. Some of the experiences we had with these very actually well known AI companies is they provide you with their product and they give you these very impressive promises, and then they kind of, you know, you have a couple onboarding meetings and then you're off on your own and you're chasing the problems. You end up in their systems back end a lot, trying to fix the issues, and it just becomes incredibly overwhelming. I think they were a couple people on my team who were spending, maybe like 50% of their workweek just chasing down these AI issues at one point and then trying to figure out logically what was happening and then how they can fix it, because we weren't given back and forth support. And, you know, talked through the transition process. Right, right. What were examples of those problems? Just like the AI doing things or saying things that it shouldn't or like what? What was that? Sometimes. Sometimes it was completely, you know, logical errors, where they were disqualifying qualified leads or, the biggest thing I saw was that some of these companies don't take the time to learn your process or to really get to know how you function on an everyday basis. And so they would do things very, very fast. And then we would eventually know to notice, like our qualified lead count has gone way down. And then we would start hunting for the issue ourself. I remember the, most recent issue we had was someone from the company we were working with. Put the wrong qualifying criteria for us, and it was a complete typo. Like, they must have tried to do something really fast and didn't, you know, double check it. They put that the year we accept for vehicles for our 11 law firm was, I believe it was like 2020 to 2000 was what they put. So for a period of time. So many leads are just getting marked as unqualified. And we just created this massive, you know, mess. And we were the ones who actually found the problem. If we hadn't brought it to them, they would have never, you know, even noticed it. So it kind of became a system of that. We see the chatbot isn't working. We see, techs going out to unqualified leads, or we see qualified leads being marked as unqualified. And then us, you know, reading through all the texts, trying to take screenshots, trying to put it in a nice, pretty package, and then talking to the company and then waiting to hear back from them. And it was just, you know, consuming a lot of our time. Right? Right. That makes sense. What I guess, how was that experience different when you worked with Super Panel? Like what? What was different there that got to their success outcomes that you guys got to? I think the fact that you were willing to sit down with me a couple times and learn our every day intake process and then not just figure out how you can launch quickly and then kind of get out of there. You, you know, learn what we were doing and then you wanted to also prioritize making it easy for intake to use and to not disrupt their process. So that was definitely something that the other companies weren't doing. I remember we were working on this automated task based system, and you decided to, you know, fit in there perfectly. And when you are ready for it to be escalated to an intake or for the lead to be addressed, you follow that same, task system. So the intake hours, it didn't cause any disruption. It didn't cause alarm. That was one large change. And then also the pilot plan that you guys have, that's different where you're constantly checking on how the AI is doing. If there are errors, you're making sure that not only do they get fixed, but that, the AI is learning from it, and then you're trying to reach a specific point where you can actually look at the data at a certain metric and say, okay, we're performing just as well as your intake, if not better. So following us through to that point was very different. You know, rather than just giving us the AI. And then not worrying about, figuring out this stable point, we can reach, that that's what left us scrambling and having to figure out things for ourselves. Hey, quick question for you. Does your intake and evidence collection process ever feel like the biggest bottleneck in your entire firm? You know, chasing follow ups, collecting documents, and just trying to keep everything organized while leads are slipping through the cracks? That's exactly what super panel fixes. It's like a digital teammate that automates everything from first contact all the way through to evidence collection, so nothing gets missed. And your team can focus on clients and legal work and not admin. If that sounds like something your firm needs, scan the QR code or click the link in the show notes to see how it works. Now back to the conversation. So I think what you're talking about is this like how we onboard what we call sequences. This is what we did with you guys. So what we do with every company, and you know, you're seeing this now, on our side of the table, seeing us working with new companies, but basically we come in and we want to do is look at a benchmark. So we take some workflow. Right. And I'll say this workflow starts here. And ends here. Let's just call this like let's say this is a signed retained case. Let's say this is like initial contact. I'll just call ICI and there's a bunch of work that gets done in between. And what we do is we say, okay, let's let's launch what we call a sequence, which involves interacting with a potential client and also interacting with internal systems, whether that be CRM, case management systems, document management system, slack channels, whatever. And we teach, what we call a digital employee to do some percentage of this work, let's say, to this percent. So let's say digital employee does all of this. And then hands off to human intake taker here at this point. And what we want to see is like if this process usually this process has some sort of success metric or some sort of KPI, it could be throughput, it could be qualified percentage, it could be conversion percentage, could be time to get done, whatever it might be. So basically look at like what is the KPI for this. And that becomes our benchmark. And so we're looking for is this benchmark of a given workflow. And then we'll say okay we're going to automate let's say 50% of it with called a sequence number one. So we might have a sequence. We usually give it a name. And rather than just like deploying it, our processes and we do this in the pilot phase is like, let's