WEBVTT NOTE This file was generated by Descript 00:00:01.170 --> 00:00:05.560 Victoria: Welcome to The Chemical Show, the podcast where chemical means business. 00:00:05.930 --> 00:00:09.710 I'm your host, Victoria Meyer, bringing you stories and insights 00:00:09.710 --> 00:00:13.620 from leaders, driving innovation and growth across the chemical industry. 00:00:14.529 --> 00:00:18.830 Each week, we explore key trends, real world challenges, and the 00:00:18.830 --> 00:00:20.580 strategies that make an impact. 00:00:20.879 --> 00:00:21.959 Let's get started. 00:00:23.243 --> 00:00:26.873 Today we've got a great conversation focusing on the use 00:00:26.873 --> 00:00:29.023 of technology and AI in business. 00:00:29.373 --> 00:00:33.863 I've got Alan Spanos, who is the director of data solutions with ICIS 00:00:34.203 --> 00:00:39.073 and Chad Alan, who is the director of technology strategy at ICIS. 00:00:39.443 --> 00:00:42.223 We're going to be talking about technology, the role of 00:00:42.263 --> 00:00:44.013 AI in business and analytics. 00:00:44.423 --> 00:00:46.393 Risks and solutions and more. 00:00:47.073 --> 00:00:48.543 Welcome to the chemical show guys. 00:00:49.273 --> 00:00:49.413 Alan: Thanks. 00:00:49.413 --> 00:00:50.463 We appreciate it. 00:00:50.493 --> 00:00:50.963 Thanks for having us. 00:00:51.643 --> 00:00:54.333 Victoria: Absolutely Alan, let's start with you Can you just give a 00:00:54.433 --> 00:00:58.743 brief intro to who you are and how you got here in your role at ICIS? 00:00:59.324 --> 00:00:59.813 Alan: Sure thing. 00:00:59.813 --> 00:01:02.063 So I didn't start my career in chemicals. 00:01:02.113 --> 00:01:07.253 Um, I started in a management consultancy company After doing aerospace engineering 00:01:07.263 --> 00:01:13.843 at university And then I spent about 14 years In aviation in the uk in a variety 00:01:13.843 --> 00:01:18.708 starting with Moving on to data and data engineering roles after that and 00:01:18.708 --> 00:01:23.648 leading a internal practice, looking at that, within the UK's largest airline. 00:01:24.108 --> 00:01:27.478 And then at the end of the pandemic, I moved on to work for ICIS, 00:01:27.878 --> 00:01:30.958 um, performing the same kind of role, but for our customers. 00:01:30.958 --> 00:01:33.938 So making sure they have what they need to do their job. 00:01:34.738 --> 00:01:35.078 Victoria: Awesome. 00:01:35.918 --> 00:01:36.658 Chad, how about you? 00:01:37.558 --> 00:01:40.658 Chad: So I'm, I'm from a technology background, so I've been working, 00:01:40.698 --> 00:01:44.188 uh, in the parent company for ICIS for about 18 years. 00:01:44.608 --> 00:01:47.018 Um, I've been with ICIS for about eight years. 00:01:47.278 --> 00:01:50.198 Uh, so I've come from a computer science background and, just really 00:01:50.198 --> 00:01:53.948 into technology, uh, but, got into ICIS because of the interesting 00:01:53.948 --> 00:01:56.848 data and all of the very interesting problems you can solve here. 00:01:57.848 --> 00:02:01.218 Victoria: So your role today is director of data technology strategy. 00:02:01.218 --> 00:02:01.558 What is. 00:02:02.008 --> 00:02:04.258 What does that mean in the grand scheme of things? 00:02:05.338 --> 00:02:05.998 Chad: Great question. 00:02:06.108 --> 00:02:10.068 Um, so it, it means, I do a lot looking at sort of the longterm view. 00:02:10.428 --> 00:02:13.478 Um, on, the business and how technology, helps support the 00:02:13.478 --> 00:02:16.018 transformation of the business where it needs to go looking at sort of 00:02:16.018 --> 00:02:19.028 emerging trends, and, you know, things that are going to make the difference 00:02:19.028 --> 00:02:20.708 both for us and for our customers. 00:02:21.148 --> 00:02:21.488 Victoria: Awesome. 00:02:21.578 --> 00:02:23.708 Well, that'll be perfect then for today's conversation. 00:02:23.998 --> 00:02:26.868 Before we jump into a little bit more about technology. 00:02:27.348 --> 00:02:32.098 Can you guys just explain a bit about who ICIS is and who they serve? 00:02:32.998 --> 00:02:34.588 Alan: Yeah, I can jump in on that one. 00:02:34.588 --> 00:02:40.148 So, um, ICIS is, effectively a information and analytics provider for 00:02:40.178 --> 00:02:41.928 the chemical and energy industries. 00:02:42.408 --> 00:02:47.008 We have a variety of different services and products that we offer from price 00:02:47.008 --> 00:02:50.948 assessments for different raw materials and commodities, but also analytical 00:02:50.948 --> 00:02:55.408 services for things like supply and demand, analytics, and price 00:02:55.428 --> 00:02:59.008 forecasts and margin analytics in the industries that we serve, um, also 00:02:59.008 --> 00:03:04.328 part of Red X, which is a global data analytics information provider, uh, 00:03:04.368 --> 00:03:10.768 working across many industries from health care, science, legal and data 00:03:10.778 --> 00:03:15.028 services, which is what we work in, uh, covering areas like chemicals, but 00:03:15.028 --> 00:03:17.308 also aviation and other industries. 00:03:17.953 --> 00:03:18.273 Victoria: Awesome. 00:03:18.273 --> 00:03:18.963 Thank you for that. 00:03:18.973 --> 00:03:22.703 In fact, uh, I had not appreciated just how big the parent company 00:03:22.703 --> 00:03:25.933 was and all the pieces it touches, which is really exciting. 00:03:26.233 --> 00:03:29.653 And then, listeners of the chemical show will know that I have talked 00:03:29.653 --> 00:03:34.333 with one of your colleagues, John Richardson, from ICIS many times. 00:03:34.343 --> 00:03:38.903 So we'll, in fact, link to some of John's episodes on, uh, on our show notes. 00:03:38.903 --> 00:03:42.833 And as we promote this episode, uh, because John, of course, always 00:03:42.853 --> 00:03:46.568 brings a wealth of Insights into what's going on in the world of 00:03:46.588 --> 00:03:48.218 chemicals and polymers and more. 00:03:48.298 --> 00:03:53.248 So let's just get into this in terms of what do you see as the role 00:03:53.318 --> 00:03:56.098 of technology in chemicals today? 