Are investors and analysts finding your IR content across AI search and answer engines? That was the core question we explored during our recent IR Impact webinar.
Together, we discussed how AI is reshaping how investment information is discovered, summarized and trusted - and what you must do to adapt.
Keep reading to learn more!
AI is no longer just a research assistant - it’s becoming an information intermediary.
AI-driven search tools and large language models are increasingly “the first reader of earnings releases, disclosures and IR websites,” shaping how information is interpreted before it ever reaches investors.
Erik Carlson, President and CEO at Notified, compared this shift to a fundamental change in how information is accessed:
“With the rise of answer engines, you’re sort of doing away with the library altogether. Essentially, what you’re doing is asking the librarian to go and research for you and come back with that answer.”
For IR teams, this raises the stakes. If AI systems pull inaccurate, outdated or inconsistent information, they can distort our financial narrative.
While SEO is about rankings and clicks, AEO is about answers.
As Muge Yucel, Director of Investor Relations and Sustainability at Galata Wind Enerji AS, explained: “SEO is about clicks. It’s about getting to your webpage. Whereas AEO basically is the content you want the direct answer to be present.”
At the same time, AEO doesn’t replace SEO. It builds on it.
“When you’re talking about optimizing for AEO, it’s yes, and,” Carlson said. “Continue to build on what you and your teams have been doing for 15-plus years.”
The key difference is that answer engines draw from a much broader set of sources, including the long tail of the internet.
“That content on the long tail of the internet actually matters more than ever before,” he noted.
When asked for immediate actions, the panel shared clear guidance.
Yucel recommended:
Carlson added:
“It’s about controlling what you can control,” Carlson said.
AI-driven discovery isn’t coming - it’s already here. Progress in this area does not require a complete overhaul, but small practical steps taken today.
And Carlson closed with a clear message for IR leaders:
“Making sure that answer engines are translating our financial narrative accurately is what this is all about.”
Gevitha Anbarasu: Hello everyone, and welcome to today’s IR Impact briefing in association with Notified. Our topic today is, are investors finding your IR content on AI? This is a crucial topic for IR leaders as they navigate how AI is redefining key processes for the future.
Before we kick off, I’d like to go over a few housekeeping points. We encourage you to send through questions for our panelists. This is a great opportunity to learn more about the topic and hear directly from our experts. Please use the chat function on the right side of your screen to submit your questions, and we will address them at the end of the session.
We will also be running a poll during today’s briefing. You’ll find this in the same chat function on the right side of your screen, so please keep an eye out for that.
For those who would like to watch this session later, a recording will be available on the IR Impact platform via our events page later today.
Before I introduce our panelists, let’s take a moment to frame today’s discussion. We’ll be exploring how artificial intelligence is reshaping the way investors, analysts, and markets access and interpret company information. As AI-driven search tools, algorithms, and large language models increasingly become the first reader of earnings releases, disclosures, and IR websites, the role of investor relations is entering a new phase of evolution.
With this shift comes a clear imperative for IR teams to adapt how they structure, frame, and distribute content. Corporate narratives must not only be compelling to human audiences, but also discoverable, accurate, and trusted by AI systems that increasingly influence investment decisions.
Today’s discussion will focus on several key themes, including how AI and answer engines are changing the way IR content is surfaced, indexed, and consumed, why answer engine optimization, or AEO, is becoming a critical capability for modern IR teams, practical ways to optimize earnings materials, disclosures, and press content for an AI-first audience, how to build a durable and discoverable equity story in AI-driven search environments, and steps IR teams can take to make their websites and communications more AI-friendly without compromising traditional SEO performance.
I’m looking forward to a timely and practical discussion, and I’m delighted to be joined by our expert panelists. Today, I’m joined by Muge Yucel, Director of Investor Relations and Sustainability at Galata Wind Energy, and Erik Carlson, President and CEO of Notified.
