Episode Transcript
[00:00:00] Speaker A: Somebody goes wrong, I want to know who to yell at. I'm not going to yell at the AI. Is it a technology failure or is it an organizational alignment failure? Organizations have a roadmap for what that looks like, like really well defined expectations of what the pilot can do. I like to say don't subscribe to the field of dreams model. Like I don't think if you build it, they will come. Is the goal now just to say AI is going to do the demand plan for you, or is it actually to think about how do we make each one of those people more productive?
[00:00:26] Speaker B: Welcome to The Entrepreneur's Electro Logbook I'm your host Zach Benard. You can find me on social at Zack B. In each episode I bring on experts from various industries for to learn about their strategies and insights driving extra business growth. Today we're joined by Russ Halper, founder and managing director of Insight Kitchen and the AI native consultancy focused on something that a lot of firms talk about but they rarely deliver on operational AI embedded directly into into the processes that actually run a business by leveraging deep cross functional expertise in supply chain, revenue management and AI. Russ his career, interestingly enough, started with a first project at the the real Willy Wonka Candy factory where he worked on production scheduling. And from there he spent the next two decades at the intersection of AI, data science, supply chain and revenue management working with various Fortune companies. He helped grow a boutique analytic firm from 16 people to roughly 100 across three countries before was acquired by Censure where he went on to lead their west market unique generative AI practice. And then after seeing firsthand how the traditional consulting model is kind of misaligned but what it actually takes for AI to drive real outcome, Russ decided to, you know, step out to build Inside Kitchen, a firm that's designated to round the result instead of the deliverable aspect of things. Russ, it's great to have you on the show. Welcome aboard.
[00:01:54] Speaker A: Thanks for having me. I appreciate it.
[00:01:57] Speaker B: Cool. Well, one of the things that I always like to ask anyone that comes on the show, it is a business, it is an entrepreneurship podcast and you obviously been around the block to put it very simply here is if you had to restart your company or just take Insight Kitchen specifically if you had to restart your company from scratch or what's maybe like the one thing that you feel you would do differently.
[00:02:18] Speaker A: It's a really good question and there's one answer here where it's not one thing.
There's a lot of things you can look and say you can do differently and Some of them are little, and some of them are big.
But if I had to think about what the journey's been like in the past couple years as we got started, one of the things that I found the most interesting or unexpected was the emotional journey that comes with actually starting something like this, where you're leaving a large organization to go kind of into the wild to start something new.
Had some experience in the past being a partner at a young firm, but started from scratch. Is a whole different world.
And then there's a part of it, of how do you get started? How do you go and put something in the world that doesn't exist?
And then as you start bringing people on board, you get to see them be able to not only contribute, but also be a part of it. And the thing that's been probably the biggest learning for me along the way is not just for me, what an experience it's been, but being able to connect some of our team, some of the opportunities they have, and even in a relatively short time, seeing them grow.
And so I think when I look at what would we do differently, it'd be more about how would I set myself up for the expectations of what that journey is going to look like. But I think part of it also is like, you just got to get started.
There's no way to figure it all out. There's lots of little questions I figured out along the way. We figured out a way you could be like, oh, should have done that different, and this different.
And I don't. I don't think you could look at those and say, we should have done those differently from the beginning, because there would have been no way to know what you should have done had you not gone through some of those experiences.
[00:04:09] Speaker B: Yeah, I feel like that question, I always like to ask it, but in a way, like, the way I look at it, it's like, hey, you have to go through these steps to learn, improve, adjust. Like, you're not going to get to where you are now without going through these steps first.
[00:04:25] Speaker A: Yeah. And it's like, you know there's a thousand sayings around this, Right. Just get started. Don't let perfect be the enemy of progress.
Even in the relatively short period of time that we've been doing this, AI has shifted dramatically.
You have other learnings that you have to adjust in the marketplace. How clients are receiving things, what capabilities you could bring to them, how do you need to grow the expertise in your team and keep them cutting edge.
Those are things you can't learn up front. Or you know, up front you got to learn them and adapt as things evolve.
