Name: Disclosures Decoded: How Global Sustainability Teams Are Winning 2026 with AI-Ready Reporting and Nasdaq Lens
Uploaded: 2026-09-15T14:41:15.839Z
Duration: 59 min 32 s
Description: Disclosures Decoded: How Global Sustainability Teams Are Winning 2026 with AI-Ready Reporting and Nasdaq Lens

Transcript for "Disclosures Decoded: How Global Sustainability Teams Are Winning 2026 with AI-Ready Reporting and Nasdaq Lens": Good morning, good afternoon, good evening to all those that are joining us today for the Nasdaq webinar, disclosures decoded, how global sustainability teams are winning 2026 with AI ready reporting and Nasdaq lens. I'm Mike Stiller, head of new initiatives here for our corporate business. I'll be your host. I've got some great great guests that'll come through, but you'll you'll see a familiar face throughout, which is which is me. Before we get started, one piece of of house, housekeeping. First, the disclaimer on on the, on the slide here, explaining a little bit about, the the risks associated with, you know, leveraging information from this presentation. This presentation is not meant to be legal advice, and I am not an attorney. So I'll let you read that disclaimer very quickly. And then the second piece is data presented in this presentation is powered by Nasdaq lens. We'll talk more about Nasdaq lens later, but it is an AI powered, research benchmarking drafting solution. And as many AI powered solutions, are these days, you must, verify the outputs, based off of, your your own internal team's perspectives and your perspectives as practitioners. So jumping into the agenda today. First, we'll talk a little bit about, the 2026 ESRS and IFRS has two disclosure trends, and so we leverage Nasdaq led us to to perform research on on disclosures to gather those insights. The second, we'll invite in, my colleague and an expert in the field, Victor Agajanian, to talk about what it takes to build AI ready disclosures. And then last, really excited to talk about AI in action use cases from the field. We'll share a little bit about Nasdaq lens, and then we'll invite in Beatrice Scharnberg, the head of external ESG reporting and ratings from DHL Group to have a conversation about the trends she's seeing, how she's implementing AI, and the promise that it, that it serves today for for reporting teams. Another housekeeping item is you'll see a chat and q and a box on the top right of your screen. Feel free to ask questions along the way, and we will get to those questions at the end for a, a panel q and a. So let's jump into the 2026 season. We're we're nine months in, so there's still a little bit work left to do. But nonetheless, most global companies, most companies in Europe and and rest of the world, have have reported have put out their annual reports already this year. So a lot of information to gather and and information to come through, and we're excited to share those. So we'll cover the SRS priorities by sector. We'll cover the, revised the SRSs. I know that's a hot topic right now is and all that's going on with the regulators and and eFreg. We'll talk about readiness and gaps there, and then we'll finish with the IFRS s two readiness and disclosure gaps as well. So let's jump into, some of the things that we've identified through leveraging Nasdaq lens, specifically around ESRS, disclosures. And so we leverage, the solution, and, again, we'll talk more about the solution later on, to understand how companies are disclosing against ESRS topics. We looked across the, call it, 10 or so sectors you see on the left, and we looked at how they're disclosing or mentioning each one of the the, the topic areas e one through g one and the frequency at which they're they're mentioning those. And so the universe of companies was about 2,300, twenty three thirty six to be exact. We analyzed the twenty twenty five annual reports that were put out year to date. And so not all those companies must comply with with the ESRS, CSRD, but, nonetheless, we thought it was an interesting comprehensive set by which to understand how companies are disclosing information. And so you'll see, the the frequency across each one of those categories is measured, and you can see the the shade of green that depicts how frequently that, those those sectors or those companies in those sectors are are mentioning or talking about those particular topics and the relevance or the the information is based off of per sector, not at a broader category level. So some some interesting insights that we gathered, 42% of all ESRS mentions were in the e one and s one topic areas. Probably not a major surprise, most relevant material to most companies. The the second insight was 13% of all mentions were in in g one. And so companies between g one, e one, and s one, that captures the lion's share of of disclosures for for companies called 55%. The fourth most common was was s two, at 9%. So you see a material drop off between e one, s one, g one all the way down to s two. And you can see on the screen here, there's there's areas in which, companies are not disclosing heavily, s three as an example, and then you can see some other errors on the on the the heat map as well. Coming into sector specific insights, we thought was interesting, basic materials. And so you think about chemicals, you think about companies in the mining space. Those are the most disclosed across, all sectors, almost two x that of of real estate. And so typically most likely because of their larger footprint and the and the impacts they have across, the ecosystem, both internally and externally, they are disclosing the most, per company. That is followed by utilities. And second, consumer and then industrials as our as our fourth, most most, disclosed areas per sector. And then as you can see here, back to my original point or my point just before, basic material sector was the most evenly distributed across the SRS topics. Between industrials and basic materials, you can tell, again, the most impacts, most material, components of of the SRS. And so because of that, you're gonna see a broad distribution, where they disclose the most across all all topic areas, e one to g one. Moving on to to the next interesting, area, we wanted to assess whether the new ESRSs, materially change the readiness picture. Right? And so we've seen a lot of work done by the regulators over the last several years to take the original ESRSs from 2023 and revise those down to a more manageable set of of disclosure topic areas. And so what