Muge Yucel: Hi everyone. My name is Muge. I’m based in Turkey and work at a renewable energy company called Galata Wind Energy. I serve as both the Investor Relations Officer and Sustainability Director. I also enjoy experimenting with AI, so I’m excited to share a few insights with you today.
Erik Carlson: Hello everyone. Erik Carlson here in New York. We’re clearly covering multiple regions today, which is great. As mentioned, I’m the CEO of Notified. We were recently acquired in May by a company called Equiniti, which is a leading transfer agent across the US and the UK.
For those who may not be familiar with Notified, we are the largest end-to-end provider of corporate communications technology and solutions across both investor relations and public relations. Sitting at the intersection of corporate communications, we’ve been deeply involved in the shift toward AEO and AI strategy over the past 12 months.
We are a global company serving around 10,000 customers across 80 countries. I’m very excited to be here today and to share what we’ve learned from our customers and interactions over the past year as it relates to AEO and maximizing communications and content for this new wave of AI-driven search.
Gevitha Anbarasu: Thank you, Erik. To set the tone for the conversation, I’d like to involve our audience through a poll. You’ll see this appear in the chat and on your screen now.
The question we’re asking today is: How does your team currently approach content discoverability in an AI-driven world?
The options are:
While we wait for responses, Erik and Muge, I think it’s important to define two key terms central to today’s discussion: SEO and AEO. Muge, could you briefly explain what these terms mean?
Muge Yucel: Well, when we talk about SEO, we basically talk about what we have done so far. It’s about clicks. It’s about getting to your web page, finding information that is on your webpage. Whereas AEO basically is about the content. You want the answer to be present directly. And that’s where we have to put important information and direct content onto the website so it’s immediately discoverable and can immediately be used to reply to whoever is searching for the answer.
Gevitha Anbarasu: Perfect. And Erik, how would you describe AEO and its function in your capacity?
Erik Carlson: Yeah, very similarly. Just to level set for everyone, this is a relatively new term. In fact, the industry has not even totally coalesced around AEO versus GEO. It’s really about how you surface information, to Muge’s point, in a way that delivers high intent and high outcomes for people asking high-intent questions of answer engines.
The reality is that the majority of search behavior is going to shift rapidly over the next two, three, and four years. We’ve already seen that last year, roughly 5% of searches were happening in answer engines instead of traditional search engines. Looking ahead to 2026, we estimate that number to be north of 25%.
The industry is shifting rapidly. It’s one of the more exciting times I can remember as a technologist supporting change, not just in this industry but in technology at large. Not since the advent of Google in 1998 has there been this significant a shift in how people access information.
If you go back to the 1980s and earlier, you went to the library to find books and source information. With Google and search engines, that process was digitized, but you were still essentially going through digital books in a digital library. With the rise of answer engines, you’re doing away with the library altogether. You’re asking the librarian to do the research for you and come back with the answer.
What we’re going to talk about today is how to make sure that librarian, or answer engine, is sourcing the right information from the library so that the message you want to tell for your brand shows up in an authentic way, aligned with how you want to represent your brand, your corporate story, and your financial narrative.
Gevitha Anbarasu: That’s a really interesting analogy. Let’s go back to the poll quickly to see what the results look like. I can see we’re still waiting for some responses to come through, but no problem. Let’s move on to the next question, which is how AEO is different from SEO and what the similarities are.
Erik Carlson: I’ll jump in with a quick take, and then I’d love for Muge to add to it. The first thing I’d say is that the strategy we’ve been using to optimize for SEO is not a throwaway strategy. When you’re optimizing for AEO, it’s really a “yes, and” approach. Continue building on what your teams have been doing for the past 15 or 20 years to optimize SEO.
The two major differences we see relate to the democratization of information. It’s no longer just about the first ten blue links on a Google search page. I can’t remember the last time I went to the second page of Google. It’s like searching through the bottom of your freezer for leftover pizza you haven’t reheated in over a year. People don’t do it.