00:03:57.248 --> 00:04:01.378 Alan: From a business perspective, the industry is very competitive. 00:04:01.448 --> 00:04:04.398 So I think, what you see when we talk to our customers and the industry 00:04:04.398 --> 00:04:07.538 in general is that everybody's looking for competitive advantage. 00:04:07.538 --> 00:04:11.228 And one way of delivering that is trying to be as efficient and 00:04:11.228 --> 00:04:15.338 creative as possible with new technology that you're able to deploy. 00:04:15.793 --> 00:04:21.173 Um, so we see that customers talking about fantastic innovation in their operational 00:04:21.173 --> 00:04:26.313 processes, making sure they can really get the most out of what they're producing 00:04:26.333 --> 00:04:27.773 and who they're producing it for. 00:04:28.133 --> 00:04:34.613 And also more recently, and in the space that we operate in, making the most 00:04:34.613 --> 00:04:36.133 of understanding the market correctly. 00:04:36.133 --> 00:04:38.483 So, um, how much should you produce? 00:04:38.883 --> 00:04:40.223 Who should you sell it to? 00:04:40.603 --> 00:04:41.743 How you should use it? 00:04:42.033 --> 00:04:45.223 Those are all types of questions that we can help with, and we're 00:04:45.303 --> 00:04:49.383 seeing customers digitize those processes, and try to apply AI to 00:04:49.383 --> 00:04:53.763 them as much as possible to make better decisions and to really do things 00:04:53.763 --> 00:04:55.643 faster in such a changing environment. 00:04:55.897 --> 00:04:57.007 Victoria: Yeah, that's interesting. 00:04:57.472 --> 00:04:58.872 You know, you talk about technology. 00:04:58.872 --> 00:05:01.512 In fact, what comes to mind is I think about is the supply guys 00:05:01.512 --> 00:05:05.032 especially would run things like, an LP model, linear programming. 00:05:05.262 --> 00:05:08.092 And yet, I don't know if we talk about that anymore. 00:05:08.122 --> 00:05:10.812 Is that a predecessor to AI? 00:05:10.812 --> 00:05:12.512 Is it part of AI? 00:05:12.892 --> 00:05:14.552 Where does that, something like that fit in? 00:05:14.897 --> 00:05:18.857 Alan: Yeah, so I think, um, in my whole career, probably everything I've done up 00:05:18.857 --> 00:05:24.437 until now, today we would call AI, and it's such an umbrella term for, kind of, 00:05:24.707 --> 00:05:26.597 lots of, in my mind, it's mathematics. 00:05:27.017 --> 00:05:32.977 So, you start from incredibly simple formulas, linear programs, or just 00:05:33.167 --> 00:05:37.807 basic, Mathematics you'd learn at primary school in effect in the UK, all the 00:05:37.807 --> 00:05:41.307 way up to the modern version of what you'd call generative AI these days, 00:05:41.317 --> 00:05:47.107 which would be much more complex and, I suppose difficult mathematics, right? 00:05:47.107 --> 00:05:50.597 And I think the difference these days is we have so much more data 00:05:50.597 --> 00:05:53.217 available to pump into those models. 00:05:53.577 --> 00:05:55.877 And also the compute power is a lot cheaper. 00:05:55.877 --> 00:06:00.963 So often people will give you the kind of adage that when I was running an Atari. 00:06:01.363 --> 00:06:04.980 For my games console back in the day, my phone has got 10 times 00:06:04.980 --> 00:06:08.010 more processing power than that, or a thousand times, right? 00:06:08.010 --> 00:06:09.260 And I just put it in my pocket. 00:06:09.290 --> 00:06:13.350 So you just have a lot more compute power and a lot more data and better mathematics 00:06:13.350 --> 00:06:14.610 to solve those problems these days. 00:06:14.970 --> 00:06:15.470 Yeah, and it's, 00:06:15.480 --> 00:06:17.170 Chad: it's all, it all builds, right? 00:06:17.240 --> 00:06:21.090 It's not like, it's not like AI emerged overnight and it was, it was everything 00:06:21.090 --> 00:06:24.550 was a whole brand new, you know, so all those techniques, we would have 00:06:24.550 --> 00:06:29.100 looked at any kind of data analytics, historically, all that mathematics stuff. 00:06:29.100 --> 00:06:29.550 That is the. 00:06:29.730 --> 00:06:30.850 Core of what fed into AI. 00:06:30.850 --> 00:06:34.340 A lot of the core things around AI and how it works are actually very 00:06:34.340 --> 00:06:37.950 old ideas, but the technology in terms of the compute power and all 00:06:37.950 --> 00:06:39.340 of that just needs to catch up. 00:06:39.450 --> 00:06:43.428 And once it became powerful enough in the cloud computing revolution 00:06:43.438 --> 00:06:48.178 happened, all of a sudden we could start just throwing tons and tons of 00:06:48.178 --> 00:06:50.358 processing power at solving problems. 00:06:50.718 --> 00:06:53.538 And that's over the years become more and more powerful to the 00:06:53.538 --> 00:06:57.298 level you're now seeing with like generative AI and really getting into 00:06:57.308 --> 00:07:00.168 what people have always thought of as being artificial intelligence. 00:07:01.473 --> 00:07:04.053 Victoria: Yeah, I mean, and in fact, I think when I first heard the term 00:07:04.073 --> 00:07:09.103 artificial intelligence, which was probably 20 or more years ago, I was like, 00:07:09.143 --> 00:07:12.033 what, holy heck, are you talking about? 00:07:12.423 --> 00:07:14.843 Um, and I think people still are. 00:07:14.853 --> 00:07:19.963 What the holy heck are you talking about in our machines taking over the world? 00:07:20.193 --> 00:07:23.543 So I think we'll get, we'll get into that cause I think that falls into 00:07:23.543 --> 00:07:27.153 some of the risks maybe later, but when you think about, what's going on 00:07:27.153 --> 00:07:29.293 with technology, what are your clients. 00:07:30.508 --> 00:07:31.638 And how are they engaging? 00:07:31.648 --> 00:07:34.798 Because I think of ICIS as a data company. 