Listen To Victoria Talk To Alan And Chad About AI, ICIS, And More:
Discover how AI is becoming as essential as the internet or mobile phones in the chemical industry with guests Alan Spanos and Chad Macey from ICIS. Alan explains the vision of being a supportive sidekick to customers while discussing the main barriers to AI adoption, emphasizing the importance of trust and effective communication. Meanwhile, Chad highlights the significant role of AI tools in improving decision-making and productivity, and addresses the ethical considerations and potential risks associated with AI technology.
Victoria Meyer and her guests delve into personal anecdotes illustrating the rapid evolution of technology, comparing past skepticism about the internet to current acceptance. The conversation progresses to the innovative AI applications at ICIS, such as the ASK ICIS tool, designed to save users time by providing focused, reliable answers from vast data sets. Highlighting transparency, customer support, and continuous innovation, the episode underscores the transformative potential of AI in the chemical industry, all while ensuring ethical usage and customer trust.
Learn more about the following topics this week:
- The role of technology in chemicals today
- AI adoption and barriers and the future of work
- Client engagement with AI and technology tools today
- Ask ICIS: how are clients are engaging and getting value from ICIS’ gen AI tool
- Trust and transparency in AI: concerns about AI “hallucinations” and ensuring reliable outputs
Killer Quote: “The future of AI in business is not about replacing human expertise but augmenting it. It’s about transforming mountains of data into actionable insights, empowering decision-makers to steer their companies towards smarter, faster, and more innovative paths.” — Alan Spanos
Other links:
Download: 10 Leadership Lessons for Chemical Executives
Download: 7 Trends Shaping the Future of the Chemical Industry
John Richardson Episode 179: How the Global Chemical Market is Shifting with John Richardson of ICIS
John Richardson Episode 144: Petrochemicals in 2024: Capacity Overbuild and Codependent Markets with John Richardson of ICIS
John Richardson Episode 102: China’s Aging Demographics and the Future of the Chemical Industry with John Richardson
John Richardson Episode 79: How The China Market Affects Chemical Prices With John Richardson
John Richardson Episode 36: How China’s Response To Chemicals Is Affecting the World With John Richardson of ICIS
John Richardson Episode 22: Understanding Market Demand To Manage Your Business Amid Uncertainty With John Richardson
Watch Victoria’s Conversation With Alan And Chad From ICIS About AI And More:
Harnessing AI To Accelerate Business Insights With Alan Spanos and Chad Macey of ICIS
Today we’ve got a great conversation focusing on the use of technology and AI in business. I’ve got Alan Spanos, who is the director of data solutions with ICIS and Chad Alan, who is the director of technology strategy at ICIS. We’re going to be talking about technology, the role of AI in business and analytics, risks, solutions, and more.
Subscribe To The Chemical Show On Your Favorite Platform
Welcome to The Chemical Show guys.
Alan: Thanks. We appreciate it.
Chad: Thanks for having us.
Absolutely. Alan, let’s start with you. Can you just give a brief intro to who you are and how you got here in your role at ICIS?
Alan: Sure thing. So I didn’t start my career in chemicals. I started in a management consultancy company after doing aerospace engineering at university. Then I spent about 14 years in aviation in the UK in a variety of roles. Starting with analytics roles and moving on to data and data engineering roles after that and leading an internal practice, looking at that within the UK’s largest airline. Then at the end of the pandemic, I moved on to work for ICIS, performing the same kind of role, but for our customers. So making sure they have what they need to do their job.
Awesome. Chad, how about you?
Chad: So I’m from a technology background, I’ve been working in the parent company for ICIS for about 18 years. I’ve been with ICIS for about eight years. So I’ve come from a computer science background and really into technology, but got into ICIS because of the interesting data and all of the very interesting problems you can solve here.
So your role today is director of data technology strategy. What does that mean in the grand scheme of things?
Chad: Great question. So it means I do a lot of looking at the longterm view on the business and how technology helps support the transformation of the business. Where it needs to go looking at emerging trends, and things that are going to make the difference both for us and for our customers.