With the rise of the answer engine economy, LLMs are much more open to the long tail of the internet. That’s both an opportunity and a risk. From an opportunity perspective, you don’t have to land in the top two, three, or four links to surface information. That’s important for smaller brands or those that haven’t historically optimized SEO.
The risk is that content on the long tail of the internet, whether it’s misinformation from an analyst perspective or opinions on Reddit, matters more than ever. As a result, the role of the corporate communicator and the IRO in commanding the financial narrative has become more challenging because a wider breadth of information is being surfaced in answer engines.
Muge, I don’t know if you agree or want to add to that.
Muge Yucel: Well, I think for IROs, this means we have to put more importance on format rather than flair. We need to move away from nuances and flowery objectives and focus on being a consistent authority for our information.
We have to be up to date so that we are the ones providing the answers and the AI pulls information from us. Whether it’s capital allocation strategy or something else, it should be our wording that’s out there. We need to present information in a way that ensures consistency in how it’s communicated and picked up.
Gevitha Anbarasu: So essentially, from an IR perspective, the takeaway is that structure and clarity matter, regardless of the tool, model, or platform.
Muge Yucel: Exactly. You need to put information out as clearly and consistently as possible. We also have a PDF graveyard. Because we have to do so much reporting, everything gets put somewhere, and ideally it should be available within two clicks.
The question becomes, are you focusing on two clicks, or are you focusing on making information available? How do you ensure information is accessible for an analyst in two clicks, while also making sure it’s available on your website so that when it’s buried in a PDF and can’t be easily found by AI, it’s still surfaced elsewhere?
Gevitha Anbarasu: So what can companies do to ensure content is easily accessible by web crawlers?
Muge Yucel: Yeah, happy to explain. What we’re trying to do right now is focus on FAQs. Whatever the question might be, we put the important answers there. Any question that comes up in meetings, we try to capture it in FAQs.
Another important thing is recency. AI pulls the most recent information. For sites that haven’t been updated since they were created four years ago, we’re going back and making changes here and there. We’re updating the About section to highlight key company points, adding more structure and consistency, and surfacing the information we want to be out there.
We’re also trying to add brief summaries of what PDFs contain. Ultimately, we’re working with our IT team on schema markup, which requires technical changes on the backend. We don’t always have the budget for major changes, so small improvements are probably our focus for 2026. In 2027, we may need to do something bigger.
AI is changing so quickly. Once you adjust for one thing, something else changes. The question becomes how fast you can adjust and where you should focus.
Erik Carlson: I think those are great points. This is absolutely a dynamic environment. It’s a new technology arc, and it requires flexibility and adaptability. That said, there are things that are just good hygiene for IR teams, PR teams, and corporate communicators, regardless of how models evolve.
There are three action steps we’ve been advising clients on over the past 12 months. The first is making sure you’re letting the bots in. Think of your IT team or website protocol as the bouncer at a restaurant. You need to allow the right people in to consume the data.
I’d encourage everyone after this call to talk to their IT department and ask, “Are we allowing traffic to surface our information?” Make sure your CDN and firewall are enabling bots. Then make sure your robots.txt file is optimized for ChatGPT, Gemini, and other bots. That’s foundational, and it’s something your IT team can manage.
The next two steps are invisible and visible structure. On the invisible side, site schema provides guideposts so bots know they’re crawling authoritative, clearly summarized information tied back to the source content. This increases an LLM’s ability to ingest content.
On the visible side, it’s about formatting and structure. We recently analyzed about 200,000 press releases and content pieces across 13 million citations over roughly 45 days. What we found were very basic structural factors: headline taxonomy, subheadline taxonomy, summaries, bullet-pointed takeaways, and content framed to answer questions directly.
For example, instead of just stating results, you explain cause and effect. “Company XYZ grew 10% this quarter, driven by X, in order to outperform analyst expectations.” Anticipating the question that will be asked and formatting content to answer it gives you an advantage.
So it comes down to three things: making sure your site is crawlable, focusing on schema and invisible structure with your tech and marketing teams, and auditing your content to ensure it’s consistent and machine-readable first.