deploy this with just a tiny amount of people, like a small cohort, let's say ten people. And so it is run ten people through this. And let's say you have, I don't know, 500 people a month that go through this workflow. We're just gonna run ten people through it and see how it goes. 99% of the time something goes wrong. And so we've got Auditability baked into our into our into our product, where we can understand why every decision was made by a digital teammate, which allows us to then quickly make a change and then run another cohort through. So we weren't run another ten people through, and usually we see a performance improvement. And then if that performance improvement looks good, we'll say, okay, let's run 40 people through this and see how that goes. And maybe there's a few more little changes we make. We make some iterations and then we're like, right, let's run 100 people through it. And we're doing this. We're iterating to get to what we call a stabilization point. And stabilization is where this sequence and the sequence again, is just like an end to end sequence of work that is done by a digital teammate. And the sequence is running at or above this preset performance benchmark. And so it can take us a little while to get there, and we just run on small cohorts until we have confidence to start deploying at a larger and larger scale. And then we'll sometimes start taking like a percentage of all people that go through this, and then we start increasing that percentage until you get to 100%. And so I think that's maybe the process you're talking about is this sort of iterative approach to deploying a digital teammate and training that digital teammate to do a given sequence of work. Is that right? Yeah. Having a clear learning phase period where you're making sure that it's meeting a specific standard before just setting us off was very helpful. Also I remember the ability to scale on increments. We, I remember I was going okay it's on 10%. It's on 20%. Let's do 50%. That was something different. Also very reassuring and helpful for directors who are, you know, maybe scared of AI or hesitant of the process, being able to take that, those small baby steps, that made everything a lot easier for us. So another thing that people talk about is like this fear of the robotic voice and like, oh, the AI doesn't sound like a human. We've had some wonderings about this that I want to share, but I'm curious, like, what are your perspectives on this? And you know, what of what have you seen just around like AI voices in general, in the, in the, you know, testing and work you've done? Yeah. So at this point, I have a lot of friends in the legal space, and I told them how we started working with you guys, how we managed to successfully bring on this AI intake and how it's been helping us reach side up numbers, you know, higher than we thought we could get to. And more frequently than not, they respond with, oh, I've seen those those AI companies, they don't work. They sound so fake. I've never come across one that actually works. So yeah, I remember and then I, I wanted to get your perspective on it and I asked you, I said, what do what is the biggest piece of resistance that you see. And you said that there a lot of times you just hear back that people believe that they prefer human integers or that human integers will convert better. So yeah. So that led us down this path of is that true, you know, or why is there that belief? I think it does stem from the fact that there were a lot of AI companies that we even tried that brought on these promises that, didn't work out well for us. So I think a lot of legal companies now have enough AI companies. As you know, they don't work. They they're fake and they don't actually produce what we need. The thing is, they're right. Like, a lot of it doesn't work like it requires. A more nuanced approach to get these things to work. And it requires, you know, software architecture or sort of built around a given use case. Also, make it work. The interesting thing for me that I found is a lot of people anchor on the Voice and they're like, oh, like, how does the voice out? Like you? I don't know if consumers don't accept this voice. And one thing that we've learned is, one, the vast majority of interactions, like if people call in right off the bat, obviously there it's voice right away. And those work. But if people also fill out a form, for example, most people actually interact over text and voice. So first off, like just anchor and voice, most people actually get qualified or moved through the process over text and email and not not necessarily always on voice. And to one that's like focus on all the channels, not just voice. But then two, the other thing we realized is, as long as the sequence is accurate, as long as the like, the the experience is accurate and helping someone get to where they're trying to get to on their terms and quickly then, a robotic voice actually doesn't matter. What matters is the accuracy. And whether you're talking to a human or some robotic sounding AI voice. If either one of those are inaccurate or not understanding you, or you feel like they don't know what's going on, you're going to be frustrated. But if they know what's going on, you feel like you're being listened to and you feel like you're you're being heard and followed up with and understood and you're getting the resolutions, what we've realized, we've run some tests around this is like robotic voices kind of don't matter. Like conversion rates don't drop, as long as the accuracy is, is high. And I think you could say the same for humans. Those humans who, if they have less accuracy, you're going to see conversion rates drop. And that's humans that are have higher accuracy and conversion rates go up. And so, I think people need to really look at it as like, how do you get this thing to perform accurately regardless of voice, and then just use the best voice you can, but like it's it's less about the voice, it's more about the accuracy of the entire process. When you think about an actual conversion killer, I can speak about my own experience from the other side, from the operational standpoint of these firms. You know, the biggest conversion killers I've seen are humans marking a lead in the CRM wrong. And then all of a sudden it becomes stagnant, you know, not following up with a lead when the lead told them they want to be called during a certain time period or, you know, not following up and getting