Awesome. That’ll be perfect then for today’s conversation. Before we jump into a little bit more about technology, can you guys just explain a bit about who ICIS is and who they serve?
Alan: ICIS is effectively an information and analytics provider for the chemical and energy industries. We have a variety of different services and products that we offer from price assessments for different raw materials and commodities, but also analytical services for things like supply and demand, analytics, and price forecasts and margin analytics in the industries that we serve. Also we are part of RELX, which is a global data analytics information provider working across many industries from healthcare, science, legal and data services, which is what we work in covering areas like chemicals but also aviation and other industries.
Awesome. Thank you for that. In fact, I had not appreciated just how big the parent company was and all the pieces it touches, which is really exciting. Listeners of The Chemical Show will know that I have talked with one of your colleagues, John Richardson, from ICIS many times. So we’ll link to some of John’s episodes on our show notes, because John always brings a wealth of insights into what’s going on in the world of chemicals and polymers and more.
So let’s just get into this in terms of what do you see as the role of technology in chemicals today?
Alan: From a business perspective, the industry is very competitive. So I think what we see when we talk to our customers and the industry in general is that everybody’s looking for a competitive advantage. One way of delivering that is trying to be as efficient and creative as possible with new technology that you’re able to deploy. So we see customers talking about fantastic innovation in their operational processes, making sure they can really get the most out of what they’re producing and who they’re producing it for. Also more recently, and in the space that we operate in, making the most of understanding the market correctly.
Alan: How much should you produce? Who should you sell it to? How you should use it? Those are all types of questions that we can help with, and we’re seeing customers digitize those processes, and trying to apply AI to them as much as possible to make better decisions and to really do things faster in such a changing environment.
Yeah, that’s interesting. You talk about technology, I think about the supply guys who would run things like, an LP model, linear programming. Yet, I don’t know if we talk about that anymore.
Is that a predecessor to AI? Is it part of AI? Where does something like that fit in?
Alan: Yeah, in my whole career, probably everything I’ve done up until now, today we would call AI. It’s such an umbrella term for lots of things. In my mind it’s mathematics. You start from incredibly simple formulas, linear programs, or just basic mathematics you’d learn at primary school in the UK. All the way up to the modern version of what you’d call generative AI these days, which would be much more complex and I suppose difficult mathematics.
Alan: I think the difference these days is we have so much more data available to pump into those models. Also the compute power is a lot cheaper. So often people will give you the kind of adage that when I was running an Atari for my games console back in the day, my phone has 10 times more processing power than that, or a thousand times. And I just put it in my pocket. So you just have a lot more compute power and a lot more data and better mathematics to solve those problems these days.
Chad: Yeah, and it all builds, right? It’s not like AI emerged overnight and everything was a whole brand new, so all those techniques we would have looked at any kind of data analytics, historically all that mathematics stuff. That is the core of what fed into AI. A lot of the core things around AI and how it works are actually very old ideas, but the technology in terms of the compute power and all of that just needs to catch up. Once it became powerful enough and the cloud computing revolution happened, all of a sudden we could start just throwing tons and tons of processing power at solving problems. That’s over the years become more and more powerful to the level you’re now seeing with generative AI and really getting into what people have always thought of as being artificial intelligence.
Yeah, and I think when I first heard the term artificial intelligence, which was probably 20 or more years ago, I was like, what the holy heck are you talking about? And people still are saying, what the holy heck are you talking about, are machines taking over the world? So I think we’ll get into that cause that falls into some of the risks maybe later, but when you think about, what’s going on with technology, what are your clients wanting and how are they engaging?
Because I think of ICIS as a data company, at least that’s my lens on it is that there’s always this wealth of data that clients go to you, I go to you, other people go to you for data and information. So what’s different today in terms of what your clients are asking?
Alan: Yeah, I think if I start under that umbrella of AI, what you’d term as more traditional AI use cases. I actually run the strategy and work with customers every day looking at data solutions as my job title. And what does that mean? That kind of means customers that are not comfortable or it doesn’t meet their needs just to read content from our website. They want it in bulk and they want to feed it into systems and they want to run their own AI models and things like that from it. What we see these days is that’s becoming more common that customers in the industry want to do that. Probably the main driving reasons are we’re needing to make decisions more quickly because the environment around us is changing.
