Latest / The Expert Method / How Leaders Should Roll Out AI at Work

Transcript

How Leaders Should Roll Out AI at Work · The Expert Method

• speaker-0: How are we going to help people get real results? Buy-in is earned. And it's earned clarity with care, with listening, with participation. Having a caring heart for your people first. speaker-1: What do you think the right process for AI adoption is for organizations? speaker-0: build a new culture, a culture of AI powered continuous improvement. speaker-1: Change is the only constant and AI is the change that companies are dealing with today. A few years from now, it'll be something else. The companies that navigate this change effectively are not going to be the ones that chase every new tool. They're going to be the ones that know how to help the people within their organization understand the change, apply it to their actual work, and keep moving without turning the whole organization into chaos. So with that, I'm excited to be joined by Mike Berksmith. Mike is the founder of Best Results AI. and he works with small and mid-size organizations to turn AI adoption into measurable changes in how work gets done. In our conversation, we discuss why AI deployment depends on change management, and how buy-in within your organization is earned through clarity and participation, why the people closest to the work need to be the ones that redesign the workflow, and how leaders can choose better AI use cases before they spend time or money building the wrong thing. If you get value from this conversation, be sure to subscribe and share it with a friend. And now here is my conversation with Mike Berksmith. Mike, thank you so much for being here. speaker-0: So good to be with you, Ian, and looking forward to our chat. speaker-1: Absolutely. So Mike, today a lot of businesses seem to approach AI deployment like it is purely a tech problem or a tool problem, right? Find the right tool, give people some training and hope that productivity or whatever outcome you're seeking just kind of happens. And if that doesn't happen, the assumption is often we must just be using the wrong tool. But your work and a lot of what you talk about seems to suggest that once AI starts touching people's actual roles, workflows and performance expectation, The problem shifts. At that point, it is not just how do we use this tool. It is how do we help the organization and the people in it adopt a new way of working. And that starts to sound a lot more like a change management, more like change management than just a tool implementation. So how did you arrive at that view? And what does change management actually mean in this context? speaker-0: You know, it's a good observation that we start thinking about technology because it's novel and it's powerful and it's interesting. And then we realize, wait a minute, we need to get some real work done. And it's in the process of figuring that out that we realize that with AI, generative AI that is, it's pretty friendly. It's really smart. It's pretty powerful. You quickly outstrip the question of which tool. And you move into how are we going to help people get real results? How are we going to help them think differently a little bit differently about their work so that they can delegate pieces of their work they've never been able to delegate before. And that means a change. That means different workflows, better workflows, less time required to get them done, usually better quality and often innovation. Now that leads us to change management because change management is about helping people understand why are we doing it this way? What's it going to look like when we get there? And do we really have a plan for getting there? I hope so to build my confidence. And then once we do get there, what is my role going to look like anyway? Because I want to know where I'm going. At least some people want to know that. So that is kind of a summary of the change management opportunity that's associated with AI and it is in every functional area of an enterprise if somebody wants it to be. speaker-1: Yeah, mean, change is the only constant, right? And change is the like productive change or the right change is the only way to continue to grow your enterprise, grow your organization. So getting change management right and making it an intentional part of your strategy or an intentional focus for you as the leaders in your organization is critical if you want to continue to change for the better within your organization. And obviously AI today is a big part. of change, but change management was just as important 10 years ago as it is today, as it will be 10 years from now. So I'd love to ask you, like, what does effective change management look like in practice? speaker-0: You know, in practice, it comes down to having a caring heart for your people first, because that'll motivate you to be kind enough to be clear about where you're going with them. So whether it was 50 years ago doing change, or as you said, 10 years, five years ago, leaders who are caring leaders will clarify where they're going because people love to know. They want to know why we're going there and what's it going to look like and how are we going to get there and what's my role going to be, as I just mentioned. And the effective work comes when we combine all of those four things with frequent, safe, open conversation. Because we're designed for fight or flight, as you know, if we study biology, right? And so when change encounters risk you know, is what we encounter. Then we're ready to run or we're ready to freeze and maybe fight. And so we don't want those reactions. We ease people into it in a way that they can embrace it. And who better than leaders who already know these people? They know where they're going to get there. And they know the work people are doing presently and they have a pretty good idea of