Demystifying AI in B2B: Moving from Hype to Action with Nick Pericle, Founder of Tenexity

Transcript

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Jamie Clapper (00:00):
Welcome to Unpacking the Digital Shelf B2B Edition, the podcast for the practitioners navigating complex data, massive catalogs, and the shifting expectations of the modern business buyer. Every month we sit down with the leaders who are moving past the hype and building digital shelves that actually scale. Jamie Clapper here, and welcome to another episode of Unpacking the Digital Shelf B2B Edition. We always hear people say, "Go do AI," but how do you actually bridge the gap between the hype and real business value? Today, Lauren and I are joined by Nick Paricley, founder of Tenexity, AI guru, and an all-around smart guy in the B2B tech space. Nick takes us through his journey on the front lines of digital transformation from his early days testing ChatGPT to running live unscripted AI demos that build complex tools in minutes. We chat about the three sleepless nights of AI adoption, why smaller companies might actually be moving faster, and how to rethink job descriptions for the future.

(01:06):
It's a fantastic conversation. Let's get to unpacking it. All right. Hey, Nick. Welcome to the show. We're excited to have you.

Nick Pericle (01:15):
Thank you, Jamie and Lauren. Great to be here with you all.

Jamie Clapper (01:18):
Awesome. Awesome. Well, we know our listeners are so excited to hear this episode today with you, so we are just going to go ahead and dive right into our questions. But Nick, you've spent roughly over a decade working with distributors and manufacturers, helping them digitize and grow. When you look back at the journey, what was the turning point when you realized AI was here to stay? It wasn't just a floozy, it's going to fly off into the wind, but a true fundamental shift in how distribution operates?

Nick Pericle (01:48):
Jamie, they say hindsight is 2020, and I think anybody who looks back and reflects on their career in an industrial vertical like distribution or manufacturing, you don't always know what's going to come next, but if you put your head down, you work hard, you take care of your customers, and you try and keep up with how the industry is evolving and changing, that things will always work out for your good. And that's really been my story. I'll comment on AI here in a moment because that's obviously the topic du jour of the year, really, and of the coming years. When I first started working with distributors, I really tried to spend time understanding the outcome of any type of project that we were working on and what we were going to solve for. How was this going to change how the business operated, the P&L?

(02:43):
Where was that going to show up? What was the financial incentive? And that has been a theme of any type of technology project. Yes, I do get carried away with how exciting the technology is in and of itself, but it's really how this is going to change the way that the company ultimately makes money at the end of the day for their employees, for their shareholders, and how they make the customer experience better overall. I did a little bit of AI, and we'll talk about this a little bit later. I did a little bit of AI work in predictive analytics just based off of my background. But really where my journey started with AI was in December 2022 when I first opened ChatGPT, and I started asking it questions that I thought only I knew the answer to, or a very select group of people knew the answer to.

Lauren Livak Gilbert (03:35):
What were those questions? Can you tell us?

Nick Pericle (03:38):
So simple things. Looking back, it's simple, but in the moment you have to remember this was significant. So it was things like when I'm designing software to be adopted by sales reps and distribution, what does sales reps and distribution care about? I wasn't asking for the golden ticket of what is change management, but just what do they care about? And when it came back and it told me things like, well, the first thing they care about is they care about how easy these things are to use. We all know that. But then it started talking about the compensation models. Then it started talking about the relationships with their customers. And again, there were blogs that maybe had been written on this, but nothing that went through and gave me 10 different reasons that were so spot on to that particular question. In that moment, I remember very clearly I called at least half a dozen customers that I was working with at the time in distribution and I said, "Have you heard about this ChatGPT thing?" And none of them had.

(04:39):
And it took about a year for people to realize that. I knew pretty clearly back late 2022, very early 23, that this was going to be a lot bigger than people were giving it credit for. And now sitting here mid - 2026, I think we're still underestimating it.

