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On this episode of The Chemical Show, Victoria Meyer talks with Kendall Justiniano, Managing Director of Growth Arc Advisors, about the state of AI in the chemical industry. Kendall argues that the “exploratory phase” is over, and the focus must now shift to achieving real business value. They discuss why many companies remain stuck in experimentation mode and what it takes to move beyond isolated pilots to deliver ROI.
The conversation covers key ingredients for effective AI use: governance, connected systems, and building internal expertise. Victoria and Kendall explore practical barriers like security and data silos, outline where ROI is taking shape in commercial and document-heavy processes, and examine how companies can build context-rich systems for long-term competitive advantage.
Topics covered this week:
- AI Maturity Curve: Why has the exploratory phase ended?
- Adoption vs. ROI: Where chemical companies are seeing returns—and where they aren’t
- Generative AI beyond chat prompts: Building connections with data systems
- The Governance Imperative: Data access, security, and responsible experimentation
- Commercial Applications: Sales, marketing, and procurement as major opportunity areas
- Context Layers: Embedding company know-how into AI workflows for differentiation
- Measuring and validating AI performance as systems evolve
Killer Quote: "We’re the ones doing the cross-system scanning—and that’s where your AI is going to live. It’s going to live right next to us." — Kendall Justiniano
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Creators and Guests
What is The Chemical Show: Where Leaders Talk Business?
Looking to lead, grow, and stay ahead in the trillion-dollar global chemical industry? The Chemical Show - the #1 business podcast for the chemical industry - is your go-to resource for leadership insights, business strategies, and real-world lessons from the executives shaping the future of chemicals. Grow your knowledge, your network, and your impact.
Each week, you'll hear from executives from across the industry - from Fortune 50 to midsize to startups. You’ll hear how they're tacking today's challenges and opportunities, their origin story (what got them here!), how you can take and apply these lessons and insights to your own business and career.
We talk:
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Founder and host Victoria King Meyer is an expert interviewer - who brings out the best in each guest. She gained her industry experience at leading companies, including Shell, LyondellBasell and Clariant. Today, she is a high-performance coach and advisor to business leaders in chemicals and energy, as well as the host of The Chemical Show podcast, and founder of The Chemical Summit.
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Victoria Meyer:
Welcome to The Chemical Show, the podcast where chemical means business. I'm your host Victoria Meyer, bringing you stories and insights from leaders driving innovation and growth across the chemical industry. Each week we explore key trends, real world challenges and the strategies that make an impact. Let's get started. Welcome back to The Chemical Show, where leaders talk business. Today I am speaking with Kendall Justiniano who is the managing director of Growth Arc Advisors. You guys have heard Kendall previously on The Chemical Show. I'll link to that episode in the show notes.
Victoria Meyer:
And Kendall recently published an article that came to my desk which is around AI and that the exploratory phase is over. And I have to say, is it? And if we're not still exploring, then what are we doing? So that's what we're going to be talking about today is just how companies are using AI and where we need to go with AI. So Kendall, welcome back to The Chemical Show.
Kendall Justiniano:
Glad to be here, Victoria, excited to talk about this topic.
Victoria Meyer:
It's one, absolutely. So first of all, let's just talk a little bit. Give us a brief intro to you for the folks that don't know who you are. I guess we could do it as your origin story or, or your origin story since we last spoke. You choose.
Kendall Justiniano:
Yeah, for sure. Why don't. I mean the basics are I'm a 30 year veteran of the chemical industry. For the last six years I've been on my own running Growth Arc Advisors, which is six years already kind of by the chemical leaders. It's been six years, Isn't that crazy?
Victoria Meyer:
That's good.
Kendall Justiniano:
We advise executives in the chemical industry on strategy, commercial effectiveness and innovation. And gosh, since we last spoke, I've gone deep into AI. My firm's been using AI for the last couple of years. We started out slow, as probably everybody did, but last year we were actively using AI for a significant amount of analysis to help our consultants. And then we ran, just finished up our first engagement that was entirely AI enabled. And about six months ago I went deep into. I'm not sure if agentic AI is the right term for it, but it's a term everyone needs and sort of the, the big data end of AI and I have to say, rocked my world. And so the firm is actively embedding AI in all of its, its current work processes.
Kendall Justiniano:
We're about 2/3 of the way through that process. But when we're done, the vision is sales and marketing, business development. All of our back office functions will be AI enabled in Some way in addition to using AI to deliver client engagement.
