Mark Fasken: All right. So this is our first episode where we've had multiple guests, which is exciting and a little bit different.
AI's Impact on Investor Relations
Mark Fasken: And of course, on this episode, we're going to be talking about AI and its impact on investor relations. As anybody who's listening to this podcast knows investor relations is always evolving.
Over the last year or so, it's felt like everybody is talking about AI. It's a topic of conversation in a lot of conferences and panels. We've got the AI summit. The Wall Street Journal recently wrote an article covering AI in investor relations. And so I want to start with maybe a bit of a broader question, and Krishna, we can start with you, focused on understanding how AI is impacting investor relations.
From your perspective, how is AI shaping the IR function right now, and where do you think it is having the greatest impact?
Krishna Rangarajan: Well, first of all, Mark, that's a great question. And we get this question a lot when we speak with our customers and prospects. One of the things you know, I'm coming at it from the perspective of a technologist and not an IR practitioner. And one of the things that struck me very early on as I engaged with the IR teams, is how under-resourced they are. You know, if you look at other functions, a tremendous amount of technology, and people and process, so on. And in the IR case, it's often a couple of people who have to bring along a disparate group of people across the organization four times a year, maybe more if you have an analyst day, and other events organized.
So, I think one of AI's biggest impacts is bringing everybody on a common platform and addressing the resource problem that IR professionals have in their ability to synthesize and grasp a lot of content and information in very dynamic markets.
Businesses are facing many disruptive trends, thematic trends, and so on. The world is changing very rapidly. So how do you effectively message into the marketplace when you are in the midst of all these crosscurrents? The buy side is also increasingly adopting AI, and they're getting smarter on a lot of topics.
So, I think it's great when we initially meet companies. Chat GPT gives them an on-ramp to personally experience AI, but very quickly, the conversation evolves into addressing some of these bigger-picture challenges and opportunities that IR professionals face.
Mark Fasken: That's great. Thank you, Krishna. Fatma. Is there something that you wanted to add to that?
Fatma Sardina: Oh, yes, please. I actually believe that the fast development of the IR professional, the main driver there is the changing of the type of audience they have, the type of audience they communicate with, which is now a lot of the young investors, who they rely on machine led algorithm trading, or they are more of a technology advanced data analytics generations.
So the communication language of the IR professional has to go with the same pace of this audience. Remember a long time ago, when the ESG topic was still like a baby topic and then, you know, everyone would ask in the investor calls about it. And then the IR people still feel, well, it's still, you know, it's still such a young of age kind of topic.
Over time, we had to pick up and catch up the pace and just, you know, more and more of ESG. The same with AI. I mean, IR professionals have to incorporate this in their communication with their investors, especially the young ones, understand how these machine trades behind the scenes, and communicate with these types of investors.
Mark Fasken: Absolutely. I love that analogy of ESG, because, to your point, I think it's very similar. I remember the early days of people talking about ESG and saying, we don't know what to do about ESG. It's such a big topic. We're not sure where to start. And then the talk track just became, well, you need to start somewhere, right?
What are the quick wins that you can find? Where can you dip your toe in, right? This is a marathon, not a sprint. Gradually, everybody figured it out. And now I feel like it's not as much a topic of conversation because people have found this steady state of, you know, we're reporting on ESG. It's not a big deal. And there's not as much stress surrounding it anymore.
Fatma Sardina: Exactly. There's a more established foundation now to build on, for sure.
Leveraging AI Tools in IR
Mark Fasken: Well with that, Gregg, I want to shift over to you because Fatma gave us a good opportunity to talk a little bit about what are some of the things that people are doing.
And so, I know you've been experimenting with AI for quite a while. You were saying it's been probably over a year that you've been leveraging AI tools for a variety of different use cases. What are some of the tools that you're exploring or using right now?
Gregg Lampf: Yeah, specifically from a generative AI perspective. We have been experiencing many, many benefits—efficiencies, drawing greater insights and really providing a lot of nuanced information. And the tools that we're using now, well, to say, first, for those who have not done much with generative AI, as Krishna is saying there's lots of free tools out there with which you can try some things.
Now, of course, you don't want to put any material non public information in any of those tools, MNPI, so I just need to, of course, state that. But these tools are very accessible to start, to dip your toe in, and go from there. With us, at Ciena, we're we have a team and we're a company that's very progressive here, and we've been embracing AI quite a bit. We've got lots of use cases under evaluation and underway.