Jamie Clapper (04:59):
I love that. So you're almost like one of the founding fathers of ChatGPT. I think I can give you that name.

Lauren Livak Gilbert (05:06):
Lauren, what do you think? I'm in it. Title.

Nick Pericle (05:08):
I don't know about the founding father, but someone who definitely, I've never been afraid of trying new things. And curiosity is something that has been both a blessing and a curse in my life. And with this one with AI, you really have to be curious and try different things. And I didn't have a problem with

Lauren Livak Gilbert (05:28):
That. And be comfortable with things breaking and are not going to plan and are checking it. And I feel like that's the nature of the world that we live in today. And that's what the commerce and digital shelf leaders in both the B2B and the B2C side of things have to be curious to ask those questions.That's what is required of people in these roles.

Nick Pericle (05:49):
Yeah. I mean, we've always heard the move fast and break things mentality, and that sounds great. When I was in college, I had a unique experience and opportunity to go to Silicon Valley and visit Facebook's headquarters. And I remember taking a picture of that on the wall, move fast and break things. And I go, "That's going to be what I'm going to have. That's my mentality." You get into an enterprise area that's not a tech company, there's no room for breaking things, but you do have to move fast. AI causes us to have to rethink and really challenge our assumptions of what is possible and what the process is to make something work well. So there is a break things mentality in there, but it's not the be reckless and break things.

Lauren Livak Gilbert (06:36):
Yeah, you can't, especially at big companies, so that makes a ton of sense. So curious, Nick, you said that some people are underestimating how much AI is going to change things, but we know it's going to change things. Do you see a gap between the hype that everyone is talking about where AI is changing everything and B2B orgs and they're using this, this, and this between what's actually happening? Is there a discrepancy between those two?

Nick Pericle (07:05):
From my standpoint, absolutely. I have the experience pretty often where I go and I sit down with an organization and I ask them what their aspirations are with AI, what they've been reading about, what they're hearing, what they want to do. And I mean, sky's the limit with what applications people want to drive forward and things like that. When I ask them what they're doing today, usually we kind of revert to the mindset of, well, we're thinking about this, we're planning how to go do this. And if it's a call or if we're in a meeting, I'll say, Hey, can I share my screen? I'll pull my screen up and I'll choose a tool. And usually it's Claude these days. And I'll say, did you know that Claude could do this today? And Jamie and Lauren, I keep waiting for the moment when people say, oh yeah, yeah, we knew that.

(07:57):
We're onto the next thing. Didn't happen in 23, it didn't happen in 24, didn't happen, and it's not happening in 26. People are still, there's a lot of talk around the idea of what AI can do and the gap between that idea and what people can do with a tool like Claude or increasingly ChatGPT, and then there's others that there's a lot more capability that is in people's hands than they're actually using today. And so that's something that I try and help people understand is yes, there's different enterprise ways to go about doing this, but if you know how to use Claude, it's a superpower in 2026. It really is.

Lauren Livak Gilbert (08:42):
But what's standing in their way? Is it fear? Is it legal? Is it data? What is the gap? Is it education? Why do you think that gap exists?

Nick Pericle (08:54):
Each company's going to be different on where they're at. I would say overall, people are very comfortable with how things have always been done. There's a lot of muscle memory in knowledge work that people have, which is funny, muscle memory in knowledge work. But yes, that's a reality. People are comfortable doing things the way they are, and change is uncomfortable. It is uncomfortable to go and be challenged. The people who are deep in AI, who are trying new things, who are challenging their workflows, a common refrain that I hear people share is they're saying, "Man, I'm having to rethink and challenge all of the assumptions that I've built my career on, the way this work gets done. New ways of doing things. The playbook's getting rewritten every six months, if not sooner, with how good the technology is getting and things like that." So I think it's comfort most of all, Lauren.