Victoria Meyer:
Okay, so that is awesome and scary all at the same time. So let's maybe just talk about where we are. So before we hit the record, but we were talking about and maybe I'm just going to go back to this title that you had, kind of a catchy title on your article, that the exploratory phase is over for AI. And yet when I look at this, and I just talked about this recently when I, I talked about the five fundamental shifts happening across the chemical industry, one of which was, you know, five years ago we didn't even know what AI was. People didn't know how to spell AI. And now it seems to be embedded, although it's not really, because I think I've seen data that says something like less than 13% of an organization or people inside of organizations are actually using AI. So it seems like there's been this mandate to go deep into AI or to explore AI and I'm not sure that people have fully figured it out. So.
Victoria Meyer:
So what are you seeing on as it relates to chemicals and materials companies and how they're using AI today versus where they've been?
Kendall Justiniano:
Yeah, and I think your observation is, is right on Victoria. If 2005 was the year of adoption, it's the every company brought AI on board in some aspect. When I talk to clients, it's pretty clear that a lot of the people are using AI. But as you, as you mentioned, a lot of what the data shows is adoption is not the same thing as return on investment. And so while there is a lot of excitement, enthusiasm and folks have done the process of sort of adopting AI a lot of times when I talk to clients, it's unclear sort of where they're going to head or where the projects are are that are going to return the kind of in return on investment that they need to see. So this will be the challenge in this year, will be the challenge of figuring that out.
Victoria Meyer:
Absolutely. And I think part of this is that we talk about augmented AI augmenting business and other things. And I don't know how you measure augmentation other than headcount. So I don't think we have great metrics. Let's talk a little bit. You started telling me that you decided to go deep into Claude Code and now by the way, I have, I've dabbled in Claude. I use a lot of ChatGPT. I've contemplated switching over to Claude.
Victoria Meyer:
We'll see to be to be determined. But you've dove Deep into Claude. And a number of companies that I've spoken with recently in the past couple of months have basically said they've turned off Copilot and they've turned on Claude. So, Claude, what's your experience with this?
Kendall Justiniano:
So it was a. That was a transformational moment when, when I dove into the. To the other end of the pool. And frankly, it was scary when I did it because, you know, I'm running Claude Code at the terminal line, and I'm not a coder. But in the process of exploring that and understanding and then beginning to work with it, you realize a couple of things, and I think this is part of what is maybe companies are getting stuck on, which is most companies have taken an approach, and a lot of the folks I talked to have taken the same type of approach where they've. They've brought Copilot in, they've opened up their entire document archive, you know, obviously firewalled, so that the entire document archive is searchable. And they've, you know, extolled their. Their organizations to, hey, let's use it.
Kendall Justiniano:
Let's see where we can. We can make advances with it. And for the most part, most people stay at the chat prompt, and there is an aspect of, hey, there's a grassroots approach to AI adoption. But as long as you stay at the chat prompt, you aren't actually doing anything at scale. And so my hypothesis is, as long as you're at the chat prompt, you're not discovering, you're not going to discover through a grassroots mechanism where the interesting opportunities are for AI. And so what that means is you've got to go deeper than what you've opened up so far in this first
Victoria Meyer:
adoption phase, two things. Number one, when any project that's unbounded, and if we can think about releasing AI onto an organization in an unbounded fashion, it can be really hard to make progress. Right. So you get nuggets and pockets of greatness and a lot of just chat prompts, as you say. So I think that's actually an interesting insight right there. Just that this whole fact of it's hard to manage a project and it's hard to measure ROI when you have no boundary conditions.
Kendall Justiniano:
Yes. The challenge is that I think organizations need more fluency in AI and I think they need more. More access to, I'll say, deeper tools in that sense. Right. If you look at, you know, our progression with AI started at chat. It starts at chat with just about everybody, and then you move from chat to what I'll call the Sort of cowork space, or if you, if anybody uses Notebook LM or cowork, you now turn AI toward a folder, right? And so now you can provide AI with 10 documents, 20 documents, 50 documents, 100 documents. Right. And have it do analysis.
Kendall Justiniano:
So you're increasing the amount of data that the AI is able to look at when you turn on, when you move into the Claude Code phase. And really it's a misnomer because the issue is not about code. The real, the real, the real unlock when you get into Claude Code is you start hooking up AI to large data warehouses through what they call connectors. Okay? So I hook up my AI to my CRM, I hook up my AI to my erp, and now I have access to data at scale. The other aspect is you have access to data at scale across all of your systems, and that's where the power lies. So when you start giving people access like that, or when you start experimenting with that type of access, now you're working at scale, now you're working with the entire breadth of the data that you have available to you across multiple systems. And now you can start to think about some really fascinating kinds of augmentation. That's a challenge, but that's really the continuum and the transition is you're moving into larger amounts of data.