the documents they need or forgetting about a specific case. And it's these bottlenecks or these stagnant leads that really are what kill conversion like crazy. The the little test you talked about with the robotic voice was really funny to me. And it completely, you know, directly overwrites that a robotic voice isn't going to impact conversion as much as, you know, a stagnant lead or a bottleneck. That's keeping you from contacting people. Yeah, that makes a lot of sense. What are some examples of those bottlenecks that you've seen? Like you talked a little bit about some of them, but can you expand on on that, like how you saw conversion rate impacted because of these bottlenecks and sort of where these bottlenecks presented themselves and how you sort of identified them? I remember when we initially want to scale and put more focus into digital marketing and increase our ad spend. You know, we started producing all of these leads and we started having more, capacity to analyze our marketing for a very like performance marketing, perspective. But we actually didn't have the manpower or the, competency of the intake years to keep up with that volume of leads. And what started happening was, you know, they would contact a lead once, not get Ahold of them, and then just leave it sitting there and, or they would they were trying to call so many leads in one day and they were doing things very fast. And, you know, marketing a lead wrong marketing lead as unqualified when it was qualified or, you know, some other version of that, and then just leaving it stagnant when that was actually a good qualified lead that could have been turned into, a sign up. I also see it past the sign up phase where you've brought on a new client, and you need specific documents from them. And, a team gets so backed up with the number of cases that they're trying to deal with that cases just start slipping through the cracks. Clients actually, requests to be dropped off. That became the number one reason for drop cases was where clients were so frustrated with not being able to get Ahold of us, or not having consistent communication that they just, you know, wanted no part with the firm anymore. And they had lost trust in us. So, those were the two biggest issues I've seen is a lack of being able to deal with the volume of cases or leads that are present. And then also human error, you know, putting in data wrong, putting in information in the CRM that causes, the lead to kind of slip through the cracks. And those are two issues that super panel fix for us, which was really nice. And I think what brought us to a sign, a point that we didn't think we would ever be able to reach. I mean, whether it's humans or AI, like, what's the best mindset to have when approaching scaling with AI or with humans? And I'm curious, like, what if there's much of a difference between the two? Because in my mind, I'm actually not sure if there's a huge difference. It's just there. It's like a different path, but the mindset might be the same. I'm curious your thoughts like what is the mindset needed to scale with AI, and how is that different, if at all, than humans? So with AI, there's more of at least at our company there is more of a bit of hesitancy. And we were lucky to have, a director who wanted to bring on, you know, modern methods and was looking at, products that, you know, we're a bit higher tech, not resorting to the same methods. All of the other firms seem to use. So we had more of an open minded leader to begin with, which made it nice. But, yeah, for some reason, there's just exists this hesitancy around, committing to an AI company or feeling like you can confidently choose one that's going to work. I do remember scaling in general, which were very overly ambitious, and we thought, you know, just put a lot of spend in there. We hired, someone who was very experienced in performance marketing and could own the process for us. And we thought more leads, more revenue, you know, more cases, everything. And we didn't think it through. We didn't realize that, you know, you can pump all this money in marketing, but if you don't have the correct process or the manpower to deal with it, it's just not going to work. So it's kind of like hand in hand. If you want to scale, you need to make sure that you have something that can process, that increase in leads. And I've seen other firms start to do this because they're their, realizing more and more how to work in the digital marketing space. And, they're not only doing the billboards and the bus ads that you see from law firms, but they're increasing their meta ads or doing Google, you know, bidding on keywords and they're doing all these different types of performance marketing, and they're able to analyze it now and scale and figure out how to, produce more leads. But then they don't have the actual manpower to keep up with it. So I think that's where Super Panel fit so seamlessly with us is we wanted to increase our sign up number, we wanted to have more leads and have more cases. But and then we pumped all this money, money into marketing. But we didn't have, you know, the manpower to match that. So then super power provided us the capacity to deal with that influx. Got it, got it. Yeah. So it just helps increase. But it helped increase the capacity that you could handle really. So you could you kind of intake becomes less of a constraint. And then it's like more of like how do we just do enough marketing. It became a good tool for us to scale because, I mean, otherwise what do you do? You hire more and takers. You take a couple weeks to train them. You know, it becomes more of an extensive process. Then when you put in the time to have a working AI and then you can just, you know, start scaling from there. Do you think there's a similarity between hiring people and hiring a digital teammate like super panel? Because one of the things I've observed is no matter what, like if you're the partner at a firm or founder of a company or whatever, like there's this hesitancy to hand over control. And if it's hiring a person, like, let's say you're the lawyer and you're doing intake like it's kind of hard to hand that over. Then you find maybe a paralegal or another lawyer that you really trust, and you let them do it, and then you're kind of hesitant to hand that off to anybody else. But at some point you have to figure out a system to train. And on board other people to do some part of the job at the company. I'm just