we wanna do is look at sector again and and assess, you know, which come which sector is the most ready to to meet, the revised ESRSs. And note that this does not in in, take into account materiality. This is looking at all the ESRS topic areas and assessing every sector at the same level. And so that's why you see some of the the readiness gaps by sector. Nonetheless, you can tell basic materials, utilities, consumer, noncyclical. So those are more like your, your household goods, your consumer staples. Those are the, the the most ready to meet the the new the new ASRSs. And then you have some of the the the more, I would say, in resource light healthcare, financials, technology that are that are lease ready, most likely because their, materiality assessments aren't going to include a lot of the the heavy, environmental topics that that you might see from from some of the the more resource intensive sectors. Some of the headlines that that we've gathered here is is simplification does not mean readiness. You're still seeing pretty substantial gaps from companies that need to meet the the standards. And so even though, a streamlined ESRS framework, we still see, readiness, more work to be done, I suppose, for for companies, and it does not, you know, eliminate the the data challenges and the underlying process challenges that companies face. One other thing that we gathered was, g one. So business conduct is definitely, the most, companies are most ready to report against, that that that, that topic area, probably because the it's the most kinda mature in terms of, long standing compliance, ethics, and anti bribery governance processes. Companies have had those in in place for a number of years, and so not as much as a polar lift to go do to go do that work. Another category that we thought was really interesting was was water, was was surprisingly mature. But, again, sector specific. And so, you know, for the sectors that have, you know, materiality based off their DMA in the water, category, they are most ready as you can imagine. And then you see some of the sectors that might not have that water dependency that might not have, that that area of, material. They obviously have have much larger gaps than than those are those that are ready. The last thing we wanna talk about is the gaps, at a more granular level. So instead of looking across all the categories, because we know materiality plays such a large role here, We want to look at those gaps that remain in e one, g one, and s one. And so you can see the top top six here, whether it be anticipated potential effects from physical and transition risks to targets on business conduct, a g a g topic, to training and some some of the other social categories. You know, those were the common ones that existed that had the highest gap areas across all sectors, within the the most relevant e one, g one, and and s one categories. So as you prepare and get ready for, the the revised DSRs, we want to share this information to give you a broad set of insight of what others are facing and how to maybe compare yourself at a more sector level. Okay. Moving on to, to a non ESRS topic. We, we wanted to look at IFRS s two, because it impacts so many companies globally. We know that IFRS set the standard, and then there's the the local jurisdictions that are adopting those standards, largely keeping them intact, but some some jurisdictions are are changing them ever so slightly. So what we looked at was, again, IFRS across all the different pillars from governance to, strategy risk management metrics and targets. And then we looked at the companies in those in those countries and we only picked, call it, 15 or so. We thought the ones that were most relevant for the global regulation that are, you know, dropping that are applying the the regulation down at the the jurisdiction level, the local level, and want to understand how those companies are prepared to meet meet the moment, tied to to IFRS s two. So you'll see the in in light gray on the right of the chart, you'll see the number of companies in each category. Obviously, you see the most companies in The United States. And then, up at the top, you'll see fewer companies in New Zealand, but nonetheless, what we thought was statistically relevant for for the analysis. And since you see New Zealand all the way down to United States, the companies that are or the the the countries this companies in the countries that are that are most ready, we thought it was interesting that that The UK is is pretty high up there knowing that The UK SRSs are are in flight and starting to work, work their way through the system. Japan obviously has had, IFRS on the on the docket for for quite some time. And then you see several other countries in Asia Pacific that, that are preparing or adopted the IFRS s two standards, but companies have still still more work to go. What we thought was, three three key takeaways. We thought sector differences were actually surprisingly modest. You know, it really varies between, like, 404159% per per sector. And so I know that's not on the on the slide here. Real estate's the the most ready versus technology's the least ready to meet the s two standard. You know, again, it's the carbon intensive sectors that have been reporting on these topics for a number of years most likely are are have been active in this space and and can meet the I versus two standard much much easier than than others. One challenge that we identified was turning climate risk into business impact. And so governance was the strongest or most, companies are most ready to meet the the governance pillar in its entirety at 62% where strategy really trails at 49%. And so, you know, get climate resilience at 27%, the effects of climate, financial position at 39%. You know, there's still a lot of room to go in terms of turning climate risk into business impact and really measuring that. And then the last piece of of insight that we gathered was targets are easier than than proving resilience. And so GHG reduction target readiness is almost 50% by the the company set that we looked at. It's almost twice, the the readiness for climate resilience strategy. So setting targets and actually, you know, setting target is is one part of the equation, but then showing how you're going to, perform under, you know, resilience strategies or climate scenario analysis. Climate scenarios is is definitely still, a gap to be done. Similar to the SRS, view that we looked at in the previous slide, you see