Muge Yucel: What I’ve done… sorry, go ahead. Yeah, sorry. I just wanted to catch up on that. When you were talking about audits, I actually went on ChatGPT and Perplexity and typed in things like, “Look at Galata Wind and tell me…” you know, questions like, “What is the strategy?” or “What is this?”
Through that, you can basically find out whether you are being treated as the authority and whether the AI is actually pulling information from your site or not. And interestingly enough, we’re getting to the point where some of that information is now actually being tracked from our side.
Erik Carlson: But I guarantee that on your first questions, you were disappointed with the responses.
Muge Yucel: Totally. And it was wrong. That’s where hallucinations come in, right?
Erik Carlson: Yeah, the same thing happened to me. This is actually where my passion for this started about 12 months ago. On the PR side, I was driving my CMO nuts because I would spend time on Saturday mornings asking questions like, “Who is the best press release provider in the industry?”
I would see false information come up. It wasn’t false in the sense that it was incorrect at the time. It was outdated, and it was tying back to documents on our website that were four years old.
What it really comes down to is how you go through the content that you know is out there, or content you may have completely forgotten about, and rewrite that narrative consistently across your entire website and corporate domain. That’s how you take control of your brand and your financial narrative.
Gevitha Anbarasu: Perfect. Thank you both for summing that up. I want to go back to the poll results from earlier. The question was, “How does your team currently approach content discoverability in an AI-driven world?”
The leading response was, “We are aware of the issue but haven’t taken action,” with 28% selecting that option. How do we feel about that answer, Erik and Muge?
Muge Yucel: I honestly thought it was going to be higher. So it’s a good thing that people are trying things out, that they’re aware and want to do something about it.
This really reflects the trend of AI and the increasing flow of information and technology changes. We’re seeing it, and we have to adapt. Investor relations is right at the forefront, and we have to be among the first to do this. So yes, it’s one more thing added to our lives, as if we weren’t busy enough already.
Erik Carlson: Yeah, I agree. The 28% doesn’t surprise me because, as mentioned earlier, only about 25% of searches are expected to leverage answer engines by 2026. So that lines up with where the industry is today.
The most dangerous part of this is that anytime you add another translator or intermediary, there’s an opportunity for things to go wrong. That’s essentially what an answer engine is. You’re abstracting your direct communication and relying on a translation layer to convey recommendations or insights.
Making sure that middle layer is working for you and your company, and accurately translating and conveying your financial narrative, is really what today’s discussion is about.
Gevitha Anbarasu: Thank you. Let’s move on to the next part of the discussion. For IR teams looking to explore AEO, what outputs should they focus on? What should be their primary objectives when approaching AEO for the first time?
Erik Carlson: There are a lot of practical steps teams can take. I’m also seeing some great questions coming into the Q&A, and I want to make sure we address them.
The first step is to do an audit. That starts with prompts or queries. You can quickly come up with 30, 40, or 50 prompts that you run monthly against major answer engines, with ChatGPT and Gemini being the two largest today. ChatGPT accounts for about 80% of global search traffic, although that may be lower on the institutional side.
The key is picking one or two engines and using consistent prompts. Questions like, “What is the definition of my company’s performance last quarter?” or “What is market consensus around my company’s performance relative to peers?” These are questions you’d naturally ask when assessing your IR and communications strategy.
You’re probably not going to like the results you get, and that’s the opportunity. Where there’s a major gap between the story you want to tell and the answer being surfaced, that’s where you focus.
Once you identify those gaps, the next step is to audit content. Is there legacy or outdated content driving inaccurate answers? The good news is that ChatGPT, as an LLM, provides citations showing where the information comes from.
When I manage brand perception, I click those sources. If I own the source, that’s good news because I can control it. If I don’t, that’s where influence comes in, whether that’s talking to investors or analysts.
From there, you need a campaign to fight misinformation or clarify information. That comes down to thoughtful, strategic content. How do you tell the same narrative quarter over quarter and build predictability and authority so that story begins to surface?