Jamie Clapper (09:58):
I was going to say my favorite saying is no challenge, no change. I'm going to shout out Robin Arzon, Peloton instructor, because I have taken several rides by her and I will live by that. But I agree, people don't like to feel uncomfortable, and that's where ego and fear and everything else creeps in. And I think Lauren and I have actually done a lot of research this year that says people want to engage in AI and they want to test it out, but then there's this trust issue with AI and what they're using it for. So there's still a lot of gaps, as you will, within the data even because it says we want to do it, but we're scared to do it, but we know it's going to change how we do things now in six months, in two years, in five years. Nick, kind of curious about the gap.

(10:45):
Are you seeing a larger gap within certain industries in the B2B side? Or are there front runners in the space that you think are doing AI better, I guess, or more ahead of the gap than others?

Nick Pericle (11:00):
Who knows, this could be a hot take, Jamie. I know we said we want to be careful about these. I'll say this. So I see organizations across all different sizes from multi-billion dollar organizations, international to, I'm not going to say mom and pop, but companies that are smaller that people are wearing a lot of different hats at. There's different ways that people are approaching it. And I kind of think about this as you can put together a plan of how you're going to go do this and then you can go do it or you can go do it and then figure out the plan as you're going. I think for a lot of enterprise projects, you want to put together a good plan, a good governing framework and things like that. The problem that I see happening with that is the plan becomes the outcome and it's very easy to put off the doing.

(11:49):
And by the time you go about doing, the technology has changed, the capabilities have changed, and all of a sudden you're going back and rewriting the plan. The types of organizations that I see getting ahead, they typically are on the smaller side of organizations. So not necessarily multi-billion dollars. And it's because they don't have all of the overhead of having to think through what's the risk if we get this wrong. They're just going out there and doing things. So they're signing their team up for an LLM tool. They are having non-technical people go out and write code, and they're having some oversight of it, but they're saying, "Hey, go prove the value of this, and then we'll figure out how we scale this and how we make this work." Those are the companies that I see getting the most traction with use cases that are directly adding value today.

(12:46):
It's in the doing of it, not in the talking about how we're going to go do it.

Jamie Clapper (12:52):
I think that's great. It's almost like getting out of, what is it, paralysis by analysis. It's going out and having that plan and being ready to run with it. So that's great. Nick, adding onto that a little bit, I guess you give talks, webinars, you're all over with AI, it's discussion. You're known for doing these live unscripted demonstrations that shows what AI can do. What's a recent moment or use case that really just kind of jaw hit the floor that you'd love to share with our listeners and something that instantly changes how a distributor handles sales, customer service, or even data?

Nick Pericle (13:33):
I always put the acceleration of how people can understand what AI can do. And I think about it this way. You can hear about things for a long time around what it can do and what's possible. You can see it, and that's where people are very hungry to see things. I'll share, and then before I get to the example here, I think the next level of this is actually doing it. There's a difference between seeing it on a conference screen or seeing it on a webinar or even somebody that you're sitting next to in your job seeing what they're doing and you doing it yourself and it answering or solving a real problem for you. I really try and get people to the C, and then I encourage them to go actually take away from whatever we've seen there and actually go do it there.

(14:18):
A few years ago, it was doing analysis, actually having Claude or ChatGPT go and analyze a set of data for a specific data point or an outcome that we were trying to drive against. And that got people really excited. In 2026, there are two things that I see people not understanding at a wide scale what AI can do. The first is AI is incredible at working with very large and complex Excel files. You can take all sorts of data files around product, customer, sales, purchase orders, industry analysis, and you can throw that into Claude or into ChatGPT, give it some background on what you're trying to accomplish. And the tool will go off and work for 15, 20, 30 minutes, and it will come back with a data model. It will come back with analysis of the data, of what is good within the data, what is messy within it, what needs to be changed, and it will try and go solve whatever the analysis problem is that you're looking at.