what it's going look like after. So to care for them enough to become clear in all those areas and then to be humble enough and empathetic enough to really listen and say, you know what? I acknowledge that this is uncomfortable, that you're grieving over the loss of the way things used to be, or maybe your identity was attached to the way things used to be. Maybe your comfort zone is being encroached upon and it's okay. for right now. Let's talk about it together. Then let's talk about how we're going to go to a new place and what that might look like and feel like for you when we get there. You know, that kind of caring dialogue is huge. So we got this combination of preparing for the bigger picture that I talked about. And then we've got that back and forth and back and forth. And remember I said it was a safe conversation? It's a dialogue and it's frequent. So they need to, you know, the top leader needs to be open to hearing. The next level manager needs to be open to hearing. The direct supervisor needs to be open to hearing. Say frequent, open dialogue is what clears the way for people's minds and hearts to be ready to embrace a better way, a new way. speaker-1: Yeah, this isn't just a conversation that you have one time and to a huge group of people, like the entire organization all at once. Now this is something that needs to be done regularly and ⁓ frequently, it sounds like, which totally makes sense. And I love the emphasis that you're putting on it being a conversation and a dialogue with the leadership, with the rest of the organization. This isn't just a dictation, a mandate that is going out from leadership to the masses. Right now, this is a very personal, intentional conversation. And it's a project that you work on together, right? You need to have buy-in from the rest of the organization in order for the change or whatever it is that you're attempting to do actually come to fruition. So I love that. So, and maybe somebody who's listening to this might sound like. The point that I'm getting to is you can have these conversations in conjunction with the other regular conversations that you have with the team and the organization already. Right? This is just something that you can add to those weekly check-in calls, which those monthly strategic meetings or those one-on-one conversations that you have. But you need to find a way to intentionally work into those conversations that you're having with the team, with the organization. Work this discussion around change and what this future is going to look like for them and then tie it to the. to the outcomes and the things that those individuals care about, is that correct? speaker-0: You know, it's interesting that unless we're doing a narrow scope of AI deployment, most organizations decide once they see how much time they can save and how much innovation they can do, they decide to do it fairly broadly across the organization. And then when they come to that point, they realize, ⁓ we're doing so much new work in new ways. We should probably do an AI Havill every week. Standup meetings where we gather people who are maybe are subject matter experts or our AI champions together with others on the team and just talk about, hey, how's it going with AI? Anybody hit any roadblocks? Would you need a little help with a breakthrough? Let's celebrate what just happened over here, right? That this person just saved 20 hours a month and ⁓ customer satisfaction scores are going up. So let's talk more about that, right? So you're right on frequency. matters and integration because one of the goals we have for customers is let's build a new culture, a culture of AI powered continuous improvement. Pretty soon everyone knows what's possible with AI if they've been properly trained in AI. Once they know what's possible, they start brainstorming with each other like, golly, we could use AI over here. That would make this better. Maybe a whole lot better. Let's go there. That kind of culture of AI powered improvement is worth a fortune, frankly. It doesn't matter if you're for-profit, non-profit government, it just doesn't matter. You're going to find the ability to get more done with less in less time. The cycle times get much shorter on getting things done. Yeah. speaker-1: And without sacrificing quality too. speaker-0: Yeah, without quality sacrifice. In fact, the latest versions of the agent builders that we've put in our ecosystem have a quality dimension embedded into them. So every time you build an agent, the expectation is you have to define your standards. You have to come up with a way to measure them. We teach the agent to evaluate its own work based on a rubric or metrics that reflect your standards. And AI will actually have to go back and redo its own work by itself until it hits the threshold you set for quality before it delivers the first draft to you. So you're not doing all those iterations, you know, back and forth with AI. It's actually doing it, simulating it inside its own mind, if you will. So quality matters. Rework costs too much. Banturing back and forth with AI is not the way to get that done. Way to get it done is have an ecosystem that builds agents really well upfront. speaker-1: You had talked about the culture of AI powered innovation and continuous improvement. Is there anything else that you believe has to be true before people are willing to change their behavior? Where you talked about the cultural piece, you kind of need to embed it into the culture. But what else has to be true before people are willing to make a change in their behavior at work? speaker-0: You know, part of it comes back to leaders being willing to speak from their heart as well as their mind. You know, saying things such as, boy, when I first got introduced to this AI world, it was like, probably many of you are. And that was, I had lots of doubts, maybe skepticism, maybe bordering