00:07:34.958 --> 00:07:38.608 Um, at least that's my lens on it is that there's always this wealth 00:07:38.608 --> 00:07:41.488 of data that, uh, clients go to you. 00:07:41.488 --> 00:07:46.338 I go to you, other people go to you for data and information. 00:07:46.658 --> 00:07:49.388 So what's different today in terms of what. 00:07:49.718 --> 00:07:50.888 Your clients are asking. 00:07:51.968 --> 00:07:54.478 Alan: Yeah, I think if I start with, under that umbrella of 00:07:54.488 --> 00:07:59.158 AI, kind of what you'd term as more traditional AI use cases. 00:07:59.298 --> 00:08:03.758 I actually run the strategy and work with customers every day looking 00:08:03.758 --> 00:08:06.118 at data solutions as my job title. 00:08:06.118 --> 00:08:07.368 And what does that mean? 00:08:07.368 --> 00:08:08.188 That kind of means. 00:08:08.543 --> 00:08:11.963 Customers that they, they're not comfortable or it doesn't meet their needs 00:08:11.963 --> 00:08:13.813 just to read content from our website. 00:08:14.253 --> 00:08:16.863 They want it in bulk and they want to feed it into systems and they 00:08:16.863 --> 00:08:19.313 want to run their own AI models and things like that from it. 00:08:19.323 --> 00:08:23.053 What we see these days is that's becoming more common that customers 00:08:23.213 --> 00:08:24.473 in the industry want to do that. 00:08:25.083 --> 00:08:29.903 And probably the main driving reasons are we're needing to make 00:08:29.933 --> 00:08:33.253 decisions more quickly because the environment around us is changing. 00:08:33.843 --> 00:08:37.793 Us is changing and to do that manually these days is just not sufficient. 00:08:37.803 --> 00:08:39.833 You just can't run that in a spreadsheet anymore. 00:08:40.653 --> 00:08:42.083 People want to be more efficient. 00:08:42.143 --> 00:08:44.843 As I say with the competitors in the industry. 00:08:44.853 --> 00:08:49.733 Everybody's looking for marginal gains in terms of shaving time off of processing 00:08:49.733 --> 00:08:54.603 and running their operational processes and with the data available, they 00:08:54.603 --> 00:08:56.633 can now make smart decisions as well. 00:08:56.643 --> 00:09:02.423 So they want to run smarter algorithms and against our data and their own. 00:09:02.813 --> 00:09:06.413 And that's really what we're seeing that, customers are pushing forward and those 00:09:06.443 --> 00:09:08.103 solutions are becoming more popular. 00:09:08.873 --> 00:09:13.173 I think Chad can give you a good answer as well about kind of the more. 00:09:13.503 --> 00:09:16.443 Generate I kind of use cases and what we're doing there as well. 00:09:16.587 --> 00:09:18.090 Victoria: Yeah, I 00:09:18.187 --> 00:09:20.223 Chad: think that's that's a different space. 00:09:20.233 --> 00:09:23.773 So I think the perception of us as a data provider. 00:09:23.813 --> 00:09:24.183 That's true. 00:09:24.183 --> 00:09:26.993 We certainly are a data provider and we do create that. 00:09:26.993 --> 00:09:29.823 But we do see ourselves very much in an analytic space, right? 00:09:30.083 --> 00:09:31.433 We do some pretty cool stuff there. 00:09:31.598 --> 00:09:33.398 The gen ai I think is a slightly different beast. 00:09:33.454 --> 00:09:37.664 I think the explosion of chat GBT and what that actually means because, it's 00:09:37.664 --> 00:09:41.454 really going into the whole thing of looking at individual productivity, right? 00:09:41.784 --> 00:09:44.694 And in some ways it's quite difficult 'cause it's sort of 00:09:44.694 --> 00:09:47.274 like it can do so much, but, you know, where do you get started? 00:09:47.794 --> 00:09:51.214 So we're seeing like different levels with our customers in terms of like. 00:09:51.439 --> 00:09:55.539 We have some who are quite savvy with it and get it very quickly in that. 00:09:55.579 --> 00:09:59.089 And you can see them immediately saying, I use chat GPT to use this. 00:09:59.089 --> 00:10:03.229 I'm using Ask ICIS to do all of these tasks. 00:10:03.419 --> 00:10:06.049 And what they're getting out of it is a loss of productivity, right? 00:10:06.059 --> 00:10:10.609 It means they spend less time doing the jobs than they're doing before. 00:10:10.989 --> 00:10:13.379 And in other areas, people are still trying to get their heads around it. 00:10:13.899 --> 00:10:18.899 The difference between using a chatbot based solution versus using 00:10:18.899 --> 00:10:21.809 a traditional search where they're still just trying to type in a keyword 00:10:22.189 --> 00:10:24.449 and just trying to get results. 00:10:24.449 --> 00:10:28.409 But, it is that evolving to that space of kind of asking questions and 00:10:28.409 --> 00:10:31.929 trying to have a conversation with, an artificial person, if you want. 00:10:31.989 --> 00:10:33.199 But, those who can really get it. 00:10:33.579 --> 00:10:36.539 They can really get advantage and there's a lot there about the skill set 00:10:36.539 --> 00:10:40.539 of actually asking good questions is, is, is something that everyone really 00:10:40.539 --> 00:10:43.839 has to start learning in the world of the AI revolution, as I like to say, 00:10:44.139 --> 00:10:44.779 Victoria: absolutely. 00:10:44.839 --> 00:10:47.769 In fact, that whole aspect of prompt engineering, right? 00:10:47.789 --> 00:10:51.239 Figuring out what questions to ask, and then to keep asking. 00:10:51.909 --> 00:10:55.519 Um, I know myself just from my use of chat GPT over the past 00:10:55.519 --> 00:10:57.679 2 years, and how it's evolved. 00:10:57.729 --> 00:11:02.559 I can get to the types of answers I'm looking for rather quickly because I 00:11:02.559 --> 00:11:07.879 know exactly how to start asking the questions, how to amend those questions, 00:11:07.889 --> 00:11:11.409 how to keep driving it to a solution, but it's, you know, the first question 00:11:11.409 --> 00:11:16.249 you ask is usually, just like in real life, frankly, the first question you 00:11:16.249 --> 00:11:18.059 ask is probably not the right question. 