(15:26):
And that is a multi-step analysis that previously would've taken somebody hours to do, and it's done with increasing accuracy. That is one that people in our industry live in daily is Excel files. I don't know if we always put this on job descriptions that Excel proficiency is required, but it is. And increasingly AI is helping speed that up. So that's number one. And then number two is where people who are using AI are not just using it to do analysis or create things like great content, but actually having the AI go build automations for you. The number one thing that AI is good at is writing code. It's good a lot of things, but writing code and writing really, really good code to go do automations. And when I show people what they can do with building out simple interfaces or multi-step processes that have an interface and have some logic behind it, and they can accomplish that by explaining it, it's really good.

(16:34):
So a real example of this is an organization needed a pallet configurator. And this came up live in a conference that I was at. And live, we gave a mic to somebody and they're describing what's needed. Little did they know I was using WhisperFlow or using a transcription tool to get the requirements live. And it goes and builds this beautiful 3D pallet configurator with the logic in there, with a pricing engine. Took about 15 minutes to do, and people had no idea what to think about that.That was previously a multi-month project. You had to go find a tool that did that. So those are increasingly the areas that people have interest in is how can it help me go build things that help my sales, my customer service, my data, et cetera.

Lauren Livak Gilbert (17:25):
Now, do you think that people in the B2B space are looking at that as, oh my gosh, that was a four-month process that I built my career on. I'm making this up and my job is going away. Do they see it as fear? Do they see it as opportunity? How are you addressing that piece of it?

Nick Pericle (17:41):
Well, I think it's all of the above, Lauren. And there's a great professor from, I believe he's from Wharton, but someone that I read and follow on LinkedIn a lot. His name's Ethan Molick, and he wrote a really good book called Co-Intelligence, I believe it's what it's called. And in that he talks about this element of what people feel when they're working with AI, and he calls it the three sleepless nights. And the sleepless nights go like this. The first is where you get incredibly excited. You're so excited about what can happen here. You start wondering how I did. How does the AI know this? How can it do this? Whatever else. And you just keep prompting it and keep working with it. The second is where you have that existential crisis fit in, and it's fear. Hey, this is something that I do or my company does or that my value is derived from this task.

(18:38):
AI can now do this. What does that mean for me? And I think in B2B, we talk about that at a high level, but it's going to need to be something we talk about quite a bit more around how do we help people through this transition? What does that look like? And I definitely have some thoughts on that. But then the third area is after you work through the fear and you understand it, the ambition opens up. And people who are using AI to get more done are working more than ever because for the first time, the capabilities at your fingertips can match your level of ambition. And so if there's stuff that you've wanted to do that you've been like bottleneck by, whether that was budget or capability, or you didn't know how to write code, or you didn't have time to go research this, the AI can go do all of that for you.

(19:29):
And so you can become really, really ambitious with what you want to accomplish. And I think ultimately that's where we want to get people is let's raise the level of what experience we want to provide to our customers of how well we want this process to work. Let's not settle because while we as humans may not have the stamina and the endurance to do this, those LLMs will go, do work for a really long time. And all we have to do is have the level of ambition to put that to work.

Lauren Livak Gilbert (20:03):
I love that perspective. I mean, I think that makes a ton of sense, and it's a great way to think about it, but you do have to go through those sleepless nights. I really like that to have the moment where you're like, oh my God, what have I been doing for 20 years? But really your job is just going to change and there's obviously still a place for humans in that, and you just need to shift your mindset and go through your sleepless nights. So I love that. Yeah,

Nick Pericle (20:23):
Totally. And I think we equate jobs and we say, "Hey, AI is coming for jobs." If you write that, the LinkedIn algorithm, the social media algorithm's going to pick that up and put that to the top there. I think there will be issues with if your job is a series of tasks and the AI can go do all of those tasks, then there is a reckoning that's going to have to happen there. And so I mentioned this earlier, how to get through that fear. I believe in 2027, but I think it'll actually be 2028, there will be a huge discussion in B2B around how do we go help people rewrite their job descriptions? All the job descriptions that we were hired for, promoted into, our expectations of what was possible, we're going to have to go and rewrite that. And I think specifically what that looks like is yes, rewriting how the work gets done a little bit, but it's more, here's what you're now expected to accomplish.