on cynicism even. And so I had to go into a discovery phase. I had to search my own concerns and fears and doubts. And now I've gotten to the level of discovery where I am developing some confidence. So they need a role model who is describing what they're going through in a semi-vulnerable way so that they can relate. then secondly, they need to understand, as I said earlier, what will my role look like? What is my part in it for the future? And by the way, does that improve my professional? trajectory? Might that improve my compensation or bonuses? Might that make all of us better off because of profit sharing? Might it allow us to expand our market share or diversify into more innovative products and services, which would be very interesting to many people from a professional standpoint. You know, there are many dimensions to motivation. And that's what I'm getting at is as a leader or manager or supervisor, let's get to the level of our individuals and their motivations, you know, to help them be ready for change, if that makes sense. speaker-1: Absolutely. And I think what I'm hearing is similar to a lot, like it rhymes with lot of the other conversations that we've had where there are these inherent fundamentals, principles that you have to have within your organization in order to effectively adopt any change. There's no difference because it's AI, right? AI is just kind of the current moment that we're in, this current like most hot button topic of change that organizations are going through now. So that's why we use a lot of those, we use AI as the example in a lot of these explanations. or these discussions at the root of it within your organization. And in order to effectively adopt any change and or make any changes in your organization, it starts by first having the culture of continuous improvement. You labeled it as AI powered continuous improvement, but I would argue that like within your organization, you need to have that culture, that value of continuous improvement embedded into it inherently. Like Amazon has this great core value that we stole from my marketing agency. I've stolen from my new organization, but it's the core value of it's always day one. Right? We're going to approach everything that we do with a day one beginner mindset of looking for opportunities and how we can make how we do what we do better. Anytime we use the excuse of that's how we, how we've always done it. That's when we're going to turn the page into day two. And that's when we start entering the slow, painful decline within our organization. Right? So that inherent truth or principle existed before AI, it'll exist after AI. And then having that embedded into the culture, that culture of continuous improvement, looking for ways, how can we be better? How can we. improve how we do what we do and deliver better results for our customers or for our team. Then it starts, okay, the leaders have to set the standard, right? The leaders have to set the standard and they have to be the ones that facilitate this change. But I can kind of stop the rant and just ask you, like, is there really anything uniquely different? Like, aren't these just inherent principles that have to exist within the organization? And then when you are selling change in an organization, right, you need to sell it to the outcomes of the people involved, like you can't again just make it a mandate. So is there really anything uniquely different about AI as it relates to kind of these principles that we're talking about? speaker-0: I think the core principles of change and leading change, especially as a servant leader who really cares, are similar. There's another element in AI that has to do with the willingness to invest in upskilling because AI can take people, and I am seeing it daily. saw it today, facilitating a cohort of learners who were fairly well along in their workshop series. And guess what happened? The light went on and they realized they could do things with AI that they could never have done before. They're so far out of the box. There's so much access to research, to expertise, access to simulating the solution of very complex problems with multiple pathways and options, all of which AI can assess identifying risk and opportunity and making it measurable, building out business cases. These are things that people simply didn't have time to do before. Now, some of your people in your organization, given the opportunity and given the training, will rise to pursue these things that were never before possible. I think that's the one thing about AI that's so exciting. The second thing about it is we're getting to the point where We can build revised new AI workflows faster than organizations can digest them. That is to receive the new way of working and embed it or weave it into the fabric of the way they conduct their operations. And so what this means is it's putting a little bit of a push and they're having to balance. how fast are we going to work on our high priority use cases anyway? Because honestly, we need to maybe finish this one before we try to deploy this one, particularly if they're cross-functional, cross-departmental, maybe even cross the entire firm type workflows. And so I hope that was helpful. don't know. It is one of those things where AI is taking us to new places. speaker-1: Absolutely. And you touched on it there with your second part of your answer where with AI, in every organization, there are hundreds if not thousands of use cases. And you can't take all of them on all at once. One from the builder side, like building all of that all at once sounds like a nightmare. And then second on the adoption and implementation side. That's a lot for people to consume and learn. So you really have to... prioritize and identify what those highest impact use cases are for your organization first before you get into building you kind of lay out like