AI의 미래: 자동화, 예측 지원 및 조정 문제

### AI의 현재와 미래: M Studio의 설립자 겸 CEO인 Dimitri Shapiro와의 심층 토론
* 🚀 다양한 AI 모델을 활용하는 노코드 자동화 플랫폼인 M Studio의 창립자이자 CEO인 **Dimitri Shapiro가 AI의 현재와 미래에 대한 인사이트**를 공유합니다.
* 🎯 1983년 영화 '워 게임'을 보고 AI에 입문한 디미트리는 전문 개발자가 되어 여러 회사를 창업하고 구글에서 일하다가 **2016년 M 스튜디오를 설립**했습니다.
* 🤖 디미트리에 따르면, AI의 미래는 **인간이 AI와 직접 상호작용할 필요가 없는 백그라운드에서 작업을 자동화**하는 것이 핵심입니다.
* 🔮 현재 비용 효율성과 성능으로 인해 실제 애플리케이션에서는 **GPT-3.5 터보가 선호되는 모델**이지만, 앞으로 대부분의 AI 사용 사례에는 타이핑이나 말하기가 포함되지 않을 것이라고 Dimitri는 믿고 있습니다.
* 📈 AI는 여러 단계가 필요한 작업을 자동화하고 데이터를 분석하며 생산성을 향상시켜 **다양한 산업을 변화**시킬 것으로 기대됩니다.
* 🛡 AI 안전 및 정렬과 관련해서는 **대규모 언어 모델의 일반적인 정렬은 해결할 수 없는 문제**라는 Yann LeCun의 의견에 Dimitri도 동의합니다.
* 👓 디미트리는 AI가 백그라운드에서 경청하고 주의를 기울이는 '제3자' 역할을 하며 **사용자의 니즈에 따라 예측 솔루션**을 제공하는 미래를 상상합니다.
* 💬 AI의 위험성을 둘러싼 센세이셔널리즘에도 불구하고, 디미트리는 **프로세스를 자동화하고 사람들을 더 똑똑하게 만드는 등 오늘날 AI의 놀라운 실용적 활용**에 대해 강조합니다.

코드가 필요 없는 AI 자동화 플랫폼인 Mind Studio는 운영을 혁신하고 현대화하여 작업을 자동화하고, 사용자 지정 애플리케이션을 만들고, 오래된 SaaS 제품을 대체합니다.

if you're paying attention to AI at all, I really think you're going to find this discussion valuable. A lot of what we talk about is counter to the common narrative that people hear in the AI world, and there's a lot of things that I think people believe is where AI is going, where our discussion tries to prove that no, maybe it's actually going in a different direction. The following is a conversation with Dimitri Shapiro, the founder and CEO of M studio. Now, Mind Studio is a no-code automation platform that leverages all of the various AI models to put AI more behind the scenes. So, you build these automated workflows within Mind Studio that leverages tools like GPT-3, Llama 3, or Claude Opus, and then instead of having to prompt all the time, these workflows just happen behind the scenes. Now, in this conversation with Dimitri, we are going to touch on Mind Studio and what it does a little bit, but the majority of this conversation is all about the present and future of AI, the cases that it's

great for right now. The use cases that it's going to be good at in the future, where all of this is headed, it's a really deep dive conversation on what two people that are in the trenches with AI all the time see as where this is all headed. Hang out, enjoy this conversation. I know I love talking to Dimitri, and I think you're really going to enjoy it as well. Can you break down your story a little bit and how you even got into AI and the Genesis of Mind Studio? I was born in Russia. When I was 10 years old, I moved to Atlanta, Georgia, and grew up there. One day, my dad took me to see this movie called War Games in like 1983 or '84, and I saw it. I was like, Oh my God, I love this. I got into computers then. I was just fascinated instantly, and you know, was a professional developer for a decade plus, and then started my first company in 2000 called Aonics. It was an Enterprise software cyber security company. I built that, raised $34

million. We had over 2 million seats deployed. Did that for 5 years. That company ended up getting sold. I started another company called Vo which was an editor to YouTube. Raised $70 million for that. That's when we first started doing a lot of work on recommendations. This before YouTube even had recommendations. People that like this also tend to like that and there's sort of like a bunch of machine learning stuff. And then after that, I went to Myspace and I was CTO of Myspace music. And there we did a lot of AI as well, obviously looking at recommendations and connecting people and things like that. Then I went to Google. I was at Google from 2012 to 2016. The first 2 and a half years, I ran product on these three teams that were using machine learning. And then left Google in 2016 to start this company. Kind of as soon as we saw ChatGPT come to market, my co-founder Sean Thelen and I, it was really clear to us these things were going to get more and more powerful.