[00:05:00] Speaker B: Yeah, but yeah, like even like the emotion set of things that you mentioned, like if you like as like an entrepreneur you just go on like the roller coaster. Like it's a, it's an up and down battle, you work through it. I mean you can't really get ready for it. It's only when you jump into the ring that you really see like what that emotion looks like. And I mean for better or worse, I feel that it's worth doing. We always, everyone has a different career path but as entrepreneurs you kind of have to dive into deep end here but swifting gears like a little bit here because we're, we've had like a, I would say like a fair amount of people on like the show, like a lot of different backgrounds, like marketing, consulting, leadership, all that fun stuff. I don't think we've had like anyone, I mean I could be wrong, but I don't think so. I don't think we've had anyone on like the more like operational like AI side of things. Like I'll tell you, we have a lot of people that talk about using AI, leveraging AIs, but it's more like they got into it like two years ago or something like that. They're using chat, GPT and everything. But it's less like really integrated within like the company really on like an operational level. And that's like some what you've been doing like the past like couple of years. And one thing I'd love to understand is like how Insight Kitchen works like when someone comes to you, obviously there's different type of clients like companies that you work with. But I love to understand like how that process looks like to be able to actually create something that gets implemented and embedded within like the day to day operation like the long run. And it's not just a project that lasts a month and then no one uses it after a couple of weeks here.
[00:06:32] Speaker A: Yeah, yeah. So great question. There's a lot there. I'm going to maybe unpack a few pieces of it to get to the final answer.
But when you, so, so when you look at new technologies that come out right and, and what you know, generative AI has been agent again in the past several years. Right. It's been just incredible.
There's often a technologist first approach on how you want to bring those to market because that's where the origin of that is coming from. You have organizations that have strong venture funding behind them, obligations to scale and it drives certain behaviors and how they bring that stuff to market and want to be able to approach things that have broad applicability across broad areas of business. You have a better client portfolio or a larger client portfolio, larger market you can address.
And so what we often see, what I've seen kind of in a few times when you think about new technologies, is you come up with horizontal products you can put in the marketplace as the starting point and then from there you bring in like more specific things that get built with those as capabilities. And I think AI is like no different there.
Where we've really decided the focus is in spaces where you have to have deep domain expertise to drive effective solutions.
And that becomes hard oftentimes when you have like a horizontal technology. Because as smart as it may be, as revolutionary as it can be, may be, there's very specific operational constraints or strategies or theory that needs to be brought into place to make that actually effective. Right. So supply chain is a great example of that, of when you think about what does it mean to drive solutions for supply chains, you can't just know the math or the data science. You need to also understand what does it mean to have a better forecast? How do you actually implement this in a way that drives value with the business process?
What does it mean for a business process of a building solution? What type of feature rich, information dense interfaces do I need for operational processes?
And how do you tie all those things together? And that was the gap in the market that we decided to go chase.
One of the things that is hard about that is you need to have that cross functional domain knowledge to be able to chase it.
And you can have two companies that have sell directly competing products with very different supply chains. And because of that, different ways you need to actually go to market. Right. You could have one consumer goods company that's doing direct store delivery to the grocery store, another one that's doing warehouse replenishment. And that might be very different things. For what does your transportation look like? What's the, you know, what's the way you need the capabilities of planning your supply chain? How do you think about distributing product forecasting and how do you then take all those and bring those into AI that actually drives measurable results is a hard thing.
So for better or worse, we chose to take on something harder.
But that's where we decided we want to put our stake in the ground and be great.
It's more an extension of what I've done for most of my career.
Having done before feels like there's also a bit of a rebranding, right beyond generative. Everything that used to be machine learning is now AI. And that included everything previously that was data science. That included a lot of things that were analytics.
A lot of these things are evolving and growing together. And there's a lot of confusion around messaging. And so we work with a lot of clients around how do we unpack all those things?
Typically when we talk to somebody, it's often coming from a functional need.
So when somebody comes to us, we had somebody talk to us about markdown optimization in a retail context and how do they think about better managing end of life inventory.
There's a lot of things we can use AI in that context for, but fundamentally the outcome still, how do I improve my profitability of that lot of product when I think about my average realization is sales price.
And so the other thing that we see happen is a lot of times organizations don't really know where they want to start with it. And so there are times when we're coming in to help them figure out what's the roadmap, what's obtainable, what are the things that you can do today.
And it's a bit of a challenge because one, the technology is evolving really fast.
But if you want to go from like a POC to something that's actually working today, it's got to drive value today. And so there's this world of we want to drive value today with what exists today, but we also want that to get better as AI gets better. Right? And so we can help organizations. We help organizations in those ways. We've helped them with those more point solutions. And other times around, you know, how do we build AI or data science? Machine learning is now branded sometimes as AI into products that also work in this domain.