Alan: To do that manually these days is just not sufficient. You just can’t run that in a spreadsheet anymore. People want to be more efficient. As I say with the competitors in the industry, everybody’s looking for marginal gains in terms of shaving time off of processing and running their operational processes and with the data available, they can now make smart decisions as well. So they want to run smarter algorithms and against our data and their own. That’s really what we’re seeing, that customers are pushing forward and those solutions are becoming more popular. I think Chad can give you a good answer as well about the more generative use cases and what we’re doing there as well.
Chad: Yeah, I think that’s a different space. So I think the perception of us as a data provider. That’s true. We certainly are a data provider and we do create that. But we do see ourselves very much in an analytic space. We do some pretty cool stuff there. The Gen AI, I think is a slightly different beast. I think the explosion of ChatGPT and what that actually means, because it’s really going into the whole thing of looking at individual productivity. And in some ways it’s quite difficult, because it can do so much, but where do you get started?
Chad: So we’re seeing different levels with our customers in terms of, we have some who are quite savvy with it and get it very quickly and you can see them immediately saying, I use ChatGPT to do this. I’m using Ask ICIS to do all of these tasks. And what they’re getting out of it is a loss of productivity. It means they spend less time doing the jobs than they’re doing before. In other areas, people are still trying to get their heads around it. The difference between using a chatbot based solution versus using a traditional search where they’re still just trying to type in a keyword and just trying to get results. But it is evolving to that space of asking questions and trying to have a conversation an artificial person, if you want. But those who can really get it. They can really get advantage and there’s a lot there about the skill set of actually asking good questions. Which is something that everyone really has to start learning in the world of the AI revolution, as I like to say.
Absolutely. That whole aspect of prompt engineering, right? Figuring out what questions to ask, and then to keep asking. I know myself just from my use of ChatGPT over the past 2 years, and how it’s evolved. I can get to the types of answers I’m looking for rather quickly because I know exactly how to start asking the questions, how to amend those questions, how to keep driving it to a solution, but the first question you ask is usually, just like in real life frankly, it’s probably not the right question.
You have to keep asking questions.
Chad: Yeah, absolutely. That’s very much the case of advanced users with looking at using AI technology. They’ll ask a question and if it doesn’t give you the answer to the one, you just go and reframe your question a bit, knowing that it’s smart and it has this amazing amount of knowledge, but it’s not always that smart and maybe it just needs a little more context and help from you to get the answer you want.
So let’s talk a little bit about Ask ICIS. So I know that you’ve recently rolled out this, Ask iCIS, which I guess is an AI based tool, to really interrogate the data and get answers. So can you talk about that and how you guys are using it?
Chad: Yeah, Ask ICIS is our generative AI assistant, as we call it, that specializes in the chemicals and the energy sector. Like a ChatGPT, you can ask it questions and it will answer those questions for you. Unlike ChatGPT, it’s not just a general purpose tool. It’s not there for answering every question. It’s focused specifically in the markets and it’s built on the data and content that we’re taking and create with ICIS. So when you ask a question like, what’s happening in the styrene market? It understands more context about what styrene is and what you’ve used it for and it’s pulling content from our price assessments or forecasts or from our news to help answer those questions. And it gives you a much more powerful answer as a result.
So what is it? What are the use cases? How are you seeing your clients using it and really getting value?
Chad: So I think the big thing there is people are looking at a really key time saver. They can get the job done a lot quicker than, just going and doing the work themselves. So we have some people who are saying that, they go and use it and they can save hours of work. It would normally take them hours to go look at our content, look at other content elsewhere, and ask that, and now they can just have a single session with Ask ICIS, ask some of those questions and get what they need, and it accesses all the content we have around market dynamics and companies, topics, events, supply and demand fundamentals.
I’m assuming it’s available only to people that are already subscribing to your services and data. Is that right?