there were going to be more and more of them. We predicted open AI was not going to run away with everything. These things were going to go to commodity and sort of the ability to be able to do applied AI. To be able to take like all of this raw material of intelligence and allow individuals, businesses, you know, whoever, to mold it into the right forms to be able to get stuff done. I tend to describe AI as sort of being this like intelligence goo that you can sort of form into anything and get it to do anything, right? But by itself, it's just goo. But it's really malleable. And so if you know how to like form it into different things, you can create amazing things with it. That's when we started building Mind Studios a year and a quarter ago. And yeah, and here we are now. As someone who, you know, obviously sees all of the models because you have a tool that's sort of model agnostic, right? Mind Studio uses any of the large language mod that you want. Is there a

model right now that you feel is sort of the ideal model to be using. Yeah, GPT 3.5 turbo really. So, you prefer that over like GPT 40 or Opus or one of those. It is clear from what is useful in getting real work done. You want to keep cost low, price is important. You want to keep latency low, performance is important. And for most of those things, GPT 3.5 turbo shines. You can kind of throw anything at it. It's inexpensive, it's fast. Now, again to your point, like we support any other model and some people say no, I need, you know, GPT 40 and I'm just going to use that and I love that better. Uh, is they say in the South, bless your heart, wonderful, this wonderful, you love it, use it. We allow you to use it. You want to run your own inference, even if you don't need to run your own inference, but you just want to do it, fantastic, do that. What are some of the like standout cool use cases the AIS that enterprises are

creating tend to kind of fall into the following categories. There's like some form that gets filled out and there's sort of data that's being accumulated. And it used to have humans sort of sit there and try to process that data. And so that might be resumes that are coming in, right? A big government agency that gets thousands and thousands of resumes a month used to have like groups of humans sitting there processing these resumes. Like the AI does that and sort of triages it and classifies it. You know, asks for more information if there's not enough information. Like for example, in the form, for whatever reason they forgot to put something in or they put something in and it's ambiguous. What used to be like a human had to reach out and say what did you mean and all that. No humans need to be involved there anymore. AI can do all of that stuff automatically. Then there are AIS that do sort of like multi-step workflow when a lead comes in. Used to be a human had to

go you know sort of start this process up and start that process up and go get some information sort of like project management type things. That used to require sort of like really well-organized people or sort of a well-instrumented sort of workflow process sort of being Track by conon. Like anything that you can sort of track by conon can now be automated. By definition things that require multiple steps that might sort of need to be fired off at asynchronously. And when the data comes in then sort of continue down the path or things that are analyzing things. Sales is a obviously super important process in any organization bringing in Revenue. It's a resource-intensive process from a human standpoint right like there're sort of like self-service things where like you just go go to our website and like read about our product and put in your credit card and buy that's great. But for everything else that requires sort of human sales people well then that is a giant lift because salespeople. First of all there's diversity in their skill set and their

ability to be able to engage customers and do stuff there's time limitations only so many hours in a day and things like that and so like sales automation is a common pattern let me give you an example many uh conversations that everybody's having now on video for example tend to have agents transcribing agents that join right fireflies otter etc right most of the time they just like sort of go somewhere in some bucket and it sits there and maybe it gives you like some kind of an action item thing or whatever but that's kind of it well now what people are enterprises are doing is setting up AIS that basically watch the bucket that gets the new transcriptions and then fire off a series of steps that the AI is doing which is evaluating is sort of like a third party listening the humans communicating with one another and this one of course is on the side of the salesperson and sitting there and saying is this prospect ready to buy are they asking the buying question or are they saying they're going to buy but they