00:11:18.249 --> 00:11:20.029 You have to keep asking questions. 00:11:20.494 --> 00:11:21.914 Chad: Yeah, absolutely. 00:11:22.024 --> 00:11:25.184 That's very much the case of, advanced users with, with 00:11:25.184 --> 00:11:26.684 looking at using AI technology. 00:11:26.684 --> 00:11:29.604 They'll ask a question and if it doesn't give you the answer to the one, you just 00:11:29.604 --> 00:11:33.584 go and reframe your question a bit, you know, knowing is that it's smart and it 00:11:33.604 --> 00:11:37.464 has this amazing amount of knowledge, but it's not always that smart and maybe it 00:11:37.484 --> 00:11:40.894 just needs a little more context and help from you to, to get the answer you want. 00:11:41.724 --> 00:11:44.064 Victoria: So let's talk a little bit about Ask ICIS. 00:11:44.064 --> 00:11:48.974 So I know that you've recently rolled out this, Ask iCIS, which I guess is 00:11:48.974 --> 00:11:54.754 an AI based tool, to really, I guess, interrogate the data and get answers. 00:11:54.754 --> 00:11:55.054 Right? 00:11:55.054 --> 00:11:58.464 So can can talk about that and how you guys are using it. 00:11:59.364 --> 00:12:03.284 Chad: Yeah, Ask ICIS is our generative AI assistant, as 00:12:03.284 --> 00:12:04.524 we call it, that is sort of a. 00:12:05.234 --> 00:12:08.414 specializes in these sort of the chemicals in the energy sector. 00:12:08.974 --> 00:12:12.734 And it's there to, you know, like a chat GPT, you can ask it questions and it 00:12:12.734 --> 00:12:14.014 will answer those questions for you. 00:12:14.194 --> 00:12:16.754 Unlike chat GPT, it's not just a general purpose tool. 00:12:16.904 --> 00:12:19.094 You know, it's not there for answering every question. 00:12:19.104 --> 00:12:23.564 It's focused specifically in the markets and it's built on the data and content 00:12:23.564 --> 00:12:25.764 that we're taking and create with an ICIS. 00:12:25.774 --> 00:12:29.804 So when you ask a question about, like what's happening in the styrene 00:12:29.804 --> 00:12:31.654 market or what's what's going on there. 00:12:31.874 --> 00:12:35.754 It understands more context about what styrene is and what you've used 00:12:35.754 --> 00:12:37.514 it for and it's pulling content from 00:12:38.184 --> 00:12:41.384 our price assessments or forecasts or from from our news 00:12:41.634 --> 00:12:43.004 to help answer those questions. 00:12:43.054 --> 00:12:45.124 And it gives you a much more powerful answer as a result. 00:12:46.024 --> 00:12:46.734 Victoria: So what is it? 00:12:46.744 --> 00:12:47.764 So what are the use cases? 00:12:47.774 --> 00:12:51.174 How are you seeing your clients using it and really getting value? 00:12:52.374 --> 00:12:55.739 Chad: So I think the big thing there is people are looking at 00:12:55.739 --> 00:12:57.269 a really key time saver, right? 00:12:57.269 --> 00:13:01.639 It's that they can get the job done a lot quicker than, um, just going 00:13:01.639 --> 00:13:02.809 and doing the work themselves. 00:13:02.869 --> 00:13:03.169 You know? 00:13:03.289 --> 00:13:05.899 So we have some people who are saying that they, they go and use 00:13:05.899 --> 00:13:07.399 it and they can save hours of work. 00:13:07.769 --> 00:13:11.099 They normally take them hours to go look at our content, look at other content 00:13:11.199 --> 00:13:14.149 elsewhere, and ask that, and now they can just have a single session with Ask 00:13:14.149 --> 00:13:19.054 ICIS, ask some of those questions and get what they need, you know, and it 00:13:19.054 --> 00:13:23.044 accesses all the content we have around market dynamics and companies, topics, 00:13:23.044 --> 00:13:25.284 events, supply and demand fundamentals. 00:13:25.284 --> 00:13:25.864 And so 00:13:26.084 --> 00:13:28.694 Victoria: I'm assuming is it, I'm assuming it's available only to 00:13:28.694 --> 00:13:32.554 people that are already subscribing to your services and data. 00:13:32.554 --> 00:13:33.074 Is that right? 00:13:33.194 --> 00:13:33.924 Yeah, that's correct. 00:13:34.358 --> 00:13:36.978 Alan: I think that the important thing that we do with Ssk 00:13:36.978 --> 00:13:37.418 ICIS, 00:13:37.438 --> 00:13:41.728 Alan: that, Some of the like, Bard, I think does it now from Google and some 00:13:41.738 --> 00:13:45.648 of the other Gen AI tools is originally they didn't tell you what information 00:13:45.648 --> 00:13:47.118 they were using to give you the answer. 00:13:47.658 --> 00:13:50.508 So, in effect, they would just say, hey, here's the truth. 00:13:50.548 --> 00:13:51.908 And a lot of the time it wasn't right. 00:13:51.928 --> 00:13:53.108 It was a lie, right? 00:13:53.118 --> 00:13:55.178 It was the AI getting it wrong. 00:13:55.178 --> 00:13:58.748 And what we've done from the beginning and some of the learnings we've taken 00:13:58.758 --> 00:14:02.708 from our sort of sister companies across Bellex was ground the answers 00:14:02.708 --> 00:14:04.028 in the articles that we already have. 00:14:04.028 --> 00:14:04.088 So. 00:14:04.658 --> 00:14:08.878 We'll give you an answer and say, Hey, you've asked about styrene in this market. 00:14:08.938 --> 00:14:10.198 Here's what we think is going on. 00:14:10.538 --> 00:14:13.298 And here's the relevant articles that we've used to create this response. 00:14:13.298 --> 00:14:16.538 And then you can click through if you subscribe to those and see. 00:14:16.883 --> 00:14:19.533 And if you don't, you can talk to us about if you want to get a 00:14:19.533 --> 00:14:20.773 subscription to those articles. 00:14:20.783 --> 00:14:25.073 So trust for us is a really big thing in using AI, and I know 00:14:25.073 --> 00:14:26.253 we'll go on to talk about that. 00:14:26.253 --> 00:14:29.073 But I think I would never trust the answers if I didn't 00:14:29.073 --> 00:14:29.943 know where they came from. 00:14:29.943 --> 00:14:31.190 And that's really 00:14:31.191 --> 00:14:31.607 Chad: important. 