the roadmap which is a perfect segue into my next question, which is I feel like a lot of organizations today and people that I talk to at these workshops as soon as they find out that I'm an AI guy they're like, how should I use AI in my business? What do you think about this tool that whatever like they're really excited, which is awesome But I feel like a big mistake that people make is they see a video online They see an advertisement, they see somebody post something about how they're using this tool. They start with the tool. They see a tool, they see an agent, they see a use case that somebody else created. And now they're like, okay, we want to take that and work it into our organization. I believe, and I would, I'm curious to get your take on this. Like, what do you think the right process for adoption, AI adoption is for organizations? Obviously you have to build your organization into a place where change management, like they're ready for change. But once you get to that point and you're ready to adopt AI and deploy AI into your organization, from my side, I believe that you need to start by looking at your organization first. Don't look at what other people are doing. Don't look at those examples. I think that's great to learn the capabilities, but you need to look internally in your organization to identify the highest impact use cases for your organization and then go out and build those. Don't take a tool and try and wedge it into your operations. But how do you approach identifying those highest impact use cases for an organization? And do you think that that's the correct order? with us, then go build what we need, not see what somebody else did and get it into our operation. speaker-0: You know, looking at what others have done might be instructed from a statistical or procedural basis, what not to do, for example, because it won't work. But I really concur that AI by its very nature can be, for the first time in history, the most personally customized resource ever. That means at the level of the one person, the one department, the one division, the one corporation, the one holding company. All of those can be addressed very rapidly in a very tailored manner. So for example, in our workshops, we have every participant build out their AI role plan matrix of responsibilities and deliverables in the first hour. And they start working on those deliverables in the second hour. And they're actually working on their work right away, getting results and being able to measure the gains. Now you can do that at the start as well. Take your strategic plan and all the tactical initiatives that are built into that by implication and by planning. Take your operating plan and you realize you got already got priorities in there. We're going to do this and this and this in that operating plan. What if you take that and you juxtapose that with what AI suggests for your organization might be the biggest bang for the buck. in terms of the hours put into AI and the return time and the return innovation and quality. What if you put those two together? What if you intersect your operating plan and what AI suggests about gains in your industry and your business, the size of firm you are, where you're playing, know, who you're serving. Put all that in the context and then say, all right, give me a recommendation. Let me talk with my leadership team about it. Let's all get on the same page because we're not aligned. We're going to be in competition internally. and a house divider does not stand. And so let's get on the same page. I think that's key is assume it's gonna be customized from the outset. Assume that you can put your trade secrets, your secret methodologies, your secret sauce, whatever that is, into the plan. You don't have to worry about what somebody else did because why the large language models are available to everybody. They're the great equalizer. That's why we don't put skins on stuff. put a wrapper on it, put it on top of a large language model and sell it for subscription. No, we just teach people how to use the large language model right out of the box and make it all transportable. If I get done with Anthropic, because I'm ticked off and I want to move up to OpenAI someday, ⁓ I can just move all my stuff because you guys have taught me that I do it native, right? Well, that takes us back to the customization. If you did it native and you did a custom, you have not painted yourself into a corner. You can go anywhere with this. And that's a better ROI in the long run. speaker-1: Absolutely. Yeah. And just to underline your point there that you made a lot of the other questions that I get are a lot of people really before they get into using AI or using AI within their organization, they really get hung up on picking the right platform first. It's like, ⁓ should we do Claude? Should we do Gemini? Should we do Chachapi T? Should we do N8N? Should we do relevance? And I'm just like, There is no right or wrong answer at this point, right? There is no absolute clear standout leader. You can learn like within all the different things that AI is capable of, right? Voice agents, conversational AI agents, agent builders, LLMs. There's going to be a handful of companies at the top of that capability. But no matter, no matter which one you start with. using all of them is going to look very much the same. So like if you build out your Claude and then three months from now you don't like Claude and you want to switch to ChadGPT, you easily can. Anything that you build on Claude, you can then go and duplicate and build on ChadGPT. So something that I really encourage people, just kind of pick one, get started. And then if you are using it, you get in, you build stuff out, you're using it you want to make a change, you can easily. So is that something that you encourage people? Just kind of get started, get in, get started, pick one, don't