Right now, what we use in this context is what we call Ciena GPT. It's secure and basically an enterprise version of Chat GPT. So I can put MNPI into this tool, and that just frees me to do all kinds of things with earnings prep, for example, which is really a use case that we've been leaning increasingly into over the last couple of quarters.
But, you know, I would say, to start, use one of these free tools and summarize some dense document like one of the latest SEC rulings on climate, just to touch on ESG, which is something like 885 pages, right? No one has time, unless you're paid to, to read 885 pages. Well, throw it up into a chat GPT or Gemini or Claude or any of these other ones.
And see how it summarizes it from your perspective as an IRO. Please summarize this document. That's a great way to start.
Mark Fasken: Okay, great. And we'll get to a question a little bit later, I know there are some large companies that have basically built AI-focused teams of people that are just looking at ways to leverage AI. I mean, there's obviously a lot of IROs that they don't have those resources available to them. And so maybe later on, we'll want to talk about, what are some places where you can dip your toe in?
But so you're leveraging Chat GPT right now, an enterprise license. What are some of the actual processes or things that you've done to improve efficiencies and streamline workflows? You talked about earnings prep. Can we talk a little bit more specifically about what that means?
What are you using Chat GPT to do to make that process easier for you?
Gregg Lampf: Sure. Well, you know, it starts with, we're an October year company, we follow as many, I'm sure who listening follow many companies, many peers during their earning seasons. It's for us, it's about 35 companies. And obviously, we get a lot of information as we're starting to think about our call, for example, and our disclosures.
And so, from an SME perspective, subject matter expertise perspective, I use one tool that's gen AI-enabled that helps us much more quickly get to some of these conclusions. So I should say that first, that's an external platform that we use. It aggregates any public company's disclosures, and you can interact with it using a generative AI tool.
Once we, as subject matter experts, summarize these various companies results, we will put these into an LLM like Ciena GPT, and say across all of these peers we’re basically breaking it down by customer type. Tell me what what are the common themes? What are the insights? You know, what did they focus on? Particularly as it might impact my company, the questions I might get. So to start to get us to think about where the focus is of these different audiences as they listen to us is very helpful.
And, you know, it's, as I say, often, as subject matter experts, it's rare that you get an aha moment.
In fact, I don't know that I've gotten an aha moment, but I get a lot of nuanced information that informs how I think about things. And when you apply generative AI, for example, across 35 companies when you're preparing for your own earnings, it really, to Krishna's early point, allows you to synthesize things a lot more quickly, and from, frankly, different perspectives as well, which is a really interesting way of using these tools.
Mark Fasken: Amazing. And so, I like this one because it's something that probably everybody that's listening to this episode does, right? I mean, every IRO is summarizing, whether it's a quarterly earnings call across a bunch of peers, looking at those, reading through them, summarizing them for the management team, identifying trends.
So basically you were doing that manually beforehand. Is that correct? You had or people on your team were manually working through those documents, and creating these reports?
Gregg Lampf: That's right. We have a distribution internally, even goes to some external folks, consultants, et cetera, that just does a very quick summary.
It's really just actionable information that we try to get into the hands of salespeople and all kinds of people based on what that company may have said so that they're prepared for incoming. Now, I think what's very important as we continue to think about our journey here is that we know AI agents and automation are coming and increasing.
But I think it's important. You asked a part of your question that really resonates with me. The human element here is very important. You need to apply your own expertise to this information, maybe even before you use AI, so you're not automating yourself out of any knowledge. So you have to be mindful of that.
These are very strong assistants right now, and they're going to only get better. But I, at least right now, believe that humans need to be a big part of this process.
Mark Fasken: And so, Krishna, I want to switch over to you because I know you've worked with several different teams that are tackling all sorts of complex IR processes.
Can you share an example or two of how AI is simplifying some of these workflows?I would say freeing up time for IROs to focus on more strategic tasks.
Krishna Rangarajan: It's for sure, you know, simplifying tasks. It's also about saving time. But I think there is potentially another dimension to this, which is in my view, transformative. And this is something we ask our clients all the time, and companies we meet, is what are your constraints? You know, and going back to my earlier point about IR teams being under-resourced, what if you had unlimited resources, how would you arrange or organize your work, right?
Another question is, what if you could have your CEO in the room while you're writing the script? How would that change how you approach the topic? Finally, the investor feedback as well. You know, a lot of information is collected in CRM systems and so on. And how about if we project that into your writing and your communications process, how would you change it?