00:14:31.607 --> 00:14:34.903 Yeah, that was when we were developing Ask ICIS. 00:14:34.913 --> 00:14:38.343 That was really the heart of where we started, which is, you know, our 00:14:38.373 --> 00:14:39.983 customers really value the content we do. 00:14:39.983 --> 00:14:41.183 They know they can trust us. 00:14:41.413 --> 00:14:45.133 They know we do our research, we do know what we're doing, and the 00:14:45.133 --> 00:14:46.863 tool must do the same in that sense. 00:14:46.873 --> 00:14:50.693 So, it's very much built into the actual design, to make sure that we 00:14:50.693 --> 00:14:56.233 know that if you see it, it's something that somebody in ICI has said. 00:14:56.233 --> 00:15:00.453 Because that's, trust is the absolute most important thing, and when you 00:15:00.463 --> 00:15:02.963 just use a chat GPT or looking at that, 00:15:03.463 --> 00:15:08.633 they talk very confidently, right, when you use these services, and that 00:15:08.793 --> 00:15:11.913 always gives you the suggestion that is, it's always right, but it's not. 00:15:11.913 --> 00:15:15.973 And it's very hard to tell when it's right or not. 00:15:15.973 --> 00:15:20.183 If you can't go and say, can I just look where that content came 00:15:20.183 --> 00:15:22.313 from to see if that's correct. 00:15:22.823 --> 00:15:25.673 Victoria: Well, and I really appreciate that as well, because 00:15:25.913 --> 00:15:29.153 especially the referencing back to the specific articles, because as 00:15:29.153 --> 00:15:32.203 we know, things change over time. 00:15:32.628 --> 00:15:32.968 Right? 00:15:33.128 --> 00:15:36.728 And so when you get an answer and say, Oh, the answer is X. 00:15:36.758 --> 00:15:37.208 Well, okay. 00:15:37.208 --> 00:15:40.758 The answer X was correct back in January. 00:15:41.218 --> 00:15:44.598 But these other three big things happened and now the answer is Y. 00:15:44.608 --> 00:15:47.888 So understanding and being able to kind of trace some of that I'm 00:15:47.888 --> 00:15:51.158 sure is super important to your clients and really to everyone. 00:15:51.158 --> 00:15:51.348 Right? 00:15:51.348 --> 00:15:55.458 Because we, when we ask a question, and somebody is coming in to Ask 00:15:55.618 --> 00:15:59.598 ICIS and be like, Oh, I'm asking this question because I have to go talk to 00:15:59.598 --> 00:16:04.678 my president, the board, the boss, a customer, whatever, you want to go 00:16:04.678 --> 00:16:06.808 back to that individual with confidence. 00:16:06.861 --> 00:16:08.158 And I think that's a, that's 00:16:08.158 --> 00:16:13.738 Alan: a common, requirement, I think, for any analytics or AI back from 00:16:13.738 --> 00:16:16.638 before people talked about generative AI, you know, we got some great. 00:16:17.053 --> 00:16:20.633 Data science teams working on price forecasts as well as what we do with 00:16:21.103 --> 00:16:25.493 price assessments and one of the key things that we work on with customers 00:16:25.493 --> 00:16:29.253 is explainability of those analytics because As you say if you're using 00:16:29.253 --> 00:16:34.903 a third party to create them If your director or cfo or cmo asks you a 00:16:34.903 --> 00:16:36.748 question about well, how did that work? 00:16:37.088 --> 00:16:37.648 Get to that. 00:16:37.648 --> 00:16:38.588 That's a strange number. 00:16:38.588 --> 00:16:39.498 I don't understand it. 00:16:39.978 --> 00:16:40.038 Chad: You 00:16:40.038 --> 00:16:41.218 Alan: need to be able to explain that to them. 00:16:41.248 --> 00:16:43.408 If you can't, you lose trust immediately. 00:16:43.408 --> 00:16:48.758 And I think no AI tool is really going to be used by customers or any 00:16:48.768 --> 00:16:50.548 users if it can't explain itself. 00:16:50.548 --> 00:16:51.338 It's very important. 00:16:51.478 --> 00:16:52.258 Victoria: Yeah, that's great. 00:16:52.268 --> 00:16:53.558 And that's actually a great segue. 00:16:53.558 --> 00:16:59.248 Let's talk a little bit about maybe some of the concerns and the risks that we 00:16:59.408 --> 00:17:03.898 think about as we think about technology and AI and how those are being mitigated. 00:17:04.923 --> 00:17:05.523 Chad: Yeah, sure. 00:17:05.783 --> 00:17:09.613 I mean, the big, the big thing that people worry about is hallucinations, right? 00:17:09.673 --> 00:17:11.833 It's the phrase everyone's kind of using. 00:17:11.883 --> 00:17:14.443 It's a bit of a disservice in some, some cases to the machine. 00:17:14.453 --> 00:17:18.413 It says it's hallucinating and that it's just hallucinating. 00:17:19.393 --> 00:17:22.563 They're actually making very, very, very educated guesses, right? 00:17:23.033 --> 00:17:23.963 That's how they actually work. 00:17:23.963 --> 00:17:27.763 But the one thing that people worry about a lot is if it goes and tells you so. 00:17:28.063 --> 00:17:28.243 Right? 00:17:28.243 --> 00:17:29.953 Can you actually handle that? 00:17:30.013 --> 00:17:31.783 Can it actually manage that hallucination? 00:17:31.843 --> 00:17:31.903 Yeah. 00:17:32.323 --> 00:17:36.213 And that's something as far as how we, we took and built it in a pattern, it's 00:17:36.603 --> 00:17:42.803 fairly common in the market to make sure that, when we train, um, Ask ICIS, we 00:17:42.893 --> 00:17:45.863 tell it that it prioritizes our view. 00:17:46.588 --> 00:17:49.608 In our content right over any other view. 00:17:49.648 --> 00:17:53.458 So it's it's always pulling from our content first, which we make accessible 00:17:53.468 --> 00:17:58.138 to it and effectively does a search and pulls back the information and then 00:17:58.138 --> 00:18:01.728 it tries to answer the question that customer has based on that information. 00:18:02.048 --> 00:18:06.478 It looks at and it's it's not drawing on as much the the LLM. 00:18:07.293 --> 00:18:12.823 To answer that question, it's using how its ability to read and its ability to 00:18:12.823 --> 00:18:14.853 reason to help answer that question. 00:18:14.853 --> 00:18:17.703 But the data it's pulling from is ICIS content. 