the top disclosure gap areas for for companies at a more granular level, from climate resilience strategy, which what we just talked about, all the way down to, you know, risk identification process. Still more work to be done for companies as the more technical items. Scope three, obviously, is a challenge for many companies as well. And so more of the technical areas that that companies need to need to get ready as as their local jurisdictions are going to, you know, ask them to to report on these particular metrics. So, that takes us to the end of the data insights portion of of our, agenda here. Next, we'll talk about, you know, building AI ready disclosures, and that's gonna be in two categories, how machines find your reports. So you put all you all put out great content. You put out great reports, but you wanna make sure it's found by, the the machines that are gathering those. And then two, we'll talk about how to make your reports more AI readable, AI ready, which, is is a is an important topic. I'm sure you spend a lot of hours and and calories, building the reports. You wanna make sure that they can be digested, not only found, but digested by by AI tools. So I'll invite in my colleague, Victor Aghajani, in to talk a little bit about, what he's seeing as a practitioner in the space. Hey, Victor. How are you doing? Alright. Yeah. Doing. well. Thanks. Yeah. K. Good. So, Victor, I'll I'll give it a a precursor here. Victor, is our AI lead for our Nasdaq lens solution. So, we'll talk again more about the solution in a bit, but he is at the front and center at the forefront of of this question, these questions today. And so he has been building the space for a number of years and what I believe to be he's one of the the leaders in in in this this topic area at the intersection of sustainability reporting and disclosure and AI. So really excited to have you tell us a little bit about your findings and things that you think are important for companies to, to to learn here. So let's jump in. A lot of information on the on the slide here, Mhmm. what what our our, our audience sees. So maybe I can boil it down or I can have you boil it down into into a couple couple key key areas. I'd say. the first question that that, I have in in talking to you is, you know, what's the best way for for companies to to publish sustainability disclosures so that AI tools can reliably find them, and access them? Yeah. Yeah. So, yes, like, there's many insights on this page, so, I won't cover, like, every single detail. But, like, yeah, I think, like, you know, over the years, I've been here for for five years. Been doing this for for five years now. We've seen, like, thousands and thousands of disclosures pre charge GPD, post charge GPD error. I think, what we find especially recently is AI performs best, when, sustainability disclosures and policies are published as stand alone, you know, self contained PDFs. And when I say, like, AI, I mean, like, tools like, you know, perplexity, ChargeGPT, Cloud, nonstop lens, like, all of these, you know, tools that that use AI to fetch this information. No. This is about, like, finding, the information exists in the first place, and the other three are about the extracting. So one of the first things we say is, again, like, we're practical, making, the key disclosures available as download downloadable PDFs and not only as, HTML content is very important. And, the reason is that, like, if you think about the alternative is to have, you know, the the website content in many different pages hidden behind navigation where you have to, you know, scrape it, and then you update it over time like policies or something, you know, code of conduct, for example. Imagine if it's in the website, you update it over time. It's really hard to see when it is updating. There is no, like, a time stamp. But with the PDF version, it really makes the retrieval comparison and citation more reliable. Especially, more recently, like, the citation and sourcing is, like, table stakes for a lot of this area. I don't know, complexity, for example, us, you know, like many others, where you cite either the page level. And with the website content, it's really difficult to to to do this and also, like, you know, keep track of, you know, what's going on. And and the second, I would say, like, very important thing is, using descriptive titles, especially for things that are you know, you don't expect to update every single year, like in a supplier code of conduct or transition plan. Maybe you update once every three years, four years, using clear titles in the document, descriptive titles. Because a lot of times when you are doing this sort of, like, scraping work, you look at the naming and then decide, like, whether you should go for the document or you shouldn't go document because a lot of times, like, links don't even change. So, making it clear that there is a change, just signals that, you know, there could be, like, a year over year benchmark. There's an update in the, you know, in the policy or in the disclosure that we wanna highlight. And, yeah, where where also, like, where it makes sense, to publish framework aligned. I love framework aligned, indices. Especially we're talking about the SRS. We're talking about IFRS and other things. Both times we see this, like, published in indices, and it was always difficult to read. Like, when I say indices, I mean, like, you know, when you do, like, page reference, without, like, really giving any, any any information inside that report. I think in the in the in the age of AI, this in this is, like, tells that it might exist the information, but it's really hard to reconstruct the answers, from, from those, you know, other sections. Even if it's limited content, just like acknowledging that it exists and more information can be found somewhere else. It's truly like, I see companies still getting more credit when they do that. And the other insight, like I was saying, this kinda category is if you are using a large report, if you're getting, like well, companies have this mega report. Having, like, very clear markers and structures, you know, structure sections, where the topic is disclosed is very important, like, how were you wanna do it. But, like, the worst thing to do here is, to, you know, frog disclose fragmented information in page five to eight and then 45 to 49 in about, like, circular economy. The reason it's bad is, because, like, all of these AI tools are built in a way like you are fetching x number of