There are structural tactics that can give you an outsized advantage, but those are really the two or three core action steps I’d recommend.
Gevitha Anbarasu: A question has just come through, and people are eager to hear your answer, Erik. If Notified is managing our IR website, is it already optimized for AI?
Erik Carlson: Yeah, so the short answer is yes, the website is optimized at the CDN layer, the schema layer, and the robots.txt protocol. What we don’t optimize is the content.
If you have outdated content because you’ve chosen to keep a PDF from four years ago on your IR website, that’s a content decision. As a practitioner in the space and as an IRO, you really need to step back and ask, “What is our communication strategy for the information we want to live on our IR website?”
We’re happy to partner with you. We’re happy to provide insight into where files may exist, where things are outdated, and help guide those updates. But the content itself ultimately needs to be managed by our end clients.
Gevitha Anbarasu: I want to move on to another question that I think many IR professionals ask. Are there any myths or misconceptions about answer engine optimization? Muge, you touched on this briefly earlier. Would you like to elaborate?
Muge Yucel: We often look at websites purely from a technical standpoint. SEO has been very important. We needed to be among the first results listed.
But AEO is not SEO 2.0. It’s something different. They have similarities, obviously. They share the same DNA to some extent. But SEO is about getting listed, getting clicks, and being discoverable on Google.
With AEO, the goal is for your specific answer to be cited and surfaced directly. That’s why it’s so important not to think only in terms of keywords, which is common in SEO. With AEO, it’s about structured logic.
You also have to consider that these platforms operate on prediction and probability. They determine what information should come next. So it’s not about tricking the bot. It’s about teaching the bot that your information is the correct answer and should be cited as such.
That’s where many misconceptions come in. We need both SEO and AEO. One does not replace the other. And you shouldn’t change your information in a way that makes it sound robotic. You still need your narrative. You still need to tell your story. It has to appeal to a portfolio manager as well as to an LLM.
Erik Carlson: Yeah.
Muge Yucel: We just have to find the middle ground.
Erik Carlson: I think that’s absolutely right. We’re writing for two audiences now. As if IROs didn’t already have enough on their plates, we’re adding another constituency.
There are two additional misconceptions I think are important to address. The first is the idea that big brands automatically have an advantage. We talked earlier about the democratization of information.
In a case study we conducted, we found that by maximizing structure, authority, originality, and recency, which is the framework we advise clients on and call the SOAR framework, relatively unknown B2B companies were able to generate more citations from things like Q4 earnings press releases than very large B2C brands. That tells me democratization is real in answer engine responses.
The second misconception is focusing only on owned content. We’ve talked a lot about content on your website, earnings transcripts, investor presentations, and FAQs. What’s difficult for many people to grasp is that 85% to 90% of content surfaced by answer engines is actually earned media.
That’s a challenge because we don’t own earned media. We influence it. This makes collaboration between IR and PR teams critical in 2026. We’ve seen strong examples of IR teams leaning into editorial bylines, which isn’t traditionally part of IR programs, to support initiatives that link back to owned content on their websites.
That linkage is key. Authority comes from consistency. Tagging your CEO in metadata, publishing executive bylines, and aligning earned content with owned sources signals to answer engines that your organization is a legitimate and authoritative source.
It’s not enough to focus only on owned content. Without a strong earned strategy, you’re missing a major piece of the puzzle.
Gevitha Anbarasu: Perfect. Moving into a more practical lens, how should IR teams audit what AI already knows? Are there any easy actions they can take to improve structure?
Erik Carlson: We touched on this earlier when I talked about auditing prompts. To reiterate, I’d recommend selecting 30, 40, or 50 prompts and testing them monthly or every two weeks, because this environment is dynamic.
There are tools that automate this process, so it doesn’t need to be done manually. Our partner Profound is one of the leading tools in this space. These platforms allow you to track share of voice and see the types of answers being returned, so you can actively manage how your information is being surfaced.
Gevitha Anbarasu: Muge, anything to add to that?