(21:28):
And it's not working 80 hours around the clock every week. It's we're giving you these tools and these capabilities and the training to go and know how to use them to go do two, three, four, 10 times what you were doing before and not having to increase the amount of time you're spending in correlation with that. So I think that will be a huge discussion that comes with how good these AI tools are getting. And it's not something I see being talked about within distribution. I think it's being talked about within tech, but within distribution, manufacturing, B2B, there will be more of that.

Lauren Livak Gilbert (22:07):
Yeah, I encourage people now to rewrite their job descriptions. Think about what you want to be in the future with these tools and how you can do more. I agree, we've never been able to do the things that we've wanted to do, so how can you do that? But then also, I guess the challenge I have, Nick, is how do you get that back into the business? So let's say you rewrite your job description, you do that amazing pallet example that you were talking about, and it was this really cool pilot and someone was like, "Hey, this is cool." And then all of a sudden it doesn't get incorporated into the business and you have seven of these and everyone's like, "This AI is doing great stuff, but it's not actually moving the needle. It's not being incorporated into the business." How do you get out of that pilot purgatory piece on the B2B side so that you're actually, to your point, re-imagining the

Nick Pericle (22:56):
Work? Yeah. Within specifically distribution, but I'll talk both distribution and manufacturing. We talk about priorities within the business. It really hit me a few years ago. I read, I don't know if it was a post on LinkedIn, increasingly LinkedIn and Instagram and social media. Those are my books. I don't always say, "I read this in a book, but I read it in a summary from someone who read it in a book." And just the idea of multiple priorities is actually an opposite in opposition to a priority is a single thing that gets done. In businesses, the most important thing always is the thing that gets done. And when I hear pilot purgatory or issues there, what that means to me is that whatever neat or cool or even valuable thing that got evaluated and built out, it wasn't valuable enough to actually go to the top of the list for the hard work to happen in order for this to be pushed through.

(23:57):
I think that was the case as people were experimenting with what AI can do, with what it's capable of and things like that. More and more, I see companies that they don't need to be convinced that there's value in AI. They don't even necessarily need to run pilots. They know it's there. And so when that is the case, what I encourage organizations to have is to look at their software deployment pipeline of, okay, you have a technology tool. Wherever that comes from, whether it's from the market, whether it's from someone internally, how does that get into production today? Who are the stakeholders? What are the decision points? Let's be very clear on what needs to be discussed. Very few companies have that. It's more ad hoc as things come up. And just with the amount of opportunities there are at building tools, deploying them, there's a bandwidth constraint.

(24:56):
You cannot look at all of these different things that are there. You're going to go to the things that are the most important, which means the things that still are important just aren't the most get put on the sideline. And more and more, that's where I see pilot purgatory happening. And I think with the software deployment process that you have there, it also needs to include who's going to continue to champion this and drive this throughout the business. And most organizations don't have a strong change management function. I was on a call with someone recently and they said, "Hey, whatever my ERP..." The best ERP I ever used was the ERP I used at my last company. Regardless, it doesn't matter. Even if I was complaining about it two weeks ago, now I'm in a new job, that ERP was way better than what we have here.

(25:49):
And so you do need to think through how do you get people to really like what you have and want to use it?

Jamie Clapper (25:58):
Yeah, I agree. I think the concept of pilot purgatory is kind of an interesting one because it's like you take the leap, you dip your toe into it, you're into it, and then things change. So it's constantly navigating that evolution. And to your point, having someone to be able to guide that and drive that. And I think that could be an area that a lot of organizations, at least ones I've talked to in the past, they do lack that. There is this desire, Lauren and I always say, "Go do AI." It's what we always hear, but then there isn't a guide around it, if you will. So any kind of advice for a company that is getting into AI, starting to lay the groundwork, how to navigate the flow and the changes of some of these AI tools they're using.