pages from each report. So you might like, AI might miss information if it's not like, if there is no, like, you know, start and then it's not, like, very clear. So it's very important to signal, like, where things start and where things, and, which is kinda like I say, it's it's really increases the signal to noise ratio, which is, which is really good. So yeah. I'm sure, like, there's a lot more to share, but, these are, like, I'll say, four top four things that's on my mind right now. Yeah. Maybe you can share one more, that I that I always have is Mhmm. okay. Found the report the the machines have found the report, right, at scale, and we collect thousands of reports. You know, how do you make it how do how do practitioners make it easy for the information to be found within the report? I know you just shared some insights, but. to read, extract, and interpret accurately beyond just order of of the the content, what else can you share just in terms of how how. companies can display their information in their in your documents? Mhmm. Mhmm. Yeah. Yeah. I I think it's yeah. I'm sure I'm sure, like, a couple intuitive things. And, if you have any questions, please ask in the chat. We're happy to address it, in the end. But I think, like, the most important thing is publishing digital native PDFs. And what I mean by that, you'll be surprised how many times we see, like, companies take a screenshot of a table or, an image of a graph and embed it inside even if it's a PDF. There's narrative, but then the data is in an image. And and the reason it's it's it's it's bad is, like, yeah, if you look at the, you know, like, the most capable models, like, you know, Astra or Fable 5.1, they are obviously like all of these, models can read, images. But a lot of the times when we talk about the extraction, which is what is done before it goes to these most capable models, is you extract the content from the PDF using a, like, framework or library. Right? And then, these images are really, really easy to miss, unless you throw, like, the most, you know, capable OCR model or most capable, like, vision language model at it. So it just, like, you will make the life easier for everybody if if it's actually like, if there's a nice image, that's great. It's nice, you know, like, waterfall chart about circular it's great. But, like, having the information also in the narrative or in a table that follows it or in some sort of, you know, like, a clearly labeled, metrics, I don't know, chart. It's really, really important. And then, the the the other thing I wanna share is using consistent methodology, especially we talked about today the SRS, IFRS. We're thinking about the raters, rankers, or we're talking about the, you know, thing like tools that do gap assessments, which is, for example, Nasdaq lens, we look for, consistency in in the in the reporting. Right? Like, I think inventing internal names for things that shouldn't have, like, different names is is, like, definitely, like, doing the service, because, like, for example, if something is called climate transition plan in, in the SRS, like, let's just call climate transition plan, not the climate action plan or some other name. It's just like the most basic example. I'm sure there is there is more. Just trying to be as close with terminology as as as you can. Because if you think about, like, how these tools are trained, to find the most probabilistic, responses, it's just like think about, like, there's so much, presence of IFRS and CSRD and all this other GRI in these, in the Internet. So that's really, like, you you definitely, like, help help yourself to give get get more credit. And then the other thing is, somewhat related to this is, when you talk about a metric, disclosing the unit of measurement here and, some of the other metadata and, like, the key met identifying information about the metric, around it so they end up in the same page or in the same I mean, they end up in the same, extract like methodology and everything. So it's just more important. Like, it's more more important than ever to do that. And then one last thing I wanna share, is, show you which kinda sounds silly, but show your work even if it's not done yet. And it's I don't wanna say, like, trick AI, but, like, it's kinda, something to do to, get credit when, it's not fully done yet Using the words like in progress, under development, even if the say, like, a target is not fully, done yet or, you know, they are working towards it. We haven't finalize finalized it for whatever reason. Acknowledging it. And I see this, like, you know, company disclosed sustainability statements. You see, like, all of these things are present, like e one one. You know? But it's not really disclosed, but they get a lot of credit for it. Right? Like, they don't have a transition plan, but they talk about the efforts, discussions, governance. I don't know. Things that they have done, inside the company to do that, and they get a lot of credit, for it. So I'm sure there is a lot more. I'm happy to answer any questions that might come towards the end, but, I'll stop here for a moment. Yeah. Tons of great insight. You know, feel free to ask questions. We'll we'll get to to get them at the the end of the presentation, and a conversation with Beatrice. But, yeah, Victor, super, super interesting, and I'm sure there'll be questions at the end for you. Thank you. Alright. Moving on. So we'll cover, what we call our section of AI in action use cases from the field. And we'll cover two things. We'll do a live look here at Nasdaq Lens for a few minutes, and then we'll invite in Beatrice, from DHL to have a quick conversation about, some of the challenges that she's facing, and, and how her and her team are leveraging AI to be effective and efficient. So I'll stop sharing the slides here and we'll go through, the screen. Okay. So, hopefully everybody can see see the, the screen here. And so what you're looking at is is Nasdaq lens. We've talked about it in several several, several areas today during the presentation. And so wanna give some background context about about Nasdaq lens. Nasdaq lens is a disclosure intelligence, solution really focused on on three specific areas. One, rent, research. The second is is benchmarking, and and the third would be redrafting. And it's built specifically for disclosure teams, and so that is inclusive of sustainability, legal, finance, and risk professionals at companies globally. So a a few more points of context of what you're looking at