So, when you ask these questions, you think of the earnings process as three stages. One is the research stage, the second is the writing stage, and finally, the retrospect stage. And across all three of these stages—and by the way, these are not perfectly linear—we find tremendous applications for the use of AI.
And when you think of it this way, companies can go beyond the 101 use of AI, which is the elementary use of AI around summarization and asking one-off questions. But I think there's a potential here to add to Gregg's point, to add a virtual assistant. To augment your team, to be able to look at not just your product peers, when you think of your peer set, but think of the broader landscape in terms of being able to track what other companies are saying and understand changes in sentiment, changes in how the Street is looking at things.
And finally, from an investor perspective, we have a lot of rich interactions with investors, but many IROs I've seen, they tend to dismiss many of the questions as, "Oh, it's a modeling question", and look at it pejoratively. However, with the help of AI, you can truly understand the biases of the investors as they come to chat with you, and also understand what parts of your message are landing.
So we are basically, really innovating a number of applications across all stages, be it research, writing, or the retrospective in terms of seeing how your message is landing.
Mark Fasken: And so when you, maybe just to back up for a second, Krishna, when we talk about writing, I want to put that in the sort of day-to-day of an IRO. What are the most common types of writing that you see IROs using, you know, these solutions for? Are we talking about press releases? Are we talking about earnings calls? What does that process really look like in the clients that you're working with?
Krishna Rangarajan: Yeah. So great question. It's all of the above. It's in preparing scripts. It's in press releases.
It may also be a CEO going to an industry conference, or your line of business executive having a press interaction. Because we want to make sure that the company's message is being distilled and being communicated consistently across the board. And to bring Fatma's earlier point about the use of bots, and these quantitative models that are actually ingesting a lot of press releases and transcripts and so on. What we find is that the use of AI and writing is not so much that the biggest value is not in getting a first draft done. But it's more in terms of testing what you're writing.
You know, unfortunately, IR functions cannot have a focus group to see how's my message going to land? How's a bot going to interpret it? Is this going to create confusion? You know, the people in the marketing departments, they do these focus groups all the time. But however, with the help of digital agents, you can try to start simulating and mimicking how the messages could land. And we've seen a case where a company in the consumer space had one of the blowout quarters that they ever had in their 30 years of existence. But yet the stock dropped, and it wasn't because the expectations were way ahead of the market or something of that sort. But there were some technical aspects of how the press release was written was confusing, and the market misinterpreted the quality of earnings.
So you can get deeply technical and try to understand it. And decipher, you know how the message is, is it going to create confusion? Is it going to land properly? Is it saying what I intend to say? So it's more along the lines of quality assurance and testing, much like, you know, software developers. We put every piece of software through a testing process.
Mark Fasken: And so again, trying to, I always do this on these episodes, peel that back a little bit, right? So you're an IRO, and you've written up a script for your earnings call. Of course, Gregg made this disclaimer earlier on, you don't want to be uploading your earnings transcript to a free version of chat GPT or any of these AI solutions.
So, you know, you want to be really careful about what you're uploading that to, is what you're suggesting is basically taking that script that you've written, uploading it to an AI solution, and basically saying, can you analyze this and tell me whether there are areas that could be perceived as negative, or read this or analyze this from the perspective of a buy side analyst, what do you think are some of the areas that are going to be concerning?
Is that essentially what you're suggesting?
Krishna Rangarajan: Yeah, yeah, it's that's essentially, you know, what I'm trying to illustrate. Now, in order to get a very high fidelity, highly reliable feedback from these AI models, going into Chat GPT, and even if it is it's an enterprise version, and it's secure, and trying to simulate the outcomes, is unlikely to give you the level of fidelity that you want.
I think in order to do this, you need to build specific models that can actually do a much better job of predicting the responses to your writing.
Gregg Lampf: We're up to that point. We're creating, we're fine-tuning our enterprise version for IR so that it has that context. And I'm looking forward to using it.
And that we most recently been using these tools precisely as Krishna has been saying, and it's been very helpful to make sure we understand all these different perspectives, buy side, sell side, bull, bear, generalist, technologist, right? When you can introduce those different perspectives and make sure that how you're communicating to Krishna's point, lands. You can't land it for everybody from all these different perspectives, but you can get a better understanding of how they're going to perceive it, and what you might need to do in Q and A or follow ups to make sure to ensure that you're getting that message across effectively to a wider group of folks.
Krishna Rangarajan: Right. And Gregg, that's a great point. And I think in terms of the value to companies, right? It's not just about the outcome, right? It's also about the process. And time and again we hear from IR saying, you know, my colleagues don't get it. You know, I'm in touch with the market. I know that this is not how you want to say it.