Muge Yucel: To be honest, that’s basically what I’m doing right now. Ever since I first heard about AEO and asked questions like, “What is Galata Wind doing?” or “What’s their strategy?”, I’ve been going back to see how the information changes and what information is being pulled.
Similar to what Erik mentioned earlier, we try to be more present in press releases and research content. We aim to show up in sector news so that, one way or another, there’s a linkage back to us.
Your information is already out there, and your CEO may have said something publicly. People read that, and AI learns that the source of truth comes from you. Over time, it starts treating that information as what’s important or what’s true.
Gevitha Anbarasu: This almost relates to the other side of the coin. Is there a risk of over-optimizing content for AI and losing the human touch or clarity in the information you’re producing?
Muge Yucel: I touched on this earlier. It’s very important not to make content robotic, but you still need to help the robot understand your information.
For press releases, in the last two we’ve done, we used to simply post the release exactly as it was sent out. Now, we highlight two or three key points right at the beginning. They’re just simple bullet points.
Honestly, I think it also looks better and makes more sense. You don’t have to read through a long, flowery narrative to understand what the company has done. A lot of what we publish is repetitive, but it has to be that way because there’s so much information out there. Repetition helps people understand the message in context, and it also helps AI capture that context. That’s one example of what we’re doing.
Erik Carlson: Totally agree.
Gevitha Anbarasu: Perfect. I want to go back to Erik. You mentioned the SOAR case studies earlier. How does recency impact AI, and should IR teams rethink cadence?
Erik Carlson: It’s a really interesting question. Historically, with earnings releases and other IR events, there’s usually a 24- to 48- or 72-hour PR cycle. You expect most of the impact to happen within the first two or three days.
AI citations work differently. When we look at how often a piece of content is cited or sourced by answer engines, there’s a much longer trajectory. Roughly 95% of relevant citations occur within the first 45 days.
If you’re not updating content at least every 45 days, you’re losing the ability for that content to surface because it’s viewed as outdated by the answer engine. That creates a different cadence than publishing quarterly.
We’ve also seen that only about 50% of citations appear in the first 16 or 17 days. There’s a long tail and a bell curve to how content gets sourced. So don’t be discouraged if great content isn’t immediately visible or cited. It usually takes a few days for updates to be indexed and then start appearing in answer engine responses.
Gevitha Anbarasu: Perfect. Oh, sorry, I didn’t mean to cut in.
Muge Yucel: Sorry, I just wanted to add that we’ve found sections like the corporate profile or “About” pages tend to be very static. You need to revisit them, make small updates, and keep them active so AI continues to crawl and reference them instead of pulling outdated information from elsewhere.
Gevitha Anbarasu: Perfect. I’m mindful of the audience questions coming in, but before we move to those, there’s one key question I know the audience would love answered. What are the three things companies can do today to maximize visibility and accuracy in AI?
Muge Yucel: Luke, do you want to kick off? Sure. I think companies should start the way I did. Build an extensive and frequently updated FAQ page, with all information written out and not linked to PDFs.
Add key highlight boxes to press releases. Consider checking responses from LLMs before earnings calls. That’s something we’ve started doing, to see whether bots actually understand the information we’re about to release, and then refine the content before publishing it on the website.
Headings also matter. I used to rely mostly on bold text, but now I use proper heading structures like H1, H2, and H3. That’s how we present information on the website now. It’s a learning process, but it’s been helpful. Erik, what do you think?
Erik Carlson: I think those are great, practical tips. For me, it starts with being curious and auditing both the answers being surfaced and the information on your website.
Second, coordinate closely with experts across your ecosystem. That means working with IT to ensure your systems support answer engine visibility and collaborating with PR and marketing on earned media, which matters more than ever.
Third, and this may sound like a shameless plug, but it’s a useful checklist: download our SOAR case study. Structure, originality, authority, and recency. Focus on controlling what you can control.