here on on the screen. The first is everything's based off of a focused company here. Everybody's, sorry. You're looking at, Nasdaq specifically, so it's personalized to to whoever's using the solution. And so you can see Nasdaq here. The second is peerless. And so you can create as many peerless as you want. I've got a global MyCAP peer list here of 73 companies. And the third point of context is the data that we, sit on top of. And so we go out and collect sustainability reports, other disclosure documents, discover, global governance documents, compliance documents, financial documents, annual reports, etcetera. From over 12,000 companies globally, we bring those into the solution. We make them AI ready. We extract out the disclosures that are relevant, and then we compare those to, to, to other disclosures as well as across, across companies. And so we have three twenty different document types that we're we're collecting and then we make use of, in the in the solution. So first point, that I wanna talk about here is our disclosure research agent. This is the the technology that underpins the entire solution. And so as you could imagine with a conversational based experience, you've got a an open, text field here to to ask questions specific to to disclosures. And then we've got a series of, work products that you can generate, if that's of interest to you. So maybe we'll start with a question relevant to the, the the the research that we provided at the beginning of today's presentation, specifically around IFRS s two reporting. And so I'm gonna ask a question here. What are my peers which of my peers are leaders on IFRS s two reporting, perform a high level analysis, and do a deep dive specifically on the effects of climate risk and opportunities on financial position, put it into itself. And so what it's going to do is it's gonna go up to the pure list here and, and and those 73 companies and start to do its work and really gather that at those insights. And those are unique insights that that only are available in Nasdaq lens from our scaled gap assessments that we do that we feed through the research agent. So while that is is running, it's starting to it's processed here. I'll come back to the home page. I wanna run another, another question that I think is relevant for for the audience today as you get ready for the 2027 reporting season. Many companies are looking at what are their peers doing in terms of commitments and goals. And so to be able to quickly run that type of analysis and you can see here, I've asked it to prepare, executive ready PowerPoint, slides with sustainability commitments made by my company and its peers. And, again, it's gonna go up to those 73 companies. So it's gonna do that that work at scale, that you could not get from from a general general tool. So those are gonna work in tandem here. I ran those previously, and so I'm gonna go look at, what what those those results were, as opposed to wait for them. They take a few minutes here. So let's look at, what was discovered from the the first question. As you can see here, it took a a few minutes to run. There are 10 steps. It shows its work as you'd expect if you were to send off, an intern or or an analyst to go do this work. It wants to show its work as well and how it arrived at this information. You can see it pulled those 73 companies or 72 companies or so, looked at all those companies, looked at the readiness to meet the IFRS s two disclosures, broke down the overall completion readiness, and then looked specifically at the categories and picked out the ones that are most aligned or ready to to meet the disclosures. I asked for it to create an Excel, and when it did that, here's the example, of the Excel. Here's the preview. So So, obviously, three tabs. You can see, you know, what it did, end to end. So you now have a a broad benchmark across, you know, over 70 companies globally of who's a leader, how do I go learn from them, and how do I improve my disclosures. The second one I'll I'll show you is, the, Excel or sorry, the the PowerPoint presentation I asked it to create. And so if you remember, I asked it to create a a presentation about those 70 companies' sustainability commitments, and so I asked it to to cite its work. Similarly, it went off and did 10 steps. You can see those steps here as to how it arrived at its answer. It went company by company, to understand, you know, what what, the the the specific climate targets are, and where you're finding that information in the documents, and then ultimately went deeper into areas that, had the most ambition, least ambition, etcetera. And then you can see it cites its work here, at the bottom. And so you can always get to the content side by side, gets you right down to the page number in which the the information sits. So in this case, I'm looking at Nasdaq sustainability report from this year. It got us right to the page in which climate targets, SBTIs were were, were housed. So really quick way to, gather information at scale across many, many companies or leverage unique Nasdaq data and insights to power climate reporting, regulatory reporting, and create files for you to go and actually, work with your internal teams and present to executive teams, etcetera. So that's our, that's our research agent. Moving on, wanna show a little bit about our, our gap analysis at scale. And so we perform these gap analyses, and this is the data that underpins, again, all of the information that we showed you on the on the slides around DSRS and and IFRS s two. And so we go out and, have public disclosures from 12,000 companies. We compare those public disclosures against the the reporting standards and then have scaled gap assessments for companies to understand their readiness and then where they need to, to improve and meet meet, meet, you know, the the regulations. And so you can see the the standards we cover here. I'll click over to s two and look at s two relative to Nasdaq here, as you can see. And then if you wanted to go in and look at, for example, one particular area, it's gonna have that information, and you'll see all the peers here that we have loaded up, of course, against the peer set, the same peer set. It looks at a very, you know, granular paragraph level, tells you whether you're aligned or not aligned, gives you the details as to why you can generate insights on the fly leveraging our gap analysis agents. And so you can generate those instantaneously. It's almost like having a consultant in your