With AI, IR can now have quantitative evidence and tools, a methodology, and a framework that they can leverage to make that case internally as well. So, I think that from a process standpoint, there are significant benefits in terms of being more data-driven and quantitative, much like any other function, such as finance or sales, etcetera.
Everybody is working off detailed models, quantitative tools, decision making frameworks, and so on. So why should IR be any different, right? And I think AI provides a solution, you know, to this to this gap in the tools that IR has.
Mark Fasken: Absolutely. And so I think just summarizing that I mean, there's a bunch of different ideas that you threw out there, but I think this idea of leveraging AI to help with your writing and really use it, I think you used the term of using it like a focus group, right? To improve your decision-making. So it's not just about writing a first draft for you, or it's not just about saving time, although there are definitely time-saving opportunities, it's also about helping you make more informed decisions, leveraging some of these tools.
And so, I think that those are some great concepts and there are just a couple of others that I've heard from IROs of different use cases, similar to one that Gregg, you mentioned. I've heard a few IROs say that they're leveraging AI to summarize research notes as well. So taking in all the different research notes that are being put together by analysts, and putting together a summary email.
I've talked to many IROs who say we have 20 analysts covering us. Reading all the research notes and summarizing those for our management team is a very time-intensive process, and so we're leveraging AI to streamline that workflow. I've heard a number of people, Krishna mentioned, using AI to help with the first draft of their earnings call.
But again, I think a lot of these are the drafting part, and it sounds like step two in your mind is going to actually helping IROs make more informed decisions, which is really interesting.
AI in Financial Forecasting
Mark Fasken: So I want to shift over to talk a little bit about financial forecasting and Fatma come back to you.
Because I think an area and Krishna just mentioned this, an area where AI is definitely making an impact is in financial forecasting and the building of models and analysis. How do you see AI being used to better support decision making in the financial forecasting world? And can you share some examples of how this could actually work in practice?
Fatma Sardina: So there are two things I want to explain to our audience here. There is data analytics, advanced data analytics, where as an IRO, you work on the old, the historical, in-house data to give some visibility or some valuable insights to the C suite. But there's also the machine learning predictive side of using models to predict how things will be in the future. So these are two different things.
So from the financial forecast perspective, in the last IR summit we had in the DFW chapter, we hosted, I believe, a very fascinating quantitative model. We hosted Tim from Modern IR, and Brian Johnson, and they built a model to explain the participation and the contribution of the passive investments, post earnings.
Earnings is always going to be a big thing for any IR professional. So, there's a lot of machine-led algorithm trading out there. Their quantitative model shows you how much exactly the contribution in the change in your stock return, and movement post-earnings made by this machine-led algorithm.
Now what I did, I really liked the model, and what I did was I built my own model internally where I grabbed the volatility for my company's stock for the last eight earning sessions, before and after, the average movement one day before, and one day after, three days before, and three days after. The key there is you want to make sure that you're not that high volatility because this machine-led algorithm, if you're very volatile, they kick you out of that, their system. So they kick you out of the package, right?
Their model shows you that you want to measure your volatility. Based on that, the way you draft your earning release, specific keywords have to be fed to these machines in order not to affect your post-earnings movement, right?
Again, you're talking about technology, how investors are now relying on advanced technology. This is one way I really use advanced analytics to help my company see how we're changing before and after earnings and how much this is happening because of machine-led algorithms. Right?
Now, the other model that I'm trying to build these days I'm working on, which is for forecasting. So there are two of them. One of them is we want to measure the key driver analysis. What exactly is the main key driver that affects your stock return? Either weekly or quarterly or monthly.
So you have revenue growth. You have earnings per share. You have trading volume. You have volatility. You even have sentiment scores that you can scrape over the news and the Internet for your stock. And, for my company, because they are in the auto industry, I use the vehicle index change. You can use interest rates, quantify macroeconomics.
It's a big model. But again, it shows you which one has the biggest impact on the change in price or the change in volume of your stocks, right?
The other thing for IR, which can be very helpful, is that all this can be done internally. And I know again, AI is such a really, some companies are at an early stage, whether to incorporate it or not.
Not all companies will be willing to jump into it and put a budget into, you know, okay, let's buy this and let's buy that. You would want your IR team to start experimenting on their own things first. So one of the things we use for the financial forecast, for example, I built a cash flow model forecasting the next five years for our company in Germany.