00:18:18.073 --> 00:18:18.413 Victoria: Got it. 00:18:18.443 --> 00:18:21.353 So it's, so it's a little bit less about generative, right? 00:18:21.353 --> 00:18:26.443 So I think this aspect of generative being creative and so creating 00:18:26.503 --> 00:18:29.573 answers that don't exist, but rather 00:18:29.573 --> 00:18:34.373 generating and then testing back to what is already known to be true. 00:18:35.043 --> 00:18:38.643 Chad: So it's, it's generative in the sense and it's creative in the sense of 00:18:39.043 --> 00:18:42.553 it makes content that's more specific to what you're trying to find out. 00:18:42.603 --> 00:18:42.933 Right. 00:18:43.123 --> 00:18:45.393 But it could be made out of six pieces of our content. 00:18:45.723 --> 00:18:46.033 Right. 00:18:46.043 --> 00:18:48.993 So it could have pulled it from three news articles, a price report 00:18:49.013 --> 00:18:53.003 and forecast because you are asking about something that hit all of those 00:18:53.003 --> 00:18:53.483 areas. 00:18:53.843 --> 00:18:57.983 And so it, it then it creates a unique piece of content for that particular 00:18:57.983 --> 00:19:00.323 answer, but it's all based on our content. 00:19:00.563 --> 00:19:01.343 Victoria: Yeah, that's helpful. 00:19:01.463 --> 00:19:05.723 What are the other risks that you see or that people concerns that people raise? 00:19:06.423 --> 00:19:09.253 Chad: I guess one of the other ones that comes across a lot is 00:19:09.253 --> 00:19:10.703 the, is AI going to take my job? 00:19:11.093 --> 00:19:13.913 That's the, the big one that, that most people say. 00:19:14.373 --> 00:19:18.033 Victoria: Yeah, do we still need the ICIS consultants if we have this? 00:19:18.563 --> 00:19:19.973 Chad: Certainly it would be my answer. 00:19:20.033 --> 00:19:23.363 And I think, I think you need to look at AI and the tools there. 00:19:24.048 --> 00:19:27.648 They make it easier to do your job or to do somebody's job with that, 00:19:27.648 --> 00:19:31.544 but it's not just a like for like replacement, like really, our content 00:19:31.544 --> 00:19:34.934 creators in our business, they, they answer very hard questions, right? 00:19:35.144 --> 00:19:38.204 They're looking at very complicated situations and 00:19:38.264 --> 00:19:39.944 apply subject matter expertise. 00:19:40.294 --> 00:19:42.554 Most of that's not in the question that's being asked, right? 00:19:42.714 --> 00:19:46.174 So when we actually look at the content, we're looking at these things, 00:19:46.175 --> 00:19:47.505 There's a lot more there to add that. 00:19:47.505 --> 00:19:52.215 So what element, you know, um, it's fine to say that as a piece of information, 00:19:52.505 --> 00:19:58.135 a tornado wiped out interstate 95 when Hurricane Milton, was hitting Florida. 00:19:58.425 --> 00:19:59.125 That's great. 00:19:59.225 --> 00:20:01.875 It's more important to know that that's a major, uh, Transport 00:20:01.875 --> 00:20:03.935 hub to the port of Tampa, right? 00:20:03.995 --> 00:20:06.775 Which actually could potentially is going to affect some customers there. 00:20:07.165 --> 00:20:09.475 So that's hard to figure out. 00:20:10.485 --> 00:20:13.914 Alan: I think as well with any AI, not just the sort of 00:20:13.965 --> 00:20:15.405 more modern generative AI. 00:20:15.405 --> 00:20:18.904 It's really a lot of the cases only as good as what goes into it in the first 00:20:18.905 --> 00:20:20.685 place from an information perspective. 00:20:20.685 --> 00:20:22.755 So we're in a privileged place. 00:20:22.795 --> 00:20:26.565 I think in ICIS that we have a lot of good information that we can feed into 00:20:26.565 --> 00:20:29.965 these things and actually, Make them more intelligent to help you make a decision. 00:20:30.465 --> 00:20:34.985 I think as well, like with, not just Ask ICIS, but the, the tools that we create, 00:20:35.025 --> 00:20:41.045 but also internally, how we use a I, um, a lot of it is around in the industry. 00:20:41.665 --> 00:20:45.805 For sure, and every industry has this challenge is how do 00:20:45.805 --> 00:20:46.995 you scale up your workforce? 00:20:47.015 --> 00:20:49.885 And how do you make them more literate to use these kind of tools? 00:20:50.045 --> 00:20:50.305 Victoria: Right? 00:20:50.365 --> 00:20:53.445 Alan: And both from a benefit perspective, as Chad was talking 00:20:53.445 --> 00:20:56.735 about from making you more efficient, faster doing your job and so on. 00:20:56.735 --> 00:21:02.085 But also the risks and pitfalls very, very early on, like, you know, because 00:21:02.085 --> 00:21:07.335 we're At heart data analysis company in a group in general, we had guidance around 00:21:07.335 --> 00:21:09.425 employees using generative AI tools. 00:21:10.195 --> 00:21:12.735 Don't put confidential information in chat GPT. 00:21:12.745 --> 00:21:15.735 It's just going to harvest it and start spitting out to other people and 00:21:16.025 --> 00:21:16.255 Chad: I'm 00:21:16.255 --> 00:21:18.345 Alan: sure a lot of companies their employees are doing 00:21:18.345 --> 00:21:19.435 that and they don't realize. 00:21:19.445 --> 00:21:20.495 So, um, yeah. 00:21:20.980 --> 00:21:25.140 Making sure that you get good training out to your employees, that you teach 00:21:25.140 --> 00:21:29.790 them what these tools can do, why they're good, why they're bad, or what are the 00:21:29.790 --> 00:21:31.540 risks around them is very important. 00:21:31.880 --> 00:21:35.200 And we have to always keep up to date with the latest and greatest new 00:21:35.200 --> 00:21:36.780 tools that are available to do that. 00:21:37.330 --> 00:21:40.815 Next year there'll be another Chat GPT like thing that will come out that we've 00:21:40.815 --> 00:21:45.755 got to make sure that we use it in the right way Like I like to think of AI 00:21:45.755 --> 00:21:47.765 analytics tools like a builders toolkit. 