pocket, and everything is site source cited and so you can get down to the information of where it's pulled from. In this case, this particular disclosure requirements pulled from the sustainability report and climate transition plan. And you can always go to peers and understand what they have and go learn from them as well to understand the how to improve and and where peers are meeting the moment and and you need to go and and clean up those gaps. Last thing I'll share is our drafting capability. And so you have the ability to generate drafts against, standards. And so in this case, I'll generate a draft against s two, against Nasdaq's disclosures. I'll pick the, the four categories under, under governance. You can pick your documents and, and really understand, you know, where you want to, you want to compare your disclosures or generate the drafts. You can pick across a number of different documents here. I'll just pick the twenty twenty four reports and generate them. This takes a few minutes. So I'll go back and and show, what this looks like in, in the history. And so I generated generated this a bit earlier. But, nonetheless, you can see, you know, we've selected the requirements here, the four requirements. And on the fly, in a matter of minutes, it looks at those requirements, performs a gap assessments against the documents that I collected, and then or or selected rather, and then we'll generate a draft as a first draft for your team to get started as you think about disclosure. So in this case, we we gathered we put it against an ASX stain available report. We put it against, the proxy, I believe it was. And so you can see it'll it'll cite the information where it needs to pull over those disclosures to be, more aligned with the the s two standard. And then two, it'll generate new text generate new text where it thinks you need that text, where you have a gap. So in this case, for example, it generated new text here, where where it believed that there was somewhat of a misalignment even if small against the standard. So that's a bit about Nasdaq, Nasdaq lens. Three specific workflows I want to show, general scaled research leveraging, a unique content library and Nasdaq unique data and insights, gap analysis against the the reporting standards, and then third, drafting against the the reporting standards as well. Many, many other workflows, but nonetheless, three ways that our our clients are currently getting value out of out of Nasdaq lens. So moving back over to, to the presentation, and and a great conversation, ahead of us with with Beatrice. Let's invite in Beatrice from DHL group to have, a really thoughtful and exciting, fireside chat on how DHL is thinking about disclosure, how they're leveraging AI, and what's, what's top of mind for her and her team. Beatrice, come on in. Hi, Mike. Thank you for having doing? me. Yeah. Thanks thanks for joining. Thanks for. joining. from Germany. Yes. Thank. you. So let's, let's kick us off here. Why don't you tell us about DHL? Right? I think most have have heard of it, but I'd love for you to talk a little bit about the the company and more specifically where sustainability fits within the organization today. Sure. DHL Group is one of the biggest or largest, logistics provider worldwide. We are present in more than 200,000 comp, countries and c territories. Our main part is, express business, express transportation, as well as warehousing, and, of course, the courier services, yeah, from door to door. With our sustainability approach, it's fully embedded into our business. We have made our decisions already very early back in the 2008 to integrate our GHG reporting emissions reporting into our financial system and broaden that even more since 2019 already when we worked to develop our ESG road map, and set new targets which are all, validated by s p SBTI. Our strategy, of how did we embed it into the entire group and into our business, We have three strong pillars. The first one is our strategic approach. So we are very much looking into the future that is done in our CEO department. Then we have the responsibility of the finance department where I am sitting. Actually, we are direct reports to our CFO, Melanie Kaurice, and we are looking into the accounting, managing all the KPIs, creating definitions, which is relevant for the entire group, each and every country, each and every subsidiary, each and every employee has to follow that structure. And we are setting targets, and we are following the track. Yeah. Do we reach our targets? Do we not? Why do we not? And what measures can be taken? And the third pillar is, of course, our business and our divisions. They have to make sure that's implemented on the ground, that we follow our rules throughout our supplier code of conduct and our code of conduct. Yeah. That's, the major pillars. Our major focus in sustainability is, of course, the impact on the climate and on the environment. As we are still relying, we are a business. We are not a manufacturer. We are service provider. We are transporting actual goods from a to b. There is no digitalization possible. Nevertheless, all relate parts of our business which we can transfer to digitalization. For example, those tags and stickers for our, transportation services are already digital and also our reporting. There is, yeah, in the end, no paper involved, at. least on my side. Yeah. Yeah. That makes that makes a lot of sense. So maybe you could talk a little about your role specifically and, you know, what are the core responsibilities you own, one. Yep. And then two, where would say the big the big challenges you face? Yeah. I'm responsible for the external reporting. So the, official reporting in our annual report, which is also audited by Deloitte, our auditor company, which, who is also taking care of our financials. I'm also responsible for our, voluntary reporting, which is a presentation and a data haven, our stat book, where we are disclosing our ESG data since 2016 if available in thousands of breakdowns. You can't count it. And, then, of course, I'm also dealing with our ESG rating agencies, and with our customer requests if they are requiring even more information than we are actually providing throughout our reporting. And what we are facing this year, of course, we are looking into 2027, you know, how to adopt the ESRS two point o. So the omnibus effect, we have made up our mind already. We don't see any reduction in our reporting because to disclose the top line numbers, we have to