There's not necessarily one path to market or how we help.
[00:12:08] Speaker B: It's different.
[00:12:09] Speaker A: But it's always about that cross functional lens that we're able to bring.
[00:12:14] Speaker B: And I had an interesting conversation, I think maybe a couple of days ago with someone else on the podcast and he was talking about, everyone is saying, oh, we need to be integrating AI within like what we do. Like I'm hearing like all these different use cases, like we need to be on AI, like we cannot like fall behind.
But I have a feeling, and correct me if I'm wrong here, that sometimes maybe people are like rushing into it, that they don't know how to effectively approach it. They just decide, oh, we need AI, let's build this thing. But they haven't like, proven that they should actually be implementing it. Like, you do the math. It's like, okay, it's going to save us like an hour or a week, but it's going to take us like five months. Like, build, like, is that really worth, like, building it here?
[00:13:00] Speaker A: I mean, like, businesses are businesses, and there needs to be the roi. Right. And what you're going to do, of course, I think AI has got a bit of a brand out there, which is.
Which drives a lot of iteration in places that it shouldn't. Right. If you abstract the name AI, artificial intelligence from what it actually is, Right. And you think about the things you can do with it. There are great things and organizations, I think, rightfully are trying to figure out how do we bring it in. But sometimes there's a desire to shoehorn it in the places when it might not fit or might not actually really drive the value. Or it's maybe a fairly crude way to think about how it might help.
We just want to replace people versus thinking about what's actually driving measurable outcomes for the process.
And so it's. It's confusing. And that's one of the places we see. We've seen companies kind of fail or failure modes, right? Is. Is that strong desire to. No matter what, we want to leverage AI. So I often tell people, look, we'll help you figure out if you should use AI, right? How to use AI. And then, you know, if you want, we can help you build the AI.
[00:14:17] Speaker B: Yeah, I think the reason I was asking that I mentioned that person I mentioned to those on the podcast mentioned, yeah, I was speeding. A Fortune 500 company CEO. I was like, hey, not CEO, CMO. He's like, oh, I'm seeing all these little things about AI. We need to be doing this. And he started working with them. He's like, do you actually need this?
Why? And then once you dig into it, it's like, okay, maybe we need to reevaluate the situation here.
But I feel it's always a different story with, like, everyone depending on, like, your use case and everything. But one thing that I feel is like a pretty common one, and I'd be curious to hear, because you're a data guy. Like, you obviously would know that it's like, one thing that you hear a lot is that a lot of people are like, hey, I need to have my data, like, perfectly clean before they even start thinking about AI. Because they're like, oh, if my data is not clean, I feed it to the AI. It's going to be Like a complete mess. Is that actually true or is that more like a myth? That's like holding people back here. We can play mythbuster here.
[00:15:19] Speaker A: I think it's putting the wrong constraint first. Often, right, not always.
I could find cases where that's an issue.
But when a lot of clients that we've worked with, what they're thinking about first is let's drive the outcomes for the business. And so if you start, if you start that as your foundation point for how do you think about what are the capabilities you need then to drive those outcomes?
Data becomes part of that.
But with the data an organization has, you can have better outcomes with that same data. You can imagine two identical organizations with the same quality data. And I mean every organization has data issues. I can think About a top 10 apparel and footwear retailer I've worked with where I could argue the data was all over the map and a mid size apparel footwear retailer I worked with that's been very disciplined as they grow about building reasonably good data.
And so one way I like to think about this is you just got to get started. And sometimes the best way to actually get your data better is to pull it along with the business initiative.
There's oftentimes places where it's like you will spend infinite amounts of time working on your data and never actually get to the point where one, you feel like it's great and two, then you're actually getting the outcomes and the value from that.
This is one of the things where I see a lot of large consulting firms play is data driven transformation and wanting to start with these large data programs. And sometimes it's important if you want to be able to have views across wide swaths of complicated organizations, you need to have access to all that data.
But I think increasingly the need to have it perfectly harmonized and co located is less important than actually the need to have the context around where the data is and where it sits. Because we have new capabilities that allow you to be able to access that data in different places and be able to better work with it. And rationalize takes work.
Right. But it's a qualifying factor. But I think it's too often used as a stage gate for progress in a lot of situations, especially when you're getting functionally specific in certain areas of the business.
It could be hard.