probably are not going to buy because they haven't asked all the questions that would be required to truly demonstrate that they understand what the product does. They're nodding their heads, they're saying yes, but the AI doesn't think they're understanding the product. In fact, maybe let's give them some additional collateral that's specifically custom made for them by the AI that helps them see the product from a different angle or preemptively answers their questions that they were too afraid to ask because they didn't want to look like a fool. They didn't want to ask stupid questions, which is a common problem in sales. Salespeople want to sell, they tend to get this thing called happy ears, they think the prospect's right to buy, the prospect doesn't want to be mean, and the prospect saying yes, this is a great product, I love it, of course I could see it in my company, and everybody loses. And the AI sort of was sitting there saying, hold on boys or girls, let me explain to you that you're not seeing each other in the

right way and like let's get this deal done right and so like this kind of intelligence injection into sales where the AI can spend meaning that can scale. It's not blocked by hours in the day. It can process every meeting. It can process every conversation. It can ask all the what-if questions. It can generate all the content follow-up that's needed that would have never been generated by a salesperson because the salesperson wants to go to the next call and like follow-up is really hard for salespeople. AI trainers is really interesting seeing a lot of those keeping teams aligned. You know every organization is constantly changing evolving. The market is changing and so retraining people and keeping them aligned as the organization shifts and evolves is really really hard and you know requires teams and that doesn't scale for most organizations. They can't get the team of they're small or medium if they're too big the teams move slowly. Now AIS can do that and and that is extraordinarily important and and again it's scale that's transformative so.

organizations are about to get radically more aligned. And when they're more aligned, outcomes, productivity, and outcomes success. Do you think the current large language models are better at sort of reading between the lines of the context of what we're trying to say than humans? 'Cause you're talking about salespeople, and an AI salesperson is going to be very, very patient with the person on the other end and answer all their questions and keep going with them, where a real human salesperson is, like you mentioned, going to want to get off the call, close the sale, get off, move on to the next one if possible, right? But you were talking about how these AI models can actually sort of read between the lines and understand that, okay, they're saying they like the product, but maybe, you know, there's some extra information there that's just trying to get them off the call so they don't have to keep on going with the sales call, right? So do you think the AIS are out of place right now where they're actually better at sort of reading

between those lines than humans are absolutely than most humans are. I'm certain that there are some humans, great empathetic insightful people, salespeople, or anybody else. Some people are just better at reading the situation, reading the room, understanding other people, really listening than other people, and those are rare. So most people don't have that skill and don't have the patience and sort of don't have the time for it. And so yes, today's AIS are dramatically better than the vast majority of people of doing that, of like looking at the situation and again sort of asking the questions: what's not being said here? What might be being said? What else could it mean? In sort of like sort of enumerating all of the potential configurations of kind of this reality of the situation and then saying, oh, these things are missing, let me introduce those in, because if you introduce those in, all of a sudden the understanding changes and the conversation changes. I want to look at the future a little bit. You and I when

we met up in person. It was right around the time chat GPT just launched their GPTs, and I think you said something to the effect of, like, yeah, they'll be hot for a minute and then nobody's going to talk about them again. And that's what happened, right? Nobody's really talking about custom GPTs over in the GPT store anymore. Do you have any other sort of future predictions? I know right now the big narrative that OpenAI is pushing is the voice narrative, the, you know, the her narrative of all of this AI is going to happen conversationally versus us typing in prompts. What are some of your predictions? Do you think that that's the correct narrative? Do you think the voice is the future of this all? And you know, what other sort of predictions might you have? No, I don't think that voice is the future. Not that it doesn't have applications, it certainly does, and but I think as we were talking about a little bit earlier, I think the real power of

AI is going to move to things that just happen behind the scenes, and you're not interacting with it. Human is the bottleneck. The best situation is when there are people out there that know what you want done or what you need done and go off and are able to do it. If you have to tell them what to do, if you have to ask them what to do, or you ask them to do something, then that slows it down. If you have to teach them to do something and be sort of precise about asking them what you want done, well, that's even more difficult, right? It requires more time, more miscommunication, etc. And so the more we can get humans out of the loop of having to give any kinds of commands to AI, the better. And so typing, by the way, is faster for many people than speaking. And so that speaking becomes an issue there as an interface, but even typing, you know, even if you type 150 words a minute, that's still like restrictive. I'm certain that again if we fast forward, you know, one to

two years most of the things that AI is used for has nothing to do with typing or speaking. has nothing to do with humans being in the loop. Humans benefit from it, and humans get involved periodically when they need to be because a human wants to hear from another human. In a sales situation, it's like human talking to AI that's selling them something. Even though people are trying to do that now, I actually think that's a completely wrong approach. That certainly there's a novelty. Nobody wants that. Nobody wants to be talking to an interactive voice response unit, which we've had forever, right? Like when you American Express and you talk to the thing that says press one for this or say your say this or say that. Like isn't that frustrating as hell that to say that that's right, you just want to talk to a damn human. You want to do that. So the more we can have humans talking to humans and AI doing sort of everything else, the better. It's better for everyone. There should be sensors.