collect the downside, numbers still. So we can't change anything in the end. And we have to follow or to apply new rules for, doing the double materiality analysis. This is also under development. And what we are facing actually with the reporting is it's already very complicated with the adoption of ESRS. But the IFRS development outside of Europe is also, challenge us challenging us in some ways because there is no one to one adoption. And what we see in the countries, it's more or less, kind of, cherry picking sounds so negative, but it's adding something here, collecting something there. And then, yeah, you have a news framework, which is no longer IFRS one or two. So that makes our life a little bit more complicated. And then, of course, what we also see is, dealing with our customers because they are not all located in Europe, so they don't care about ESRS. On the other hand, they don't adopt IFRS, and then it's a mixture of everything. And, yeah, to find the balance is quite difficult somehow, Yeah. but I think, we are on a good track. yeah, what is old is new again. The global standard didn't come to didn't come to fruition, but, we're definitely seeing some regionalism. Okay. So, at some point, you realize those and those challenges probably aren't wildly new. They probably have been around for a bit. But you realize you realize at one point you needed, a new approach or new tools to support your efforts. And what what challenges were you trying to solve when you were looking around for for new tools to to alleviate some of those challenges? Yeah. First of all, we looked on the market, of course. We we saw many, products available and diff with different approaches. For us, it was I think the major topics for us are usability. So easy to use, easy to digest, having all or having a big bunch of reporting information, and that the tool itself is updating more or less immediately if something changes in the legislation requirements or in the standards, which we apply. And, we're using, NASDAQ lens since last year. Still, we are working on evaluating what is the actual outcome. We are using it now for our double met reality analysis. On the one hand side, to do a thorough benchmark analysis in our sector, but also abroad because we are in the Dux 40. And this is not only of transportation and logistics. So let's face that. We have different peers, in different sectors, and we need to, yeah, somehow compare us with a broader audience. And here, Nasdaq helps us a lot because there I don't know the number you have in your tool. I think it was more than 10,000. And we can also use, the tool for identifying how present different companies a certain topic, different approaches from a language style or to from a, visual style. And then we use it also to add some flavor into our discussion with the auditors because sometimes they have a different opinion on our evaluation than we have. And in that discussion, we can always use some statements we find in other reports throughout NASDAQ lens. And what we I always use the NASDAQ lens for is to look after the response, from for customers. So if customer requests come in, they are sometimes not easy to understand what they're actually looking for because one question contains three or four more aspects, and you have to identify what is the actual answer you can give to that. And here, Nasdaq is very helpful, because, of course, it needs still the human being, yeah, to to identify did it find the right sources? Is it combining the right topics in the answer? But still, at least 80% of my usual work is done with that and or even more sometimes. That's nearly a hit in the eyeball a boy's eye. Yeah. So that's very, very helpful. I don't want to miss NASA plans any longer That's great. my daily great. to hear. Yeah. You've been a great partner and and shared shared great feedback along the way, and so we always incorporate that feedback and and, and look to implement as soon as possible. So maybe talk a little bit about that evaluation. You looked at a bunch tools. You mentioned some of the reasons why what you were looking for in a solution, previously. You know, I would say every company, most companies have some sort of general tool on their desktop, by, you know, many of the large technology providers. Maybe you can share a little bit about, you know, how Nasdaq lens stacks up relative to general your general corpus tools that you all have leveraged or that you leveraged today, or, you know, in your evaluation, what stood out in that valuation more broadly, as you're looking to implement a tool to, to solve your needs? As I told you already, the, the variety of peer comparison possible with Nasdaq lens. This is something we are a Microsoft company, so you can guess what, tool we are using internally. But it's it has its limits. Yeah. It helps somehow. It gives you a flavor. But more or less, I very often identify that, the sources are outdated, not picking the right topic. And NASA glance is more specific and much easier to use, especially for developing our report. Although I must say, I'm not using it to draft our report because we are only, rolling forward what we already reported. There's no we start from a scratch approach. What we did already in 2024, we tried to make our report understandable to AI. It was more or less successful, still can be improved. And, this is something we run still our internal assessment, with the AI internally, and then we challenge it with Nasdaq. So this helps a lot, to develop even further. Yeah. But, yeah, in the end, you need the human being to, yeah, to be able to to to get the right sources and right order and to identify what is really of value. And I think each and every company has their maybe a different approach. But what I can say, Nasdaq lands is really easy to use, has all the the assets I need for my work, and my work is very broad because I'm also dealing with rating agencies and with the customers, not only focusing on the reporting. And it helped me a lot and helps me a lot. It's not great. the story has not ended yet. Yeah. Yeah. And there's, I think, if if I remember correctly, there's several members of your team that use it together and some members of the finance team, and and your and within DHL, sustainability reporting flows into finance. So, you know, it's not just a a single player, effort. It's a takes a village and many of your teammates are are leveraging the solution as well. Yep. That's great. Well, that's that's our time for for for us here, Beatrice. I'll