Kendall Justiniano:
And so now you're going at scale. Right. And now you have access to the full resource set available to you.
Victoria Meyer:
The challenge, of course, as you talk about this, I think about corporate governance. Kendall and you and I both worked in major corporations for large amounts of our career. We're running our own businesses which also have a version of corporate governance, albeit at a smaller scale. But I think about the governance aspect of when you start connecting these systems, people didn't always have access to all of this stuff. And I think that's a concern, right? That's a risk that companies I think are probably still wrestling with to a degree.
Kendall Justiniano:
Yeah, I mean, the concept of saying, well, let's open it up and let's get anybody who wants to sort of operate at that level. Access into systems becomes potentially really problematic. And so I think executives have to think about how they start to gain access or how they start to work with access to those systems. And it's a little bit of you've got to have experts in the processes, the work processes that you use, all the sources of data that you use who understand what happens in day to day activity, but you've also got to have folks who can design, architecture systems Built on AI. That's where the grassroots part of it falls a little bit apart, which is you do need that context, you do need that understanding of what's happening day to day in the trenches. Right? That's the grassroots piece of it. But you're not going to get that from, from one person playing with chat. You're not going to see the kinds of things show up that are going to be at scale, incredibly beneficial.
Kendall Justiniano:
And so you've got to put in as well AI expertise. Right. And you've got to bring the level of understanding of folks up a little bit to, to start to say, okay, what are we really looking for here? What kinds of activities are we looking to displace and what kinds of pilots and experiments can we run in that sort of covered governance window?
Victoria Meyer:
Where does that AI expertise come from? From what you've seen? Is it there? I mean, so again, I know you and I both know some startups that are promising AI that are bringing AI power in a certain space, right?
Kendall Justiniano:
Yeah.
Victoria Meyer:
Is that where the expertise is coming from? Is that what you see as being effective right now?
Kendall Justiniano:
So it could be, and that may be the future is you have vendors who provide you sort of AI layers. The challenge is that the power is close to your organization. You're having AI do things that humans might have done before or that would be human tasks but were too complex or too time consuming for humans to do. I tell you where it's not, where it doesn't come from is it's not going to come from a single system who's turned on an AI agent inside of it. Right. Salesforce has AI inside of it. ERP systems are starting to have AI inside of it. Those systems don't reach across all of your systems.
Kendall Justiniano:
Those systems don't live in the trenches in the workflow of your human beings. Yes, they have them, those tools are embedded, but that's not really where you're looking for. What I've seen start to happen, at least internal in companies is you get some folks who develop some expertise and some understanding and they are the ones who start to do the really interesting things. Right? It's not the person who does the chat and sort of makes one task efficient, it's the person who's sitting there going, well if I put web search and get my customers TDS or my customers MSDS sheets and then I can go to my selection guides and apply the selection guides to their MSD sheet and then I can go into the customer and I already have pre selected products for Instance, right. This sort of daisy chaining of I can do this and then I can do this and then I can do that and then I can do that. Those are the kinds of folks that you want to try to find who are, who are doing that level of experimentation because those are the places you're going to start to surface. Oh wow. You know, we could take five steps out of this workflow and really increase the quality of what we bring to our customers.
Victoria Meyer:
So the other thing I heard from you, Kendall, I'm now making notes because for those of you guys that are listening, this is a bit of a fluid conversation. Kendall and I came into it slightly unprepared. Although Kendall, I gotta say is always prepared. So he is probably way more prepared on this than I am. But I think about kind of critical success factors that we've talked about so far. One is governance, right? And having kind of a clear governance system to enable the right access and the right objectives, etc. So that's one aspect of it. The second piece is this connected systems and data that I heard you say because.
Victoria Meyer:
And maybe this is this whole connected enterprise and I keep hearing about it and it's kind of a little scary to me sometimes or a little bit. What the heck does that mean? But I think what you're saying is if we're not connecting the data and the data across the systems, we are sub optimizing our business enterprise.