get too hung up on that because you can change pretty easily. speaker-0: You know, I like to use the analogy of the horse race where the horses are all in a starting block and the gates are all shut and the gates all open simultaneously and they're off, right? And by the first corner, one horse is ahead. By the second corner, a different horse is ahead. By the third corner, yet another horse ahead. And by the finish line, the guy comes from behind. Well, this is the nature of LLMs, the frontier models. Every two to four weeks. somebody leaps ahead for another few weeks until the next guy does. Thus, we encourage them to look at their ecosystem. Are you already a heavy Microsoft shop? You all want to consider Copilot. Are you already a Google workspace shop? And maybe you want to look at Gemini since now they've come out with some pretty good stuff. Or do you already have a bunch of BYO AI, bring your own AI to work people who are running chat GBT and they just happen to have a background level of knowledge. Well, why don't we just standardize on that, get them all trained up to a whole new level of knowledge. And then you all be just a chat GBT shop until you don't want to be anymore. And so, ⁓ I'm like you, I just say, let's let them consider their contacts, just pick one and go because a month from now we're going to be talking about the next release from somebody else. speaker-1: So what makes a high impact use case for AI, in your opinion? speaker-0: You know, the cool thing about high impact is we see in organizations that different functions have different degrees of leverage. For example, a CEO and the C-suite are responsible for the strategy of the firm. They might look out 20 years, 10 years, five years, somebody's got to do it. They have to own it. They've got to make the decisions. So for example, decision support is a huge value. AI use case, people don't talk about it a lot, we build into our workshop series a decision support dedicated time. And the reason for that is probably obvious once you think it through. And the same with strategic simulation. I mentioned earlier that AI has a lot of power in simulating alternatives and it can do so by simulating an entire model, not just a single thread, not just a single function. but multifunctional simulation. Now that's highly leveraged work, which is at the strategic level. At another level, if we're talking about operations, we're talking about content development, talking about analysis and representation of dashboards, the stuff that it used to take Power BI to do and now AI can just do it. All of those bring support. They all bring value. The question is, have we really considered where we'll get the biggest bang for the buck by the time we put into it? And it turns out AI can simulate that as well. So let's say I have a roster of 60 different deliverables in just my job. Well, why don't I just go into AI and ask it, just simulate semi-automating or automating all 60 of those before I do anything. And force rank where I can put in the least amount of work with AI and get the biggest result and it'll do it for you. And you can decide whether that fits your plans or not and whether you want to marry the two that we talked about and get it done, you know? So this work is, ⁓ you're going to be assessing value based upon speed, ⁓ efficiency, quality, potential for innovation and potential for collaboration. Because when we look at business process, often what happens, well, can I give you an illustration? I'm working, let's say, with a half a billion dollar home builder. And home builders ubiquitously go out and buy dirt from somebody and get it developed and then build homes on it. Well, between the time you buy dirt and you break ground, it's called soft cycle time. It's everything you've got to do. Get your permits, do your demos, get the trees out of the way, whatever you got to do to get ready to build. That is costing you a fortune. got all this capital tied up and you got all these people waiting to pull the trigger. Well, I got about a dozen people in the room with that group and we started flow charting their soft cycle time. We started identifying, you know, weeks of cutting, but we found a day here, two days there, half a day here, three days there. Pretty soon, soft cycle time is cut by 30%. Incredible. It's worth a fortune. This is a principle when we're doing any workflow re-engineering is to go to work together. If you can collaborate and you can cross functional disciplines, you can very often find that the handoff or the minimal data necessary to get the handoff done right isn't being done right. It's not being done in a way that's friendly to the recipient, for example. And man, if we can get in and dig hard and innovate these flows, we're going to get more value than we can just by being faster. Okay. speaker-1: Absolutely. And just to underline that point too, why do you think it's better? Like, okay, let's say one hypothetical example. This company just hires best results AI. Best results AI comes back and we're like, okay, based on the work that we did, here's what we think the flow should be, like the updated flow versus the example where you get in, you have 12 people in the room, fully engaged in the discussion, they're contributing ideas, you're asking them questions. Like why is that second version so much better as it relates to like the engagement and the buy-in of the people that you're going to be helping as opposed to them, you just telling them what they need to do pretty much. speaker-0: it's super hard to beat a subject matter expert at their own game. So let's say I have 12 subject matter experts in the room. They all own their stuff, they know their stuff, they know how they connect to the other people in the room. And when there's a facilitator there, or maybe you teach somebody within the company how to become that workflow facilitator, so