Nick Pericle (26:48):
I always like to look at an organization's AI policy to first get an understanding of what is their both risk appetite and innovation index. How much do they want to push forward in that way? And usually you'll find that an AI policy is really a risk mitigation. It's, "We use this tool, don't use these tools." Or it's, "Don't put this data into it." A good AI policy and having a good AI policy has elements of that, risk mitigation and security, but it also has, "Here are ways that we encourage the use of it. Here are people within the business that we've identified who are champions of this, who like this, that you can reach out to and you can talk to." And it provides direction for people wherever they're at on the spectrum, whether they're just getting introduced to a tool like ChatGPT, Cloud, or even Microsoft Copilot, or whether they're, going back to what we talked about earlier, building some of these tools and automations themselves.

(27:56):
Giving that sort of direction on, okay, this is where you're at. Here's what you want to do. Here are the resources that we as a company have invested in, are providing. Here's who you can talk to so that you have someone to support you in overcoming that inertia of, "I've always done it this way." That's what I've seen be within a good policy is that type of direction. And then talking about it regularly throughout the organization, whether that's at town halls, whether that's within each individual function. So the CFO, the CIO, the chief supply chain officer, the head of sales, whatever that looks like, them talking about it and driving it down versus it being a one-size-fits-all approach.

Jamie Clapper (28:47):
Yeah, no, I think that makes sense. And I think that's great advice for our listeners out there, especially as I think there's an assumption. AI, it lives in digital, it lives in the commerce side of things. And there are probably some organizations that still aren't thinking about all the different departments that can be impacted by AI and what's happening in that space. So wildcard question, Nick, get out your crystal ball because I'm sure, as many people know, AI brings a lot of anxiety among executives getting disrupted by AI. What's going to happen? I think my dad will say, "I think it's a bust and it's not going to go anywhere." I've actually had that conversation with him. This is also coming from someone who's never used ChatGPT or any type of AI activity. But where do you see the biggest long-term opportunity for our traditional distributors who embrace this now versus those who wait on the sidelines?

Nick Pericle (29:44):
I'd answer that in two ways, and this is more top of mind for me. So maybe Jim, I'll first talk about a big risk that I see and then the opportunity on that side. It's not my area of expertise, but I see this growing In frequency and just how often it's talked about throughout the industry. And it's the investment that organizations are making in their own cybersecurity plans. We see often things like hacks or ransomware, and you can go online and search any type of industrial company that's gone through this. It's paralyzing to an industry or to an organization. I think there's going to be a desire to invest in that functionality more heavily in order to protect the value that organizations have created there. So I see that as both a risk and an opportunity to mitigate that. The next thing that I see on the opportunity side, I could talk through the automation of certain types of functions that you have.

(30:53):
Order entry is the best way to get started with AI and distribution.

(31:01):
The problem is basically solved. There's so many great vendors and organizations out there doing this. It's been well thought through, so the technology works there, but there's others as well. You have AP, you have AR, you have product content, and just the ability to get your product content right. I think the biggest opportunity throughout all of this with AI is for organizations to create a unified data layer across all elements of data in their business, their ERP, their CRM, their phone systems, the conversations that are even happening at the branch counter. I think in a year and a half from now, end of 2027, that we will be installing, not my company, it was not something we do, but as an industry, we'll be installing microphones at counters to capture that context that's being shared there and to automate order entry, quoting, price look up, et cetera.