everywhere, you know, periodically now I wear the meta smart glasses. You can ask it to see things and, you know, hear things and query things and all that. So we'll, we'll, we'll have the ability to when we need to speak to Information Technology. We can say it, we can type it. It understands us, that's great. But again, the real productivity gains, the real Innovations, the real things that can scale are all things that require human to get out of the way so they can sort of work at inference and line speed and, and just get stuff done. I have the meta Ray bands as well. This might just be more of a me issue. It might not be a common thing, but honestly, I feel awkward as hell talking to my sunglasses, right? Like when I'm out in public and I have like a question, I'd much rather pull out my phone and like either Google it or ask chat GPT than spend like press a button and be out in public talking to my sunglasses. It just feels weird to me. It might be a generational thing, right?

like the next generation of people coming up. That may not have that same sort of insecurity around it. But for me, I just feel weird. I totally agree. The real values that there are now sensors that we are wearing. That can see the world through lenses and can hear the world. And again, the more human gets out of the way. And the more things sort of happen autonomously. Where the human might be sort of prompted with like, Oh, I've got some context for you here of like the situation that you're in that you should know about. And maybe that comes through audio. Or maybe you look at your phone. And whatever. Where the AI sort of listening in the background and paying attention in the background. And again, it's sort of this third party that's really sort of present in understanding it. And not, Oh, you know, hey Mata, do this or that. Like that. That can't be the right, right. It feels like it's going in the direction of it's going to be a little

bit more predictive and sort of know what you want from it. Like, for example, if you've got these sunglasses on, it knows that maybe you want some extra information of what's going on in the world around you. Or, um, you know, maybe you want to know if somebody's coming up behind you. It can, like, let you know in your ear, Hey, there's somebody walking up behind you, or things like that where it's more predictive and it's giving you solutions without you having to necessarily prompt it. It's just kind of knowing the environment and trying to be helpful in the moments where it feels it can be helpful. Yeah, I'll give you a killer app for most people. Most people are really terrible with names. And so if somebody's coming at you, if the AI says, That's Matt, that's a killer app. Yeah, especially if it's in glasses with, like, a heads-up display or something too, like, Hey, like, it's got a name tag above the person or something, you know, right? Yeah, to your point, it's got to different

people have different needs, and today Meta does not understand the difference between our needs. Does sort of the same thing. That's wrong. It's not very valuable. That's generic. But as soon as you can, for example, like using Mine Studio, create a custom agent for yourself versus a custom agent for others. What does it do? Well, now it becomes a platform. And again, now with predictive things, whatever it is that you want it to make you smarter at in real time, it can do. And that becomes powerful. Right now, one of the hottest topics sort of in the AI world is the safety, security alignment stuff. I would love to get a little bit of your thoughts on that discussion. You know, obviously over at OpenAI, they just sort of disbanded their alignment team. And then Sam Alman went and created a sort of new safety board where he is the head of that safety board. But then you have Yan Laon out there saying, Yeah, but we're not even close to being where we need to.

worry about this stuff yet so why is everybody freaking out. There's like all of these different narratives going on around the safety and alignment issue. So I'm just curious your just sort of overall general thoughts on it. I agree with Yana that this, while again theoretically it makes sense that you know alignment and safety is a thing. I think sort of this generic alignment of large language models doesn't make sense because alignment is different for different use cases and different parties that are using AI. But it's an unsolvable problem to create something that's aligned for everyone. It doesn't make sense. Can you put constraints on it of like don't teach people how to create a bomb? Sure, again we've been doing that kind of stuff forever. There's nothing sort of special about that. I'm not sure that Sam is wrong in sort of disbanding that team. The right place to do alignment is at the level of like what is it that you're using it for. If you're an enterprise and you're using this

thing to help your sales people be more productive, more effective, more whatever. That's the place where you sort of fine-tune this alignment, where you give it more nuanced instructions on how to behave, what to do, what not to do, etc. And again, that might be different than some other part of the process that you're doing. When I think of alignment in this sort of general sense, when it's just sort of generally being used, I almost think of it in the sense of like the paperclip maximizer scenario, right? Where you basically tell an AI to go make paper clips at whatever cost, and then it starts eating up the human resources and deciding that, you know, like it does whatever it has to do to make more paper clips, even at the expense of humans. And from that perspective, I totally agree with Yan Laon, because I don't think large language models are going to get to a point where that's an issue. I don't know enough about the sort of algorithms and the math and all the stuff.