What helped a lot is, these all work. I'm trying to make it as automated as possible, so all this can automate reports and dashboards for the C suite just letting them know every month when something changed, how would that affect the next five years?
And there are plenty of simple models. You can start from a regression model, to a more gradient model like the XGBoost. Again, it's more on machine learning. But what you do is split your data into 80 percent the historical one you have for your company now, and for the market and you train it. Then, you will use this to experiment with the 20 percent remaining of your data. It's fun, but it gives a different kind of visibility of your company to see what we can do now and what we can change in the future.
Mark Fasken: So, the summary, I think, is that everybody needs a Fatma on their team to support investor relations, and then this will be much easier.
And so, I think this is again when we're talking about, I'm just going to make this up. Level one, intro to AI, level two I think Fatma, you're at level two, level three, probably in terms of the complexity of how you're using AI. And I mean, some amazing examples. There's so many things that we can cover here.
Ethics and Governance in AI
Mark Fasken: One of the things that I do want to talk about is ethics and governance. I mean, because, you know, you're talking about all these different data sets that can be ingested into these solutions. And, you know, we're talking about transcripts and all the very sensitive data that we need to make sure we're handling properly.
When we think about governance, investor relations, and AI, what are some of the key considerations that IR teams should keep in mind as they're diving into some of these solutions? Maybe we can start with you and see if anybody else has anything they want to add.
Fatma Sardina: I think there is a lot of, I mean, the fear not to use it I think will hinder us more from using it and finding the right way to do it. So, from the compliance perspective, from the ethics perspective, I think everyone who's using AI in the industry now is working on putting a policy, or what's the best way to do this without exposing your data and your company. Again, for the investor relations team at early stage, they can start doing this on the internal basis. And then later on, they can start expanding different tools that is maybe public. But when it comes to using your data, you have to be very, very careful because it's going to be a big deal. You know, it can bite you later for sure. But there's so many people in the industry now where you can reach out and ask about the reliability of these platforms, and the ethics perspective or the security perspective of that too.
Mark Fasken: Great. Okay. Krishna or Gregg, anything you wanted to add on that?
Krishna Rangarajan: Yeah. Mark, I think one of the things that's important for everybody to understand is with, even a tool like chat GPT, as you're prompting these tools to provide outputs to you. You are in effect, right, programming your coding. It so happens it's your coding in the English language.
So you have to think of, when you put these processes or systems or tools in place to work with you, and for you to leverage, in the course of some very important activities, like representing what your CEO is saying. These are very high stakes type documents and content you're putting together.
It's important to recognize that you're writing a piece of code, and from a governance and risk management perspective, then it begs the question, how do you maintain and ensure the quality of it? How do you ensure it is debugged? So these are very, very basic hygiene practices in writing any piece of software.
So I think the audience needs to recognize, you know, people need to, IR professionals need to recognize that they're in effect coding. And, how do you bring in a dimension of quality control and testing to the code you're writing to get reliable outputs?
Mark Fasken: Gregg, I want to come back to you.
We're starting to end up where we started, which is with a little bit of a summary because, I think for a lot of people who probably have listened to this panel, we've given some really actionable things that they can do. We've talked a little bit about what the future might look like.
Getting Started with AI in IR
Mark Fasken: But you know, if you were, if you had a colleague, which you probably have had, you have a colleague who reaches out to you, or an IRO at another company and they say I'm looking to start dipping my toe into using AI, where should I start? What are some of the recommendations that you would give that person?
Gregg Lampf: Yeah. As we all know, it's stated in different ways, it's early days, but at the same time, keep in mind that there's been a recent survey that 40 to 45 percent of investor relations officers are using AI now in some manner, and that might be very simplistically, but they're using it.
So, you know, my argument to start would be, a year ago or so, we're having conversations like this. It was truly very early, and you could be forgiven for lack of a better word for not doing things. But I do believe at some point in time in the not-too-distant future when an investor asked a question, for example, like, "Are you using AI?" And the answer is no, they're going to ask you, well, why not? So we're getting there, and we should consider that.
Now, how do you start, you know, there's, you look at it, if someone's totally concerned about and hesitant about doing something with their professional work, start personal. That's how I started. You're going on a long weekend vacation with your family. You want to draw up an itinerary based on their ages. How much time do they need? Naps, whatever. That's a very benign, easy way to try these generative AI tools. I've used it for exercise routines, right?
So if you want to go very free, completely safe. Do things like that, using the free tools. And if you want to do something that's more sophisticated, or is using information like MNPI if you can get a secure version, then you start summarizing some of the information that you're preparing.