Alan: Yeah, that’s correct. I think that the important thing that we do with Ask ICIS that, something like Bard from Google and some of the other Gen AI tools don’t do, is originally they didn’t tell you what information they were using to give you the answer. In effect, they would just say, hey, here’s the truth. And a lot of the time it wasn’t right. It was a lie. It was the AI getting it wrong. And what we’ve done from the beginning and some of the learnings we’ve taken from our sister companies across RELX was ground the answers in the articles that we already have.
Alan: We’ll give you an answer and say, “Hey, you’ve asked about styrene in this market. Here’s what we think is going on and here’s the relevant articles that we’ve used to create this response.” Then you can click through if you subscribe to those and see. And if you don’t, you can talk to us about if you want to get a subscription to those articles. So trust for us is a really big thing in using AI, and I know we’ll go on to talk about that. But I think I would never trust the answers if I didn’t know where they came from.
Chad: And that’s really important. When we were developing Ask ICIS, that was really the heart of where we started, which is, our customers really value the content we do. They know they can trust us. They know we do our research, we do know what we’re doing, and the tool must do the same in that sense. It’s very much built into the actual design to make sure that we know that if you see it, it’s something that somebody in ICIS has said. Because trust is the absolute most important thing, and when you just use a ChatGPT or looking at that, they talk very confidently when you use these services, and that always gives you the suggestion that it’s always right, but it’s not. It’s very hard to tell when it’s right or not. If you can’t go and say, can I just look where that content came from to see if that’s correct.
I really appreciate that as well, especially the referencing back to the specific articles, because as we know, things change over time. So when you get an answer and say, “Oh, the answer is X.” Okay. The answer X was correct back in January. But these other three big things happened and now the answer is Y. So understanding and being able to trace some of that I’m sure is super important to your clients and really to everyone.
When we ask a question, and somebody is coming in to Ask ICIS and saying, I’m asking this question because I have to go talk to my president, the board, the boss, a customer, whatever, you want to go back to that individual with confidence.
Alan: And I think that’s a common requirement for any analytics or AI back from before people talked about generative AI. We got some great data science teams working on price forecasts as well as what we do with price assessments and one of the key things that we work on with customers is explainability of those analytics because as you say if you’re using a third party to create them. If your director or CFO or CMO asks you a question about well, how did that work? That’s a strange number. I don’t understand it.
Alan:: You need to be able to explain that to them. If you can’t, you lose trust immediately. And I think no AI tool is really going to be used by customers or any users if it can’t explain itself. It’s very important.
Yeah, that’s great. And that’s actually a great segue. Let’s talk a little bit about maybe some of the concerns and the risks that we think about as we think about technology and AI and how those are being mitigated.
Chad: The big thing that people worry about is hallucinations, right? It’s the phrase everyone’s using. It’s a bit of a disservice in some cases to the machine. It says it’s just hallucinating. They’re actually making very, educated guesses. That’s how they actually work. But the one thing that people worry about a lot is if it goes and tells you something, can you actually handle that? Can it actually manage that hallucination? Yeah, and that’s something as far as how we took and built it in a pattern, it’s fairly common in the market to make sure that when we train Ask ICIS we tell it that it prioritizes our view.
Chad: It prioritizes our content over any other view. So it’s always pulling from our content first, which we make accessible to it, and effectively does a search and pulls back the information and then it tries to answer the question that customer has based on that information. It looks and it’s not drawing on as much the LLM to answer that question, it’s using its ability to read and its ability to reason to help answer that question. But the data it’s pulling from is ICIS content.
Got it. So it’s a little bit less about generative, right? I think this aspect of generative being creative and creating answers that don’t exist, but rather generating and then testing back to what is already known to be true.
Chad: So it’s generative and it’s creative in the sense of it makes content that’s more specific to what you’re trying to find out. But it could be made out of six pieces of our content. So it could have pulled it from three news articles, a price report and forecast because you are asking about something that hit all of those areas. Then it creates a unique piece of content for that particular answer, but it’s all based on our content.
Yeah, that’s helpful. What are the other risks that you see or concerns that people raise?