[00:17:53] Speaker B: Interesting. Some people are using that as an excuse like oh, the data is, it's not like organized, like maybe, maybe we don't use it yet. I mean a good example of that we were like Building like a, in an internal tool. We had like 60,000 rows. And I know in your world that's probably not a lot when we're talking about like data, like data like a Google sheet. It was a complete mess. But I was like, you know what, maybe we take a bit of time, we organize it, but I push it off for so long because it was like I don't feel like cleaning all this data to be able to use it. And then I took a weekend like you know, let's, let's clean like the basic of it, make sure that I can use in like some function. And now we have pretty clean data. It works for what we want to do. It's helpful. And I feel like a lot of like leaders, you know, like push off like doing the initiative to make sure that they have the data they need. It doesn't obviously need to be 99% there anything but I had a large.
[00:18:50] Speaker A: So Fortune 500 client has stocked at some point. It was for like a specific supply chain function in that client.
This is a decade ago, right. This is a 4 chatgpt, right. You know, back in the, when big data was decreased, I remember them telling me we got a big data problem.
We got a couple hundred thousand rows in the spreadsheet.
But I mean labels aside, right. Again separate the brand from the technology that was imported to them. Right. Those, those couple hundred thousand rows were really critical for running that business process. So sure, we've, we've, we've had situations where there's most time much larger data sets than that. Yeah, but that's what they needed for them to be successful when they were building this solution. Right. And that was their hard problem that they were trying to overcome. So I think there's a maybe, maybe a little bit less principle than many when I come to brands and things around technologies and things like that and more about let's get after it and let's show we can drive the measurable outcomes for businesses. And especially when you're working in operational areas, working in supply chain, working in revenue management, most of the operations are fairly quantifiable.
You look at supply chains that are often cost and customer service driven and there's, you know, well, I don't think we need to get into it, but many, many metrics that are important. And you know, when I looked at our semiconductor clients, right. The way they think about measuring success in their supply chain is very different than a consumer goods company, which is very different than like a parcel delivery. Right.
Service.
But it's generally Fairly quantifiable, which means you can do different types of things in there. Now the other challenge is a lot of it's very numerical because it's so discreet. A lot of the actions and a lot of actions can be captured well in data.
You have lots of numerical like decisions that you can measure with actual numbers.
And now we're trying to apply technology which is really, really good with text and images and video and be able to apply it to the data and how do we actually get it to drive outcomes for us. And it's not to say that it's, it's not to say at all that it's bad in those areas. But you know, I work with, I was, we have one client which is a very well known Silicon Valley giant and I was talking to the VP there about their forecasting and I'll say they were very invested in AI. It's a core part of their business.
And he was saying, yeah, no, we can't use it for forecasting. We use it to talk to the forecast to describe scenarios. We get the other models to run scenarios for us.
But it's not the same as just throwing it and hoping it's going to give us a good forecast. At least today that's where we see it.
[00:22:04] Speaker B: Interesting.
And one question I wanted to ask you here because I feel like this applies not only to small businesses, large fortune companies or anything like that, but you hear a lot about companies starting an AI pilot works well. You know, a couple first weeks you're like, wow, we spent all this time building this, it's working great.
But after, you know, a couple weeks or so it just never goes like company wide where like everyone is like using it. It doesn't get like actually integrated within the company's operation. I'd be like kind of curious to hear like what's actually happening, what separates a, like a project that actually sticks versus one that just dies after the demo or like the beta here?
[00:22:54] Speaker A: There are several things that can affect that.
I break it down often into two broad categories, right? Is it a technology failure or is it an organizational alignment failure? And those two things aren't necessarily discreet.
But oftentimes when we think about going from how do we run a pilot and know if that's going to drive the outcome we want to have and be able to prove that, you know, we can, we can go from there forward.
We think it's really important organizations have a roadmap for what that looks like, like really well defined expectations of what the pilot can do and you know, hopefully they're working with partners that don't oversell what the technology can get get done for them. And that's, that's one of the biggest things that we see is organizations picking partners, be those consultants, technology vendors or whatnot, that in order to win the business are willing to dramatically over promise what can come out of that.
Run a pilot.
Things end up well short and then you have not just the challenge of the gap in expectations but now you have an ongoing trust issue with, with them as a vendor or as a partner along the way. And that's, that's one of the places where you know, generally a pilot is going to have a gap in it. That's why it's a pilot. But it's important to make sure that there's a lot of clarity and transparency on where those might be up front.
And you know, how we get from pilot to something that's a little bit more robust and is actually attainable in the, you know, short to medium term.