Kendall Justiniano:
The way I think of it is the AI is going to live close to your organization. It's not embedded in a system. It's going to live next to your organization. And if it's doing work that people was too time consuming, but it's still human work. Guess what? We do as human beings in organizations like these, we're the ones who go to this system and get this thing, go to that system and get that thing, go to that system and get that thing. Then customize it for another conversation with a customer or something to that effect. We are the ones doing the cross system scanning and the cross system pulling. And that's where your AI is going to live.
Kendall Justiniano:
It's going to live right next to us. And so it's this aspect of it's not going to be a software provider. The expertise has got to get into your organization and then it's got to actually be working with your human systems to figure out what it is that you could design that AI would, would operate against.
Victoria Meyer:
I have a vision in my mind of like an octopus shaped interface layer that somehow is just, it's there it's just a layer on top of everything. And that's what AI becomes.
Kendall Justiniano:
Yeah, it has to be designed, it has to be built. And it's not magic in the sense of, you know, we all know in chat, you've got to manage the context that you give the AI or you get bad results right at scale, you're not prompting the AI, you're providing the AI with information and a set of operating instructions. And all of that context has to be well architected in order for it to operate well. So it's not as though sort of everybody's just going to be building an agentic system. But the key is that your organizational processes, your organizational context is going to get embedded in these systems. And so it's not something you're going to have a vendor do at arm's length. It's not something where somebody's going to be a software provider and provide you a platform that somehow has all that context built into it. There'll certainly be layers of tools that are provided, but ultimately the work's got to be done inside the organization.
Kendall Justiniano:
And this is what, this is what the McKinsey's of the world have figured out. And if you, if you look at all the model providers, they've all spun up consulting operational AI expertise because they, they have now figured out that, hey, the embed of that expertise into organizations is where the value capture is, it's where the return on investment is. And so they're spinning up to be able to help companies do that as well. Right?
Victoria Meyer:
Yeah. So you mentioned that you've just concluded a project where you basically were AI, was one of the partners on the project. How did that work for you? First of all, how did that work for you? And how did your, how did your clients respond? Because again, you know, in fact you, if you read the news, you see pushback on. Oh well, oh my gosh. One of the latest things I've seen that was a, has been drama in 2026 is Booz. Booz Allen. Is that what they're calling them these days? Huge government contractor doing consulting work and they've applied AI and now they're like, well, you can't charge as much for that. So it's around you and how dare you bring the AI in and what did you do? So I guess the question I have is, as you come in and work with companies, are they embracing that? You're embracing AI?
Kendall Justiniano:
So they certainly value the quality of the work that we can bring as a result of using AI. So we Make a point of letting them know, hey, for this spend, the quality was significantly higher than what we would have normally been able to do. Not being AI enabled, we're fully transparent with clients before we start that we use AI, we're firewalled internally as well. Obviously you get into governance issues the minute you get into relationships like that. But I think, number one, it's been successful. Number two, what happens is we're bringing our best in class expertise on the particular topics that we consult on, and we're now starting to embed that expertise in the way our AI functions for people. Right. And so these layers that are inside your organization that have the contextualization of how you operate built into them, become sources of competitive advantage.
Kendall Justiniano:
Because if you're able to make recommendations to customers, for instance, on products and where you would use them and how you do that, and you do that in a particular way and it gets you advantage in the market as a result. That's part of what's going to sit in this context layer. These context layers are going to become confidential context layers. Right? There's going to be governance and protection around not letting those context layers disappear outside of the company. But if you automate on those, then you're building your best in class capabilities as part of the, as part of the system and it becomes a source of competitive advantage. Right? It's a source of competitive advantage for us for sure, on that basis because we can do things that other folks can't necessarily do. Right. I lived in that world two years ago where everything was done sort of by humans.
Kendall Justiniano:
I don't see us ever going back, but I think consulting's not dead. AI is going to be here with everybody. And so that's the challenge, I think in this next phase of AI adoption is figuring out where the benefits are and then figuring out how to build your organization to extract.
Victoria Meyer:
So where do you see? So I think that's a great segue because we talked about 2025 being the year of exploration and 2026 being the year of ROI. Where's the ROI for chemicals and materials companies? Do we know yet?
Kendall Justiniano:
Yeah. So let's talk. There's different kinds of AI, you know, there's machine learning as well as language models. So let's talk a little bit about language models. And this is, this is one of those aspects that the blog article you mentioned, which is one of a series where I'm actually doing a whole series on AI and different facets of it. You're either going to improve the quality of Something that's done with AI, the effectiveness, you're going to speed up how quickly you get to end state with AI or you're going to become more efficient. So that's the first thing to kind of think of is where am I doing something that's doing one of those things. Okay.