Chad: I guess one of the other ones that comes across a lot is the, is AI going to take my job? That’s the big one that most people say.
Yeah, do we still need the ICIS consultants if we have this?
Chad: Certainly, would be my answer. And I think you need to look at AI and the tools there. They make it easier to do your job or to do somebody’s job with that, but it’s not just a like for like replacement. Our content creators in our business, they answer very hard questions. They’re looking at very complicated situations and apply subject matter expertise. Most of that’s not in the question that’s being asked. So when we actually look at the content, we’re looking at these things, There’s a lot more there to add. It’s fine to say that as a piece of information, a tornado wiped out interstate 95 when Hurricane Milton was hitting Florida. That’s great. It’s more important to know that’s a major transport hub to the port of Tampa, right? Which actually potentially is going to affect some customers there. So that’s hard to figure out.
Alan: I think as well with any AI, not just the more modern generative AI. A lot of the cases are only as good as what goes into it in the first place from an information perspective. So we’re in a privileged place in ICIS that we have a lot of good information that we can feed into these things and actually make them more intelligent to help you make a decision. I think as well, like with not just Ask ICIS, but the tools that we create, also internally how we use AI, a lot of it is around in the industry.
Alan: Every industry has this challenge, how do you scale up your workforce? And how do you make them more literate to use these kind of tools? Both from a benefit perspective, as Chad was talking about, from making you more efficient, faster doing your job and so on. But also the risks and pitfalls. Very early on, because we’re at heart data analysis company in a group in general, we had guidance around employees using generative AI tools. Don’t put confidential information in ChatGPT. It’s just going to harvest it and start spitting out to other people and I’m sure a lot of companies, their employees are doing that and they don’t realize. Making sure that you get good training out to your employees, that you teach them what these tools can do, why they’re good, why they’re bad, or what are the risks around them is very important.
Alan: And we have to always keep up to date with the latest and greatest new tools that are available to do that. Next year there’ll be another ChatGPT-like thing that will come out that we’ve got to make sure that we use it in the right way. I like to think of AI analytics tools like a builders toolkit. Someone gives you like a brand new drill, you don’t just try and do something with it straight away. You read the manual, right? Try to make it, you should do that.
Chad: Or you drill a few holes in the walls and see what happens.
Hey, maybe.
Alan: I’m that guy who reads the manual. But I think it’s important that, they’re fascinating. They’re brilliant. They’re important tools and Chad and I will often geek out on, it’s a very exciting time to be in business to use these tools in your personal life as well, but you should make sure that you do a little bit of due diligence to understand which tool to use in what way, and also there’s a big risk that you run up with lots of costs. Some of these tools can be quite expensive. My mantra has always been in the AI space, if you can write a linear program to solve a problem to a level of accuracy that’s acceptable, why would you deploy a neural network? It’s 20 times more expensive. So having that cost benefit analysis, when you’re deploying these tools is important from a business point of view as well.
Yeah. Especially when you think about the broader social context of it is, the power usage that goes along with AI as you say these sophisticated neural networks. I don’t need to ask it how to make a peanut butter and jelly sandwich because I can get the answer anywhere else. But asking it things that I can’t find the answers to becomes critical so that I’m using the resources wisely
Alan: Yeah. A hundred percent.
You talk a little bit about training, do you guys provide training to your customers when they start using Ask ICIS? Because obviously you want them to use it effectively and get the benefit.
Alan: Yeah, absolutely. We have a customer service team or customer success team, which is not just for Ask ICIS, but they’re available to our customers for any product that we ship as part of onboarding and general queries. If anybody has any questions, we can do that. Interestingly, there’s a use case for those types of algorithms that you can ask them how to use them as well.
That’s actually really good. I hadn’t thought about that.
Chad: I think it’s something that as we develop it, it’s always about making it easier and easier. I think the thing with a lot with these chatbots is trying to make them as simple as possible. Like it is just a box, you type in a question and you try to have a conversation with it. But there’s always elements of, how can we make it easier? How do we help people to onboard? And that’s something we’re always looking at and we can evolve the tool and how we approach things, of course, too.