[00:24:38] Speaker B: Yeah. Having, having some expectations set and like alignment, like where it's going to be used, how it's going to be used. But I like the fact that you mentioned like the organizational alignment because I feel like a lot of people are going to create this amazing product and it's like no one uses it. It's like why are you not using it? Like maybe there was some, a bit of market research, like market feedback you needed to collect before creating the tool because maybe no one wants to use it because it's useless and it's just slower. It takes more time to do what they want, what they're like used to. So I imagine a big part of the puzzle is making sure that people actually like integrate that within their cult, maybe not culture but like their day to day using the tool. Is that something that you see being like a big issue with a lot of companies on the like alignment side of things. Yeah.
[00:25:22] Speaker A: I'm going to say too there's a case when people don't use it, but there's also a case where people don't use it or people like using it, but it doesn't actually drive anything different in terms of an outcome for the business.
[00:25:32] Speaker B: Right.
[00:25:32] Speaker A: So you know, you can get something that's used and all my results that I can measure in the back end all look exactly the same. So what's the value of me making that investment in that?
[00:25:44] Speaker B: Yeah, that's fair.
[00:25:47] Speaker A: You know, so I think there's a couple of failure modes that can happen.
[00:25:51] Speaker B: Yeah.
[00:25:52] Speaker A: With it. But in Terms of like the usage. I like to say I don't subscribe to the field of dreams model. Like I don't think if you build it, they will come.
You can have the best solution, the best technology you put in place in the world. But when people have to change the way they do their day to day business, their day to day work, that's hard.
Oftentimes when you're running a pilot, they're doing that work on top of their already existing work. So now you're adding just overhead to them to then prove out with this new capability that's telling them to work differently than it did before, that it's going to be successful.
And that's a hard thing.
That's a high bar. Right. That we need to make sure.
Organizations need to make sure that they get over when they're going through those types of engagements.
[00:26:39] Speaker B: Yeah, it's hilarious because we're having this conversation, I'm in the middle of doing that specifically where we're rolling out a new tool for the team, something different from what they're used to, making sure that they're using it. It's like exactly like what you're talking about. So I feel like it's an interesting conversation because I'm dealing with that right now. But I feel obviously we're a smaller company, but when you think about like these big fortune companies, it's like, oh God, like there's definitely a lot going on there that you want to make sure that all the wheels are in motion, the ducks in a row and there's no issues going on there because that can really affect the bottom line of a company there.
[00:27:15] Speaker A: Yeah, yeah. And you know, you have AI might be this great new technology, but people are still people.
Change is still hard.
I believe that organizations still want to hold somebody accountable if something goes wrong.
People.
I like to say that if somebody goes wrong, the CEO is going to ask their C suite, is going to ask the VP at some point somebody's going to be accountable for that.
I was talking to a friend of mine who's another entrepreneur, had a couple exits in the SaaS world and his words to me were damn right. If something goes wrong, I want to know who to yell at. I'm not going to yell at the AI.
But it's true though, right?
How they're measured, people are still people.
And it's easy to lose sight of that when we think about something new, like agentic models interacting with the business process.
[00:28:17] Speaker B: Yeah. Cool. One last piece of the puzzle. I wanted to touch on here before we like wrap things up is you talk a lot about like the difference between just like AI and like the operational AI side of things and like how a lot of industrial, like supply chains specifically have been like, very bit slow to adapt. Like these AI tools. Where do you see this, like, I guess heading over like the next few years and what should business leaders paying attention to right now? Because there's obviously so much things being said about AI, but probably not all of it is relevant for them at the moment.
[00:28:50] Speaker A: There's some areas where when you look at what AI can do, there's really, really natural fits. I mean, you see code and Copilot is one, right?
[00:29:01] Speaker B: Yeah.
[00:29:01] Speaker A: You're writing text. That's code and they're already geared towards writing text. So that's actually real great fit. You see areas in customer service and image generation and media and a long list of others that we won't have to go through all right now where it's really fitting well. And then you see places where it's slower to adopt.
And part of that is the complexity that happens in those areas of the business. And part of, part of, I think about, it's like, what's the cost of a wrong decision?
[00:29:37] Speaker B: Right.
[00:29:37] Speaker A: You. You show somebody the wrong video to select on a Netflix homepage. It's, you know, the cost of a wrong decision is not that bad. Right. You send a shipment to a wrong customer, that's expensive. Right. Both financially and potentially from, from a relationship point of view.