they're not dependent on the consultant anymore. Those subject matter experts together will always be smarter than one person. They are even going to be smarter than AI by itself, if that makes sense. Because AI by itself is gonna make great recommendations, it's gonna suggest workflows. They're very often good workflows, but what can happen is... It's not really seeing the context as richly as the human beings who are in the middle of the work. Right. speaker-1: Yep. And that is a line that I repeat all the time that any AI output is only as good as the context that it has. Right. And that is really the core driver or the core principle of great prompt engineering is making sure that you're equipping it and you're giving the necessary context, the clear direction, the clear instruction in your prompt in order to get the highest quality output that you can. And if you're just working with AI by itself, You talked about the piece where AI is really the great personalization of software and technology. And I completely agree with you where if AI is just operating with the consultant, the consultant has limited context. The AI has limited context about how that team or that individual goes through their own personalized, unique workflow through the process. So it's just going to be very generalized. But if you're working with those people, Not only is the quality of the output going to be improved, the quality of the workflow recommendation, the workflow re-engineered recommendation, but also kind of going back to what we started our discussion with, those people are going to be a whole lot more bought in to this idea and bought in to adopting and using this new thing that we built because they participated in the conversation. They were allowed the opportunity to contribute their ideas, which then just goes back to the fundamental principle of organizational buy-in. don't know exactly what the discussion would be, that just makes me think of a Patrick Lincione book right there. speaker-0: Well, know, buy-in is earned and it's earned clarity with care, with listening, with participation, with asking questions, probing, going deeper, asking the five whys, even playing a root cause analysis journey when you bump into somebody who's resistant and go deep with them. You earn buy-in and buy-in sometimes requires investing in education. Because if I've got an employee or a colleague and they don't really understand the subject matter related to AI yet, but they're struggling with it, I think I'd be making a great investment to get them up to speed on it. Then we can have a real conversation about it. And that conversation is going to be a lot more meaningful than just, I'm skeptical and I don't want to talk about it because I saw it hallucinate once and now I'm a cynic. speaker-1: Yeah, absolutely. Amazing. Mike, thank you so much for the time. I have one more question for you. But before we get to that, where can people find you? speaker-0: You know, my email is the easiest in the world. It's Mike at bestresults.ai and ⁓ we have a website that's simply best results AI. And so super simple. And ⁓ people want to come there because we give away free custom AI deployment roadmaps. give away free AI policy and governance workshops. We do that because we think it's the right thing to do. So people get it done right the first time instead of squandering time in my speaker-1: Absolutely. Yeah, Mike, my final question for you is a recurring question that I ask every guest. But if you had a giant billboard that everyone in the world could see for 10 seconds, what would it say and why? speaker-0: Walk humbly, serve in love, honor God. speaker-1: What does that mean to you? speaker-0: Well, it means that the richness of who we are uniquely designed to be as human beings emerges when we're thinking about others first and when we're full of thankfulness because thankfulness is like a drug of choice. It's been proven by neuroscientists that we're filled with gratitude that we're actually more innovative. Our prefrontal cortex is firing fabulously when we're full of gratitude. And I found in my own journey that really discovering a relationship with God was what led me to the ability to be grateful in all things, not just the easy things. And what that does is it makes me more innovative through all things, including the hard things. Does that make sense? speaker-1: Absolutely. Thank you for sharing that. Mike, thank you so much for the time. speaker-0: Yeah, you bet. Good to see you. speaker-1: Thank you for listening. The main thing I took away from this conversation is that AI is just the current change that organizations have to deal with, but the fundamentals of change management have not changed. The tool is new, the pace is faster, but people still need clarity, context, trust, repetition, and a real role in how the change gets applied to their work. Mike made the point that buy-in is earned through care, listening, and participation. That matters because the next major shift after AI will ask the same question of leaders. Can your organization absorb the change without losing the people who have to carry it out? The companies that build that muscle now are not only going to be better at adopting AI today, they are going to be better prepared for whatever comes next. If you want to get connected with Mike Berksmith and his team at Best Results AI, you can find the link to his website and other resources in the description. And if you run a coaching program, mastermind or course, and you know that your clients aren't getting the results that they could be, download the free program execution system toolkit in the description. It'll show you how to build what I call execution infrastructure that will help more of your clients get the result and the transformation that your program promises. Thank you for listening and I'll see you in the next one.