using intake as an example. It's kind of the same with AI. You just need to go through a training process where you're handing over control to this digital employee and understanding how that digital employee or that digital teammate makes decisions and does things, and then iterating on it like I view it as kind of the same thing. It's just a different approach that you still need to hand over that controlling it, the process to sort of train and onboard and build trust. Yeah. It's, whether it's a person or AI, you still need to set them up for success and give them that graceful learning phase, I guess, is what you would call it. Right. Investment. Right. You do invest in that. Let them make some mistakes at the beginning, but have a process to not make the same mistake twice, but learn from those and just get to that stabilization point. I mean that's a good way of looking at it because you know firms are comfortable hiring on a new intake year and providing them with a training program. And they understand okay, it's going to take this period of time for them to get on their feet. And they're used to that transition. But then when it comes to AI, they're kind of, you know, gripping on tightly, watching it closely, watching to control how it's performing. And, you know what, speed. But if you give it that graceful learning phase that it needs, the thing that separates AI from a human is that, you know, there's more sustainability with the AI. You don't have to worry about it, you know, deciding to leave and then having to bring on a whole new person and start the process over. You can also, it has it can have a higher workload than a human. So that's, as it still needs that training period, though. Yes. Like, like you would with a human. Integra. Yeah. Yeah. Like that's the critical piece. People don't realize that that training and onboarding period is really important in that you need to know and be okay with it. Making some mistakes to figure out how to do something the way you want it done, how to behave in that way. Have you ever had an experience where a firm is finding you hard during the, training and learning phase of the AI and how that kind of affected your ability to, get to that stabilization point? Yeah. I mean, the the biggest things we've seen go wrong is in a couple of areas, one sort of expecting perfection right out of the gate and not really being willing to have any mistakes right out of the gate when you need like a little bit of sort of room to test and, and you can minimize the mistakes, but you still need like some testing to happen knowing that some mistakes might be made. And to like an unwillingness to do that was seen from some companies, which, you know, they're kind of not ready. And the other thing we've seen is process changes. So a challenge is like, if you're training a digital employee to perform a workflow and kind of follow a process, if the process is already broken and not working or the process changes a lot, you end up having a very limited amount of like a very small sample size of data. So maybe that's like ten people that go through a certain process. And so you learn enough to start to iterate and to improve that workflow. Then often the workflow changes and then you're back to square one. And then you're like rebuilding to try to fit the new process. But there's like it's not enough. There's not enough cohorts going through to train the digital teammates to perform and hit that stabilization point. Because the process keeps changing. And so those are the problems that that we've seen. If like if, if, if something doesn't work well, like that's usually why. It's just like the process changes too much. There's too low of a volume of data. We're not able to like run iteratively and get to that stabilization point. But if able to run in Italy and get to the stabilization points, and there's a process that's that somewhat stable and that works. Already with humans, then we've seen success every single time. Yeah. So I guess to speak to that, I, you know, destined to be treated as a tool as you would with, you know, scaling, marketing where you need this testing phase to learn what's going to work and what's not. Or if you bring on a new person, you know, you start, have them start training and then change the process that they're learning. All of a sudden it's just not going to set you up for success. So yeah, I guess that that speaks to the mindset. You have to approach it as a tool and something that you need to foster success with, and not just expect it to jump in there and become this magical wizard who can fix everything for you. Yeah, 100%. And I think that's that's a that's a shortcoming in the market too, with various AI vendors. Survey AI platform vendor is kind of over promising and like, I get why the over promise? Because it's like there is a high amount of promise. But it's like we need people to know the process to get there. And then it is a process and it takes some time. I think that's why Super Panel was so successful with us. Also is you, we're very transparent about needing that. You know, we've had other AI companies and I used to also work a lot with data, and they would come in and promise, oh, we could, you know, just throw it all in. I know, produce this dashboard for you or, you know, we'll suddenly get you this many new board meetings and they didn't talk about ever even addressed this sort of learning phase that you have to go through or that it, it requires, coordination between the company and I, you, you know, you it's a lot of back and forth. It's not this tool that just jumps in and then serves whatever you need it to get done. So I think also, yeah, being very transparent and emphasizing that there is a learning phase, you know, that, the importance of the learning phase as well. I think that's also what made a super panel so different for us. Oh, cool. Why don't we end it there? There's so much more to talk about. Let's go to the session. Yeah, I think we covered a lot here to all listening. See you soon. Yeah. Thanks for checking out the Scalable Law Blueprint. If today's conversation helped you think differently about how your law firm runs, share this episode with a colleague. And don't forget to follow the show so you never miss what's next. To see how automation can transform your intake and operations, visit Super panel.io and discover how leading plaintiff firms scale with confidence. I'm Julian Emery and I'll see you in the next episode.