So, you know, there's, there's different bars. Levels of scrutiny need to happen to be able to use this in certain parts of the organization.
And there's different levels of complexity that's needed to make sure that it's done in a way that actually drives outcomes. And what that means oftentimes is the sort of the natural way people think about applying it doesn't necessarily translate well.
Originally, I think a lot of the thoughts were like, can we just reduce our headcount? I have, I have an organization where like we have a thousand demand planners or we want to get down to 400 and it's, it's okay.
Is the goal now just to say AI is going to do the demand plan for you, or is it actually to think about how do we make each one of those people more productive?
Yeah. And if you ask that question differently, you're going to get to different outcomes when you think about how you're going to apply it.
Not only that, in the operations world, a lot of the technologies that exist today take a long time to build. So a lot of the players in the planning space, I'd say most of them are technologies that were built long before AI came out. And so they're often ingrained in certain ways of operating and they're now trying to figure out how they can bring that into the platform or bring the capabilities and platform to leverage it.
And some are doing it more successfully than others. I think they're all trying, but they have to do it within the context of they might have a large client base of lots of different clients that are on legacy technology they have to continue to support.
So it's just a bit of the natural, when we talk about operational AI, really think about how do you embed it into the operations, bring it in a way that drives, that captures the complexity that is inherently there and you know, brings in the thoughts around how do you need to operate as a business and, and be able to, you know, actually see the results.
Yeah, and it's hard. It's hard.
Yeah.
[00:32:07] Speaker B: I feel like a lot of people are going to use Chat, GPT or CLAUDE and are going to consider the business AI powered or anything like that. I mean, you see that so often.
But that's just using AI that's not operationally integrating within your process, making sure that you're actually driving an outcome. I feel like if you're using AI, you're more utilizing it in terms of productivity maybe. And then operationally it's really actual results.
[00:32:33] Speaker A: There's a world where I can use AI to help me write an email.
There's a world where I can use AI to which we have one large client that does be able to run scenarios where I can go and type in a prompt which is tell me what my capacity plan looks like. If I were to increase my demand by 5% across the board, then that then goes and calls the right type of model to run a scenario which might not necessarily still be an agentic model.
And then that's coming out with a solution that is then being spit back into the planner who can then go look at it and actually embed into their operations that way or be able to use it as a planning, I hate the word copilot, but assistant along the way. And that's one of the places I think more and more organizations are seeing value, is being able to think about where people are finding values, being able to use AI just in general as a thought partner.
Hey, I'm thinking about this. Let me refine it. Here's what I think. And you go back and forth with it kind of as a buddy to think through problems.
The analogy of that when you're actually thinking about your plan, how is it catching? How is it looking after you with exception management. Then as a planner does it have your back and make sure that when you're putting a number out there, you're finding the issues with the plan before the number is published, before the organization acts on it.
And so you're not waiting on if you're building forecast. What's my forecast accuracy to know how I did. Can I find those issues up front before I actually go and you know, start doing things with inventory based on that forecast?
[00:34:21] Speaker B: Yeah, I love that. Well, thanks Russ, for joining. There was a lot of things to like unpack here. So I think a lot of people are going to get value out of it. I mean I know I did because there's a couple of things you were saying like crap. I think we're guilty of that. I think we're going to have to adjust a couple of things based on what Russ was saying here. But no, a lot to unpack. What we'll do is we're going to put like all your info in like the description. But as usual, if people want to reach out to you, get in touch with you, what's kind of the the best way to go about doing that here?
[00:34:48] Speaker A: So always open to emails and always open to have a conversation. My my email is Russell R U S S E L L at Insight Kitchen AI and you know, executives are having problems out there. Always happy to have a friendly conversation and the commitment I make and anybody who's worked with me has heard me say this is look, if we can help you, I'm happy to tell you that. But if we can't help you, I'm going to tell you that too.
And often if we can't help you, I can help you find the people that can.
[00:35:20] Speaker B: I love that. Yeah, I have a similar motto. It doesn't make sense when I tell people, oh yeah, we can make promises, we'll help you. And it's like no, we couldn't actually help you. So love that here. But Russ, thank you so much for joining here and true or listeners, if you've enjoyed this episode, like subscribe, comment, all that fun stuff, reach out to Russ here. We're going to put all his link in the description here. If you want to connect, reach out to him here and then, yeah, until then, keep pushing and we'll see you in the next one.
[00:35:45] Speaker A: Thank you.