underneath it to talk super intelligently about it, but from my understanding, a lot of these large language models are basically predictors of the next word, right? They're basically data and statistics, and predicting the next best word in the statement based on the prompt that it was given. And that technology, I have a hard time seeing it get to AGI and getting to like this paperclip maximizer scenario because it doesn't totally understand what it's doing. It doesn't understand the logic behind what it's doing; it just understands its training. And that's sort of how I see it from my view of where we're currently at in large language models. But again, I'm not an engineer, I'm not a scientist. Yeah, look, I actually think that you don't need to be an engineer, a scientist in this case. So again, let's maybe play around with this for a second. So let's say that you took a large language model and somehow connected it to some, you know, Factory that produces paper clips and has the ability to

Source more materials to produce paper clips and and this thing did it token completion and and said oh what I need to do is order a bunch more raw materials and you know sort of Arbitrage the cost and what it could come up with this kind of a scenario like it can do that now you can say give me a plan to do it and this might be like one of the plans is like let's deplete the world of all precious metals make paper clips out of them uh and then you know theoretically if it was connected to a factory it could go ahead and run the machines and do it but like so what who would do that and what does that mean you could do that now I don't need a large language model to do that like if I wanted to sort of do that I can write a simple python script that does that too yeah but even in the scenario that you're mentioning somebody could go and unplug this machine still exactly yeah there's a show called electric dreams it's I think it's an

Amazon original. I think second episode in this thing is called The Factory, which sort of has a scenario like this. It's like this post-apocalyptic world where there's like this factory, and it just produces junk, robbing raw materials from the world, and sort of by drones delivers this to these like now primitive people that are just living at the edge of the factory, and it just drops stuff. It's like killing all the raw materials, you know, siphoning them off, and just giving them junk. And finally, they have to break this factory. But that's generally silly. The problem a lot of us are experiencing right now is like all of this weird sensationalism about AI. And I think like this talk right about alignment and danger and fine-tuning and massive disruption and all of that is sort of taking a lot of the oxygen in the room. Where if you sort of got rid of that and looked at just like practical uses, it's freaking amazing. It's insane what you can do now and again.

automating things and making all of us better and smarter. Incredible! Don't need to wait for GPT-56 at all. You can do that now. You just need to put a little effort into telling the damn thing what you wanted to do. How do you describe Mind Studio? A Mind Studio is a platform that can be used by any kind of organization to transform itself and modernize its processes, to automate a bunch of things that used to be done manually, and automate them completely, sort of freeing up people to do other things, making it much more efficient, higher quality, etc. Create custom new business applications for humans to use, sort of replace a bunch of old off-the-shelf SaaS products that were all sort of cobbled together. The average Enterprise has over 130 some odd SaaS products that it uses, and so you can sort of replace those. You need fewer products to do a lot less but do exactly what you need it to do. It's a no-code platform, so just people in any organization can quickly learn to use it and transform their

operation. It can also be used by individuals. We have workflows. Thousands and thousands of individuals use M Studio to automate their own workflow. Thanks once again to Dimitri for doing this interview with me. I had an absolute blast having that conversation with him. I hope you enjoyed it. If you want to hear more conversations like that, make sure you check out the next WVE podcast. That's the platform where I tend to do a lot of long-form deep dive interviews, along with my co-host Nathan Lance. And if you want to learn more about Dimitri and M Studio and what it's capable of, I will link up M Studio in the description, so make sure you check it out. It is a really cool platform. I've used it myself. I've demoed it in past videos. I plan on making more videos about it in the future. It is a really, really cool tool that I am happy to talk about because it's super helpful. Thank you once again to Dimitri. Thank you so much for tuning into this video. I really, really appreciate you. I hope you enjoyed it.