One thing you could do to, for example, using an external tool that kind of maybe blends the two, is take your latest public investor relations presentation, put it up into one of these LLMs and ask it for recommendations. Now there's lots of ways you can do that, but just very simplistically do that.
And you'll find, it'll come up with five or 10 suggestions, frankly, some of which will say, you know what, I don't want to do that. But ideation, these tools are tremendously helpful for ideation. And you can try something like that as a way to safely dip your toe in, if you will.
Mark Fasken: Absolutely. And I mean, I know that there's a number of people here at Irwin who I know use, whether it's chat GPT or other solutions to help with their writing, right? They'll write, say, an email or whatever, where they're working on a presentation and they'll ask Chat GPT then to read it and say, make suggestions. How do I make my message more concise? How do I make myself sound more confident? Those are, I think, interesting ways to your point that you can test those personally, and then that can go into your work life.
Fatma Sardina: Yes, I think for anyone who doesn't know where to start from, I think some companies, have different types of audiences.
Some have more global presence, so they will have more of European investors, or more of a local presence. But I think every investor relation professional has to start from, what is the one thing that takes the most time from me? What would be one area I would love to have more efficiency in?
Is it the market sentiment? Is i the peer analysis? I get a lot of questions comparing our company to our peers. What is the one area that takes the most of your time, and you would prefer to have some technological advances there to make it more efficient? I think, but then you should look for which tool you can use. Is it public, or is it should I pay for, I think this is where we can start just what, where exactly you can generate efficiency.
Mark Fasken: Yeah, absolutely. I think that idea, Krishna would know this, the problem to be solved or jobs to be done is a very common term used in software development, looking at it from that perspective.
What is the job to be done as an IRO? What is the thing I need to get complete? You know, Gregg, you talked about, the job I need to get done is I need to summarize all my peers earnings calls. Okay great. That takes me 10 hours. If I can turn that into 10 minutes or 30 minutes, that's a huge win.
And the thing that I wanted to ask about, and Krishna, this is maybe pointed at you, AI is again, a big topic of conversation. Do you think that it's a passing trend? Is it fundamentally reshaping IR for the long term? What do you, how do you see AI playing out over the next few years?
Krishna Rangarajan: Yeah. Mark, I come at it from the perspective of somebody who has had the good fortune of having lived through many of these waves. If you go back to the internet wave, there were very, very similar questions that were being asked. Is it a hype? Is it real? What do we use it for? Etcetera. And I found that the businesses who took the maximum advantage of these technologies were not trying to answer the question of is it a hype cycle or not, or where we are in the hype cycle.
They were looking at it purely from a business value perspective and saying, "what are the problems that this technology can solve?" And working backwards from it. And that's the point Fatma made as well. One question is, what are activities that take up enormous amounts of time that I would like to reduce the cycle time of?
Or, what if I had ten people in my group versus one? How would I do things differently? And I would encourage professionals in IR to ask those sorts of questions, and they will find the answers themselves. And what I think they will find is that it is a source of competitive advantage. AI is definitely going to help you tell a better story. It's going to help you bring your organization together on a more data-driven platform. And last but not least, you have your audience, which is the buy-siders using AI in various shapes and forms, and you're in an arms race. And IR professionals need to absolutely embrace these tools in their own ways.
Mark Fasken: Absolutely.
Conclusion and Final Thoughts
Mark Fasken: Well there have been so many great insights shared here, one thing that I think is amazing about the investor relations community is everybody is always willing to share what they're doing. Everybody's always willing to help. But I think for a lot of IROs that are listening to this. Myself included, right?
I think that all of us think, where do we start? Right. That's always the challenge. I think many people that are listening would also be surprised by the number of people probably in their own company who are already experimenting and are probably pretty advanced as it relates to AI.
It doesn't need to be a dedicated team of people that are the enterprise AI team. There may be somebody over in your finance organization who is just messing around with AI on their spare time, and can teach you how to do some of these things. So I think that's, the message as well is like, there are others in the IR community.
Fellow IROs who are messing around with this stuff. So Gregg's probably going to get a bunch of emails or phone calls after this asking for help, or Fatma or Krishna, that are willing to support. And I think it's really just about, asking for help if you're not sure where to start. So again, thank you all for your time.
Really appreciate this. It's been super helpful.
Fatma Sardina: Thank you so much Mark for having us. Thank you.
Mark Fasken: Thank you.
Gregg Lampf: Reach out to us. We're happy to help.
.png)