00:21:48.225 --> 00:21:52.330 Someone gives you like a brand new drill You don't just Try and do 00:21:52.330 --> 00:21:53.370 something with it straight away. 00:21:53.370 --> 00:21:54.560 You read the manual, right? 00:21:54.560 --> 00:21:58.000 Like, so, try to make it, you should do that. 00:22:00.650 --> 00:22:02.727 Or you drill a few holes in the walls and see what happens. 00:22:02.862 --> 00:22:06.630 I mean, hey, maybe, maybe I'm that guy who reads the manual. 00:22:08.460 --> 00:22:11.070 But I think it's important that, they're fascinating. 00:22:11.070 --> 00:22:11.690 They're brilliant. 00:22:11.720 --> 00:22:13.680 They're, they're important tools and they are. 00:22:14.370 --> 00:22:20.000 Chad and I will often geek out on, it's a very exciting time to be in business 00:22:20.040 --> 00:22:24.200 to use these tools in your personal life as well, but you should make sure that 00:22:24.200 --> 00:22:28.040 you do a little bit of due diligence to understand which tool to use at what, 00:22:28.340 --> 00:22:32.680 uh, in what way, and also, I mean, not just risks, like there's a big risk 00:22:32.730 --> 00:22:34.530 that you run up with lots of costs. 00:22:34.955 --> 00:22:36.675 Some of these tools can be quite expensive. 00:22:36.675 --> 00:22:41.025 So if you can solve, um, my mantra has always been in the AI space, right? 00:22:41.025 --> 00:22:45.365 If you can write a linear program to solve a problem to a level of 00:22:45.365 --> 00:22:48.675 accuracy, that's acceptable, why would you deploy a neural network? 00:22:49.055 --> 00:22:51.055 It's like 20 times more expensive, right? 00:22:51.055 --> 00:22:55.325 So having that cost benefit analysis, when you're deploying these tools is important 00:22:55.325 --> 00:22:56.405 from a business point of view as well. 00:22:56.445 --> 00:22:56.755 Victoria: Yeah. 00:22:56.815 --> 00:22:58.585 Well, especially I think when you think about the. 00:22:59.140 --> 00:23:05.600 Maybe the broader social context of it is the power Usage that goes along with AI 00:23:05.600 --> 00:23:09.200 as you say the these sophisticated neural networks I don't need to ask it how to 00:23:09.200 --> 00:23:14.320 make a peanut butter and jelly sandwich Because I can get the answer anywhere 00:23:14.320 --> 00:23:20.190 else But asking it things that I can't find the answers to becomes critical so 00:23:20.190 --> 00:23:23.510 that I'm using the resources wisely Yeah. 00:23:23.740 --> 00:23:24.300 A hundred percent. 00:23:24.990 --> 00:23:27.280 Do you, folks, you talk a little bit about training. 00:23:27.580 --> 00:23:32.550 Do you guys provide training to your customers when they start using Ask ICIS? 00:23:33.100 --> 00:23:37.390 Because obviously you want them to use it effectively and get the benefit. 00:23:38.120 --> 00:23:38.970 Alan: Yeah, absolutely. 00:23:38.970 --> 00:23:42.780 So, so we have a customer service team in effect or customer success 00:23:42.780 --> 00:23:46.650 team, which not just Ask ICIS, but they're available to our customers 00:23:46.650 --> 00:23:47.900 for any product that we ship. 00:23:48.410 --> 00:23:52.620 As part of onboarding and general queries, if anybody has any questions, uh, we 00:23:52.620 --> 00:23:56.170 can do that as interestingly, a use case for those types of algorithms that you 00:23:56.170 --> 00:23:57.850 can ask them how to use them as well. 00:23:57.880 --> 00:23:58.230 So it's 00:23:59.700 --> 00:24:00.750 Victoria: actually really good. 00:24:00.750 --> 00:24:01.760 I hadn't thought about 00:24:01.760 --> 00:24:01.940 Alan: that. 00:24:01.940 --> 00:24:06.870 Yeah, Chad, just don't Ask ICIS specifically anything that he helps. 00:24:07.180 --> 00:24:11.280 Chad: Yeah, I think it's, it's something I know that as we develop it, it's 00:24:11.280 --> 00:24:12.880 always about making it easier and easier. 00:24:12.880 --> 00:24:15.030 I mean, I think a lot with these chatbots is trying to 00:24:15.030 --> 00:24:16.290 make them as simple as possible. 00:24:16.300 --> 00:24:19.955 Like it is just a box, you type in a question and you try 00:24:19.955 --> 00:24:21.035 to have a conversation with it. 00:24:21.655 --> 00:24:22.795 But I think, there's always elements. 00:24:22.795 --> 00:24:24.245 How can we make it easier? 00:24:24.245 --> 00:24:25.915 How do we help people to onboard? 00:24:25.915 --> 00:24:27.375 And that's something we're always looking at. 00:24:27.515 --> 00:24:31.125 And we can evolve things, the tool and how we approach things, of course, too. 00:24:31.975 --> 00:24:32.325 Victoria: Awesome. 00:24:32.795 --> 00:24:33.075 Love it. 00:24:33.295 --> 00:24:34.495 So what's next guys? 00:24:34.495 --> 00:24:40.040 What do you see as So Ask ICIS is the big thing for 2024 and obviously continuing to 00:24:40.040 --> 00:24:44.490 roll it out into 2025, which is when this episode is going to be getting published. 00:24:44.860 --> 00:24:45.280 What else? 00:24:45.300 --> 00:24:45.870 What's next? 00:24:45.870 --> 00:24:49.570 What should we be looking for from, from your team, from ICIS? 00:24:50.270 --> 00:24:54.850 Alan: I think more innovation, I think, is what we, we want to kind of promise 00:24:54.850 --> 00:24:58.350 to our customer base and ourselves that we're going to keep trying to push 00:24:58.350 --> 00:25:03.110 the boundaries of what's possible and what's sensible in our space as well. 00:25:04.115 --> 00:25:07.295 And I think whilst my head of product would kill me if I said anything 00:25:07.295 --> 00:25:10.375 specific, from my perspective, that's more investment in some of 00:25:10.375 --> 00:25:15.395 these tools, like Ask ICIS, growing its capability, but also in my space. 