It’s very difficult to control answer engines themselves, but you can put yourself in the best position to ensure that accurate information and the financial narrative you want to convey to markets, both institutional and retail, is being surfaced appropriately through that middle layer. Those are the three points we advise clients on every day.
Gevitha Anbarasu: Thank you both. And I believe the checklist will be available to download after the webinar, so do be sure to check that out, I thought it was quite helpful.
I’d now like to spend some time on the audience questions that have come through. Eric, there are quite a few here. Let’s start with one that ties back to Muge’s earlier point about AI hallucinations.
The question is: How do you deal with AI hallucinations? We recently received a request from an investor to comment on information that surprised us, and we later realized it was the result of a ChatGPT hallucination. How do you handle a situation like this?
Muge Yucel: We actually had a similar situation happen to us recently as well. It even came up during our last earnings call, where a question was asked that was completely well not correct.
In those situations, since you’re speaking directly with the investor, you obviously have to correct the information. Then you need to understand where the bot sourced that information from.
One very important issue we’ve seen is that many of our PDFs are column-based. And correct me if I’m wrong, Eric, but AI systems tend to read across rather than down. That’s correct, right?
What happens is that the AI may pull information from across the page instead of from the bottom section, which actually contains the correct information. That’s where the mix-up occurs.
This is why it’s so important that your most recent PDFs are available directly on your website, or that the information you want AI to read is immediately accessible on the site itself, so it can be pulled accurately.
Erik Carlson: I think that’s a great point. I’d start by asking for the source, because typically ChatGPT or other large language models will cite where the information is coming from.
If the information is verifiable from that source, then you likely have a content problem. If it’s not verifiable, then as Muge mentioned it could be related to the structure or metadata behind the PDF.
LLMs today are not very good at ingesting complex visual components. So while visuals are fine to include, it’s critical to clearly articulate what that visual is conveying in the paragraph below it.
Highly stylized charts, pie charts with small numbers, or visuals that strictly follow a design guide often aren’t easily ingested by AI systems unless the meaning is clearly explained in text.
Gevitha Anbarasu: Perfect. I’m conscious of time, and I believe we can address some of the remaining questions in a post-session follow-up.
So I’ll move to a brief summary of the key points that Muge and Erik have raised today. First, AI is already reshaping how investors and analysts discover, interpret, and summarize company information. Second, AEO is not about chasing algorithms, it’s about improving clarity, structure, consistency, and accuracy across core IR materials. And finally, progress in this area doesn’t require a complete overhaul. Small, practical steps taken today can significantly improve visibility, accuracy, and confidence in how IR content is surfaced and interpreted.
Thank you again to Eric and Muge for sharing your expertise today. As mentioned earlier, the recording of this session, along with our previous and upcoming briefings, will be available for access on demand at ir-impact.com/events. You can also access this recording on the Notified platform.
Eric, do you have any final remarks on other webinars or materials our audience can access?
Erik Carlson: Just one quick note. As I’ve been looking through the questions apologies we couldn’t get to all of them, there are some really great ones here.
One recurring theme is whether we could provide a list or sample of those 40 audit questions. Forty questions can sound like a lot, and I think that presents a great opportunity for us, as a thought leader in this space, to publish something helpful.
I’ll take that back to my team. And if there’s interest from this audience, it’s absolutely something we could publish in a short-term thought leadership piece or case study.
Gevitha Anbarasu: Perfect. And any final thoughts from you, Muge?
Muge Yucel: Looking at the questions, they actually give me a lot of input for the monthly articles I write on LinkedIn. I recently shared prompts related to benchmarking and how I use AI on the buy-side perspective.
There’s a lot of valuable input here, and I think it’s something we can continue to put out and share together. Sharing knowledge is always helpful.
Gevitha Anbarasu: Perfect. I’m looking forward to reading more of your LinkedIn pieces, Muge, I’ve been following them and find them very insightful.
Thank you both again for your time today, and thank you to everyone who joined us for the briefing. From all of us here, happy holidays, and we wish you a pleasant winter break. Thank you.
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