Kendall Justiniano:
When I think of language models, I also think of where am I doing all sorts of human mediated language transformation or file access. That's unstructured. Okay. So this is not an ERP system. And the kinds of things that come to mind, Procurement, right. A ton of documents, contracts, information, orders, invoices, transacts through email. And all of those are unstructured documents in a sense. I gotta be honest, my really interesting
Victoria Meyer:
opportunity space was designed to be unstructured. What a beast.
Kendall Justiniano:
Yeah. And so, and so the LLMs, this is their, this is their sweet spot. They excel at taking unstructured data and turning it into structured data. The other place that I have I am really enthusiastic about, but it's a tricky one because nobody thinks about it that way, is actually the commercial function. I'll give you an example within our own. So if you think, stick with me, if you think about this, right? In commercial, what am I doing but translating one document type into another document type, right. I take a template deck from marketing, I turn it into a specific deck for my customer. I take that value proposition and turn it into a social media post or I turn it into a blog post.
Kendall Justiniano:
It's the same content being transformed over and over and over again to fit in a given context. Right. Huge opportunity for AI. So those are the types of things. You know, I've had some, I've seen some cases where folks are trying to do market strategy with AI, right? And that one doesn't quite fit my, my filter because you're not doing it repetitively. If you're going to take a little bit out of every, every task that gets done over and over again, that's, you know, market strategy is something you do once a year for five segments. So there you got five iterations a year. Yes, it's a long process.
Kendall Justiniano:
But how are you going to validate that? How are you going to say, okay, the AI has produced some, has produced a good strategy. We know that this strategy will work, therefore now we can let it run free on our market strategy. Right. There's no good validation path for that. But if you think about the activity that goes on in sales, that goes on in marketing, sort of in the trenches, I think is huge, huge potential, right? We're doing that kind of implementation in our back office right now. My entire marketing stack is almost completely automated now. Yeah, I can go from concept to deployment almost instantaneously. So for instance, I'll do a roundtable, right? I do roundtable series for executives.
Kendall Justiniano:
That entire, you know, pre production, post production, production process is almost entirely automated now. So I literally start with a conversation with, with a potential guest. We sit and talk for about an hour, and that conversation transcript turns into a concept which is then immediately proliferated into all the marketing materials that are necessary.
Victoria Meyer:
So curious on that, because I do, I mean, I frankly, I do something similar with the podcast with The Chemical Show. Right. Everything gets AI transcribed and we create blog post, emails, other stuff. We still have a human qc and taking it from Castmagic, which I've been using for years now, and then taking it into scheduling tools. So I do know some people that automate that. I'm personally a fan of a human intervention. Like, okay, let's do a logic check.
Kendall Justiniano:
So the potential. So you can think of your deployment tools as just another pipe to tied into another system. Right. Which I can connect up to the AI. And you're right in the sense that you want a quality check. Right. But think about that workflow. If I generated the concept, all I have to do is have a conversation with a client.
Kendall Justiniano:
The entire concept is generated and everything is put together. The quality check is almost trivial. Yes, you can check it and yes, you can say, go back and do this a different way. And then you can say, okay, deploy. Stick the data in the pipe and go all the way to deployment. It's not that you're fully automated in the sense that, oh, I just let the thing run. But you're transforming your workflow. You're saying, if I know these things, what can AI do for me if I give it the proper context and deploy it in the proper sequence? And the answer is an awful lot.
Kendall Justiniano:
Right? A lot of what? A lot of what people get scared about in terms of hallucination is an artifact of chat. It's when you don't actually provide enough context with your prompt. And the AI does its best to fill in the blanks and it fills them in wrong. And then you go, oh, gosh. And we don't, we don't trust the AI. But if you've run those systems at scale with appropriate contexting and appropriate protection of that context, like hallucination isn't the kind of issue that it is or that people see and fear when they're sitting at that chat prompt. That's one of the things of getting out of chat, is you've got to now think in the systems.
Victoria Meyer:
Right. I do think. You're right. It's an archive. It was a concept and it was an issue two years ago when we were new and we didn't know how to use these large language models. But back to that, I'm coming with your three, which is back to if you're using governance, connected systems and contextual layers and have enough depth built into that, your outputs are going to be strong, and that's where you create value.