Here's Where AI Is Headed (Feat. Dmitry Shapiro)

AI, No-Code, Tech, Futurism - I'm a tech nerd and talk about tech nerd stuff

Matt Wolfe

AI의 미래를 만들어가는 마인드 스튜디오의 역할

마인드 스튜디오의 창립자인 디미트리 샤피로가 AI의 현재와 미래에 대해 이야기하며, 더 많은 배후를 향한 AI의 방향성을 강조합니다.

구글에서의 근무부터 마인드 스튜디오 설립에 이르기까지 AI에 대한 그의 여정을 공유합니다.

이 회사는 개인과 기업이 놀라운 것을 창조할 수 있도록 AI의 접근성과 유연성을 높이는 것을 목표로 합니다.

5 터보는 비용 효율성과 고성능으로 다른 모델보다 선호됩니다.

다양한 작업을 처리하는 데 탁월하며 짧은 지연 시간으로 빠른 결과를 제공합니다.

기업들은 데이터 처리, 다단계 워크플로, 영업 자동화와 같은 프로세스를 자동화하여 효율성을 개선하고 사람의 개입 필요성을 줄이기 위해 AI 시스템을 활용하고 있습니다.

AI는 잠재적인 고객의 우려와 질문을 해결하기 위한 맞춤형 자료를 제공하여 제품에 대한 이해를 높일 수 있습니다.

AI는 행간을 읽고 고객의 요구와 동기를 이해하는 데 인간 영업사원보다 더 효과적입니다.

또한 AI는 지속적인 교육과 적응을 통해 조직 팀이 서로 협력하여 더 나은 결과와 성공을 이끌어내는 데 중요한 역할을 할 수 있습니다.

AI의 미래: 음성 및 타이핑 그 이상

맞춤형 GPT는 인기를 잃었고, 앞으로의 AI는 사람의 개입 없이 백그라운드에서 작동하는 작업으로 나아갈 것으로 예상됩니다.

이는 병목 현상으로 여겨지는 인간의 개입을 줄이는 데 초점을 맞추고 있으며, 인간의 입력이 필요하지 않은 작업에 AI가 사용될 것이라는 믿음에 따른 것입니다.

인간은 다른 인간과 상호 작용하고, 그 외의 모든 것은 AI가 처리하여 궁극적으로 모두에게 이익이 되도록 하는 데 중점을 두고 있습니다.

웨어러블 AI 기술에서 인간 상호 작용의 역할

웨어러블 AI 기술은 자율적으로 작동할 수 있는 잠재력을 제공하여 사람의 개입이 필요하지 않습니다.

그러나 스피커는 공공장소에서 음성 명령을 사용하는 것에 불편함을 느끼며 보다 신중한 정보 검색 방법을 선호합니다.

웨어러블 AI 기술의 미래 방향은 사용자의 명시적인 요청 없이도 정보와 솔루션을 제공하는 보다 예측적이고 능동적인 기술이 될 것으로 보입니다.

AI의 다양한 요구사항에는 일반적인 접근 방식이 아닌 맞춤형 조정이 필요합니다.

모든 사용자를 위한 정렬을 만들기보다는 특정 사용 사례와 당사자 수준에서 AI를 정렬하는 데 초점을 맞춰야 합니다.

대규모 언어 모델에는 특히 특정 목적을 위해 기업에서 사용하는 경우 미묘한 조정 지침이 필요합니다.

대규모 언어 모델은 학습을 기반으로 다음 단어를 예측하는 방식으로 작동하며, 실제 이해나 논리가 부족합니다.

이러한 모델을 생산이나 의사 결정과 같은 실제 행동에 연결하면 윤리적 문제가 제기됩니다.

AI의 정렬, 위험, 혼란에 대한 현재의 센세이셔널리즘은 AI의 실제 사용과 잠재력을 가리고 있습니다.

비즈니스 혁신에 있어 마인드 스튜디오의 힘

마인드 스튜디오는 조직과 개인이 프로세스를 자동화하고 맞춤형 비즈니스 애플리케이션을 만드는 데 사용하는 코드 없는 플랫폼입니다.

수많은 기성 SaaS 제품을 대체하여 효율성과 품질을 높이는 것을 목표로 합니다.

수천 명의 사용자가 마인드 스튜디오를 활용하여 워크플로를 자동화함으로써 현대화 및 효율성을 위한 강력한 도구로 활용하고 있습니다.