(31:58):
That's data that is being overlooked. So you go down the list of all these different data sources, putting that into a way that AI has access to, and then having AI go do things with that, whether it's analysis, whether it's creating agents that can go follow up on things and do work for you, or whether it's taking the overview of everything that you're doing as a business and saying, "All right, we now need to build a better process to go analyze this full end-to-end function." That to me is the biggest long-term opportunity. And so anything that you can do in terms of getting data clean right now, organizing it, and having that done, not as a single standalone project, but thinking about it as, okay, yes, there's ROI on this single standalone project now, but it's also helping us in our journey to say every single system is now connected within our business.

(32:58):
The technology companies that have done this, they're able to move at a speed that traditional incumbents can't. You just can't. You can't mobilize fast enough. You can't do analysis fast enough. So that is long-term. I think every organization is going to come to that conclusion on their own, and then they need to determine the timeframe in which they want to go about pulling that together.

Jamie Clapper (33:26):
Isn't it wild that it all comes back to product data? At the end of the day, it's always about the data. It's data and content, king and queen right there running the show. Yeah, not sexy,

Lauren Livak Gilbert (33:37):
But it is the main piece.

Jamie Clapper (33:40):
It is not sexy. It's not fun, but it's there for a reason. And the way it's just continued to be weaved in and out of the latest and greatest technologies, it just shows you how important it is.

Nick Pericle (33:53):
Yeah. And there's data in. Another thing that AI is going to do, there's data in areas that we haven't looked at yet. In, again, conversations and voices and how things are shared and things like that. But there's also data in documents that live on SharePoint. There's also data in the thought process that employees and people go through to solve problems. All of that has not been captured up to this point, and I think increasingly it will be. So that's really neat.

Jamie Clapper (34:22):
Well, it's like what they say. There's always so much knowledge in someone's head and it's how do you pull it out of an employee or someone who's been there for several years that knows the product in and out? How do you pull that data out and utilize something like that? So I love the idea of microphones a checkout. I'm sure not everyone will love that idea, but it is a cool concept and it's definitely something that continues that growth, if you will, of how we're capturing data and ways to utilize it.

Nick Pericle (34:51):
Yeah. And I think the microphone thing might be a little polarizing for people. I do think we will get there though, that there's so much valuable information. And the benefit is there to the people who are participating in that. If you can get your order way faster, if there's a discount that you'll get just by having that, there's a lot of benefit. But I would say start with your phone calls. Start with your customer service and your inside sales phone calls. The amount of organizations that aren't capturing that and recording it and are afraid of, "Hey, well, what happens?" You put that disclaimer on the beginning, "This call may be recorded for quality and training purposes." That is totally socially acceptable now. And what you can do in mapping out what your customers are asking for, where there are frustrations, where there are customers that are asking for goods and services that you could provide, you're just not providing them because you don't know that they're being asked for.

(35:48):
All those things are really beneficial.

Jamie Clapper (35:52):
Absolutely. I could not agree more with that. Well, Nick, I'm sure our listeners are so excited with all the information that you've given them starting off point. Obviously, Nick is all over LinkedIn, y'all, so give him a follow, check him out. He is truly a genius in the AI space. He is showing people how to get it done, and we are so grateful that you joined us here on the B2B podcast, so thank you for the time today.

Nick Pericle (36:17):
Thank you, Jamie. Thank you, Lauren. And I'll share, my passion really comes from, and over the last decade, working with organizations in distribution and in this industry, I recognize that really the economy is built on this industry, and it's been very overlooked for a long time. And so I love that the spotlight is increasingly here, that there are so many new people entering the space, interest in how to go and apply this AI to it. I spend night and day thinking about what is the transformation this industry is going to go through and how do we get it done in the right way? So I really appreciate the conversation, both of you.

Lauren Livak Gilbert (37:01):
Thanks for sharing your insight, Nick. Really appreciate it.

Jamie Clapper (37:04):
Thank you. Thanks for tuning in to Unpacking the Digital Shelf B2B Edition. To keep learning from the best in B2B, subscribe now and join our community of leaders by becoming a member of the Digital Shelf Institute. Thanks for listening, and we'll see you next time.