Kendall Justiniano:
Absolutely. Yeah.
Victoria Meyer:
All right.
Kendall Justiniano:
Now those have to be. Those systems have to be tested and validated. Right. That's the other piece of it. The architecture is well built, and then it's validated. So when somebody comes to me and for instance, and says, hey, I've automated something with AI, the first two questions are for me, okay, what's the methodology you built in? How did you build the architecture and then how did you validate it? And if the answer is, I didn't really do that, then you've got real strong suspicions to say, well, I'm not sure this is the system's trustworthy in that sense.
Victoria Meyer:
Well, and I think the other piece, Kendall, to me is, as I think about this, is we can do our best at a point in time. So we're sitting here June of 2,026. You've been vibe coding with Claude. I do a lot of work with ChatGPT. Others are using whatever systems they're using. And this is all really valid for the tools and systems that know how that we have right now. And when we get to June of 2027, we need to recheck to make sure we're using the best of it. I mean, because I say this like, you know, I talked about Castmagic, which is a AI tool that.
Victoria Meyer:
That we've been using for years for podcast content, helping to transcribe podcasts, turn it into something, give us some insights. And I had this, like, epiphany about a year ago. I was like, we need to look at this again. Like, we started using it. We were like, we were one of the very early users of this tool. I'm like, I know that it's evolved, but our work processes haven't evolved to keep up with whatever the evolution of this tool is. So you have to go back in and make sure it's still valid. And it's not that it's not valid, and it's not that it's wrong.
Victoria Meyer:
It's more that you're not getting the power of it based on the continued improvements, based on the better systems and structures and contexts and all of those things.
Kendall Justiniano:
Yeah. So I think it's a really interesting insight. There is this issue of is your context layer designed with a particular system in mind? Does it accidentally have part of the system implied in its architecture? The thing that's come out in the last few months since Claude Cowork came out, there've been a lot of folks who have been sort of, how do you design these structures? How can you do it in such a way that people who are not code experts or architectural experts do? It can do it in such a way that it's actually independent of an agent. Right. Where you assure essentially that, okay, the context, I'm providing the context layer entirely and I'm separating that from any of the functionality of the agent. And there have been some new developments and sort of how people are thinking about this just in the past couple of months since Claude Cowork came out, where the system I'm building, for instance, is model independent. In other words, the context is provided and it's provided independent of the operating model. So literally I can swap out Claude Cowork and go grab another LLM and say, read my specification and it will know how to operate within the environment that I've provided that specification for that.
Kendall Justiniano:
Again, it's an architecture, it's a design issue. The challenge with all of these, when you say context lures built within the company and customized to the company is now who maintains that? Right. Who's, who's helping you think about that architecture? Who's helping you keep that architecture up to date if it needs to be kept up to date? If it breaks, what happens when it breaks? Right. That's the other aspect of this. But those are the places where AI is showing itself to be able to do return on investment. Right. And so, and so you know, from an executive standpoint, you've gotta, you've gotta go there, you've got to start there because your chat experiments and pilots aren't going to generate for you the kinds of concepts that are really going to do the return on investment for you.
Victoria Meyer:
Awesome.
Kendall Justiniano:
And it's fraught with complexity, but that's where you got to be.
Victoria Meyer:
Awesome. Well, this has been really good, Kendall. I appreciate your time. If people want to get more insight from you and connect with you, what's the best way?
Kendall Justiniano:
So I'm on LinkedIn. Still on LinkedIn. We talked about that earlier today. So Kendall Justiniano I post primarily on my personal profile. Company profile gets a little bit and then my website where the newsletter that you mentioned is is hosted is www.growth-arc.com. for anybody that's interested in this topic, I've got another series of maybe six more articles that are coming out on different facets of how executives need to think about AI and what this year is going to be about.
Victoria Meyer:
I love it. Kendall, thanks for joining me today on The Chemical Show.
Kendall Justiniano:
Thanks so much Victoria. It was a lot of fun and
Victoria Meyer:
thank you everyone for listening. Keep listening, keep following, keep sharing and we'll talk with you again soon. Thanks for joining us today on The Chemical Show. If you enjoyed this episode, be sure to subscribe, leave a review and most importantly, share it with your friends and colleagues. For more insights, visit thechemicalshow.com and connect with us on LinkedIn. You can find me at Victoria King Meyer on LinkedIn and you can also find us at the Chemical show podcast. Join us next time for more conversations and strategies shaping the future of the industry. We'll see you soon.