00:25:16.370 --> 00:25:20.060 new analytics for customers to use to help them make decisions across our 00:25:20.060 --> 00:25:24.280 industries, but also keeping up to speed with developments in the software 00:25:24.280 --> 00:25:27.710 and analytics market if they want to take data directly from us as well. 00:25:27.710 --> 00:25:30.120 We've done some investments on that recently as well. 00:25:30.430 --> 00:25:33.930 Um, and we need to make sure those tools are up to date when our 00:25:34.050 --> 00:25:35.900 customers come asking us to help them. 00:25:36.370 --> 00:25:37.650 So yeah, keep watching us. 00:25:37.650 --> 00:25:40.300 We'll try and post as much as we can on socials and things like 00:25:40.300 --> 00:25:41.760 that personally and as a group. 00:25:41.760 --> 00:25:43.850 And, Yeah, I can't be more specific. 00:25:43.850 --> 00:25:49.160 I'm afraid that if you want to be braver than me, but I think it's, 00:25:49.170 --> 00:25:53.480 Chad: you know, as always, it's with with all generative AI tools 00:25:53.520 --> 00:25:54.930 that people use today, right? 00:25:55.300 --> 00:25:58.360 It's not always going to be about specific features or things you're 00:25:58.360 --> 00:26:02.430 looking at, but it's just, it's a just how do you make the experience better? 00:26:02.600 --> 00:26:05.040 It's a lot of a lot of fine tuning. 00:26:05.415 --> 00:26:09.755 Of how things work or behave and respond and looking at particular use 00:26:09.755 --> 00:26:13.535 cases on how does it answer particular kinds of questions or things like that. 00:26:13.535 --> 00:26:13.705 Right. 00:26:13.705 --> 00:26:16.965 So it's it's it's hard to say exactly what's there because we're 00:26:16.965 --> 00:26:19.675 looking at, feedback from customers and seeing what's important. 00:26:19.715 --> 00:26:20.924 And, that's what we're looking at. 00:26:21.105 --> 00:26:22.565 Yeah, that's what drives. 00:26:22.735 --> 00:26:26.165 Alan: I think what I would say is, well, if you want an advanced look at any of 00:26:26.195 --> 00:26:30.225 the stuff we're developing at the moment, we have like a custom advisory panel. 00:26:30.345 --> 00:26:34.955 You can apply to and we'll be testing like tool prototypes and things like 00:26:34.955 --> 00:26:36.515 that with with those customers. 00:26:36.525 --> 00:26:38.565 So, um, how does somebody 00:26:38.565 --> 00:26:39.445 Victoria: get on that panel? 00:26:39.445 --> 00:26:42.715 Is there a website or an email or can I just how do we direct 00:26:42.755 --> 00:26:43.275 Alan: people to you? 00:26:43.745 --> 00:26:46.035 Yeah, we can, we can supply that if I don't know if they're 00:26:46.035 --> 00:26:48.585 still recruiting people, but I can give you that information. 00:26:48.595 --> 00:26:49.085 Well, and, 00:26:49.135 --> 00:26:51.905 Victoria: and for sure, we're going to include a link to Ask ICIS. 00:26:51.905 --> 00:26:54.475 And I know if people are already ICIS customers, they can probably 00:26:54.475 --> 00:26:57.475 talk to their salesperson, the relationship focal point to learn more. 00:26:57.475 --> 00:26:57.845 Awesome. 00:26:58.025 --> 00:27:00.725 And I think, uh, you know, if nothing else, as we head into 00:27:00.725 --> 00:27:06.865 2025, learning how to use AI effectively, including tools like Ask 00:27:06.865 --> 00:27:11.405 ICIS becomes a real differentiator for companies and for individuals. 00:27:11.945 --> 00:27:12.925 Chad: Yeah, absolutely. 00:27:13.345 --> 00:27:14.135 There's, there's a lot. 00:27:14.225 --> 00:27:17.485 I think customers, people working in this industry to think about about 00:27:17.495 --> 00:27:21.698 how, how can we use this technology to help improve our productivity? 00:27:21.848 --> 00:27:24.638 How can we help our individual people get more productive? 00:27:24.808 --> 00:27:28.288 Time to do the things that add value to your business and less time 00:27:28.298 --> 00:27:30.648 doing, you know, some mundane tasks. 00:27:30.728 --> 00:27:34.558 That's where these tools are very powerful to get people to focus on the stuff 00:27:34.558 --> 00:27:36.208 they tend to like doing more as well. 00:27:36.668 --> 00:27:40.768 Alan: So, we honestly, we feel like these types of tools and us as 00:27:40.768 --> 00:27:43.798 a partner with our customers, we want to augment what they do. 00:27:44.098 --> 00:27:45.308 We want to make them better. 00:27:45.718 --> 00:27:50.968 Ask ICIS as a name, even think of us as basically, you're the superhero in our 00:27:50.968 --> 00:27:52.808 story and we want to be a sidekick, right? 00:27:52.808 --> 00:27:57.258 We want to help you, we want to help you get done what you need to do, and we 00:27:57.258 --> 00:27:59.668 want to make you look great to your boss. 00:27:59.898 --> 00:28:01.256 That's effectively what we would like. 00:28:01.256 --> 00:28:02.290 And that is a 00:28:02.290 --> 00:28:03.448 Victoria: great way to end. 00:28:03.448 --> 00:28:06.708 So, Alan and Chad, thank you so much for your time today. 00:28:06.708 --> 00:28:07.308 This has been great. 00:28:08.148 --> 00:28:08.548 Alan: It's a pleasure. 00:28:08.728 --> 00:28:09.538 It's a pleasure. 00:28:09.538 --> 00:28:09.858 very 00:28:09.858 --> 00:28:10.178 Victoria: much. 00:28:10.178 --> 00:28:10.538 Absolutely. 00:28:10.538 --> 00:28:11.798 And thank you everyone for listening. 00:28:11.798 --> 00:28:14.298 Keep listening, keep following, keep sharing, and we will 00:28:14.298 --> 00:28:15.808 talk with you again soon. 00:28:17.908 --> 00:28:20.068 Thanks for joining us today on The Chemical Show. 00:28:20.408 --> 00:28:24.868 If you enjoyed this episode, be sure to subscribe, leave a review, 00:28:25.138 --> 00:28:28.668 and most importantly, share it with your friends and colleagues. 00:28:29.528 --> 00:28:32.418 For more insights, visit TheChemicalShow. 00:28:32.428 --> 00:28:34.948 com and connect with us on LinkedIn. 00:28:35.598 --> 00:28:39.548 You can find me at Victoria King Meyer on LinkedIn, and you can also 00:28:39.548 --> 00:28:41.498 find us at The Chemical Show Podcast. 00:28:41.908 --> 00:28:45.138 Join us next time for more conversations and strategies 00:28:45.398 --> 00:28:47.188 shaping the future of the industry. 00:28:47.558 --> 00:28:48.268 We'll see you soon.