Love it. So what’s next guys? Ask ICIS is the big thing for 2024 and obviously continuing to roll it out into 2025, which is when this episode is going to be getting published. What else? What’s next? What should we be looking for from your team and from ICIS?
Alan: I think more innovation is what we want to promise to our customer base and ourselves, that we’re going to keep trying to push the boundaries of what’s possible and what’s sensible in our space as well. And I think whilst my head of product would kill me if I said anything specific. From my perspective, that’s more investment in some of these tools, Ask ICIS, growing its capability, but also in my space new analytics for customers to use to help them make decisions across our industries, but also keeping up to speed with developments in the software and analytics market if they want to take data directly from us as well. We’ve done some investments on that recently as well. We need to make sure those tools are up to date when our customers come asking us to help them.
Alan: So yeah, keep watching us. We’ll try and post as much as we can on socials and things like that personally and as a group. And yeah, I can’t be more specific Chad if you want to be braver than me…
Chad: I think it’s as always, with all generative AI tools that people use today, it’s not always going to be about specific features or things you’re looking at, but it’s just, how do you make the experience better? It’s a lot of fine tuning of how things work or behave and respond and looking at particular use cases on how does it answer particular kinds of questions or things like that. So it’s it’s hard to say exactly what’s there because we’re looking at feedback from customers and seeing what’s important. That’s what we’re looking at and that’s what drives.
Alan: I think what I would say is, if you want an advanced look at any of the stuff we’re developing at the moment, we have a custom advisory panel. You can apply to it and we’ll be testing tool prototypes and things like that with those customers.
How does somebody get on that panel? Is there a website or an email or can I just how do we direct people to you?
Alan: Yeah, we can supply that. I don’t know if they’re still recruiting people, but I can give you that information.
Awesome. And for sure, we’re going to include a link to Ask ICIS. And I know if people are already ICIS customers, they can probably talk to their salesperson, the relationship focal point to learn more. If nothing else, as we head into 2025, learning how to use AI effectively, including tools like Ask ICIS becomes a real differentiator for companies and for individuals.
Chad: Yeah, absolutely. I want people working in this industry to think about, how can we use this technology to help improve our productivity? How can we help our individual people get more time to do the things that add value to your business and less time doing some mundane tasks. That’s where these tools are very powerful to get people to focus on the stuff they tend to like doing more as well.
Alan: We honestly feel like these types of tools and us as a partner with our customers, we want to augment what they do. We want to make them better. Ask ICIS as a name, think of us as basically, you’re the superhero in our story and we want to be a sidekick. We want to help you, we want to help you get done what you need to do, and we want to make you look great to your boss. That’s effectively what we would like.
And that is a great way to end. Alan and Chad, thank you so much for your time today. This has been great.
Alan: It’s a pleasure.
Chad: Thank you very much.
Absolutely. And thank you everyone for listening. Keep listening, keep following, keep sharing, and we will talk with you again soon.
About Alan and Chad
Alan Spanos
Alan Spanos is the Director of Data Solutions at ICIS. With a strong background in the aviation industry, he brings expertise in Data & Analytics Org Design, Product & Program Management, Data Architecture, and Executive Communications. Alan holds an MEng in Aerospace Engineering from the University of Cambridge.
Chad Macey
Chad Macey is a seasoned technology leader with nearly two decades of experience at Reed Business Information, where he currently serves as Senior Director of Technology – Architecture, a position he has held since July 2018. Throughout his career at Reed Business Information, he has progressed through increasingly senior roles, including Principal Architect (2014-2018) and Senior Solutions Architect (2012-2014).
Prior to joining Reed Business Information, Chad worked at several technology companies, including Zynap Ltd. as a Senior Software Engineer/Technical Lead (2003-2006) and Direct Image Development & Consultancy, where he advanced from Junior Developer to Senior Developer (1999-2003). He began his career as a Developer at Financial Times Information (1998-1999).
Chad holds a Computer Analyst Programmer degree in Computer Science from RRC Polytech. Most notably, Chad was instrumental in leading the technical development of the award-winning Liveflo product for XpertHR.