invite Victor in here. Okay. We'll have a a broader, Yep. q and a here amongst the panel, and, and we'll have, some some great questions. So Victor, come on back over and we'll, we'll answer some questions from from the field. Welcome back, Victor. So some questions from from the audience. I've got got several here that that, that that came up. One more, you know, deep in sustainability reporting and regulation. Beatrice, this is for you. And you touched on. this, you know, at a high level. You know, what are you all doing to to transition from the original ESRs that you've been reporting on already in your annual report to the revised ESRs, the the omnibus version, that are all but final? So what we are doing did I get that right? Yeah. How are you how are you preparing. as you think about the the changes. that are It's an ongoing process. Yeah. We look at each draft. Is there a single word change which maybe change the dimension of of a reporting KPI or of an statement of qualitative information? And it's a pity for us that the ESRS are still not in endorsed. And in Germany, we have we are facing the situation that at not at all the CSRD is endorsed in national law. So we are in, yeah, a weird situation from my perspective. Still, yeah, we are looking at it, and we have made up our decisions already, that we came to the conclusion an actual benefit in reducing our reporting is not there. Maybe we can reduce some information in the external reporting. But to get on the top line in the external report, we need to collect and, do all the the business we did so far, in the in the lower in the lower areas of the KPI. And so what we think at this point in time, the decision is not taken yet, but I think our report would will not really change. That's at least what we see in the KPIs. It depends what is the outcome next year on our new materiality analysis. Yeah. Will it change? Will it derive new topics? Then, of course, the report will even broaden. Did it answer the question? Or Yeah. so it's an interesting. ongoing process. Yeah. No. That was interesting. There's no was, yes and no. know, I think, yeah, companies are facing, yeah, companies are facing challenges. So I think, you know, you're you're not alone, and we're all living it through, through it in real time. Maybe this one's for Victor on on more of an an AI specific one. Someone asked, I have I have gen I have access and and Beatrice talked about this a little bit. I have access to a general purpose AI tool today, right, in my organization. In your view, what is a general language model LM do well, and where does domain specific, really step in and create a meaningful advantage? Yeah. I think that's a great great question. I can talk about it for a while. But if I have to, like, separate things to, like, different categories because, I mean, you mentioned some Beatrice mentioned some great point. I'll say data, workflow, and scale. And, I mean, every has access to same same or similar Frontier models. It's just the scaffolding, the harness that you build around the model, that makes makes a difference. Right? Like, every most people have access to to the Copilot. But I think when I look at those tools, I still think of them as a and a one off question, question and answer. Like, I think that's I think to just be more specific, let's talk about the ESRs, like, ESRs gap assessment or ESRs benchmarking or drafting. When you think about the ESRs, it's such a complicated and there are hundreds of topics, in the, like, sub sub, topic level. And as the other said, there's, like, number of, like, things that lead up to KPI. So there's, like, certain things that, cannot be solved to the the surface level. So what we do, for example, now it's like specifically, we are, like, massaging that framework where, separating into topics, we're collecting, like, breaking it down, and then we're building skills for the language models, that and the run time or the scale run, it progressively loads those skills and, for each specific topic and then, takes your company's disclosure and performs gap assessments. Right? We're so we're we're building that data, the quality, of data, and then we're, you know, like, validating the accuracy, and then we're releasing that. So data assessment. Second is, like, workflows. Right? Like, how many times you have to ask that question in the chat to be able to, analyze the gaps, of your company against the SRs. There's just so many, like, just too much going on there. So I think, like, still building UI workflows, makes a makes a big difference when you just, like, come in, you know, like, everything is already done for you, and then you go to drafting. You run the draft, and then you wanna see, like, investor perspective or, like, you know, like, regulation perspective, what you are missing. And I think there was actually a question about, like, the generated aspect of the draft versus the cited aspect of the draft. Right? Like, when you generate the draft, you don't wanna start from scratch, as Beatrice also said. You never really wanna start from scratch. You wanna load up all of your pages of disclosures from last years and then, cite them as much as possible and then look, okay. Like, there's, some new nuance about this specific disclosure or, like, this specific country disclosure, you UKSRS or, you know, Turkish. Yes. Right. The IFRS. I don't remember that. And and then you wanna, like, incorporate those nuances of the, of the framework in the draft and then assess to see, like, how it will be perceived. And then the last one is the scale. I think, like, being able to, run, this scaled analysis, you know, this gap assessments or other types of analysis for, like, thousands of companies. And then you sometimes you just don't know what you don't know. Like, you just wanna, like, look outside of your peers. You wanna look into your out of companies in your sector or just go broad through the index. For example, it's really, like, helpful to see, like, what others are doing because it's a new thing, and it's going to be, like, super new, next year. So I'll I'll say, like, this three categories, and there's a lot more to say, but, I'll I'll be the answer to this question. Yeah. No. That that's that's great. Maybe we'll we'll end here on a look to the future, and we'll come at it from a practitioner's perspective with Beatrice. What reporting process do you think will look different three years from now because of AI? And what should companies do to prepare for that shift? And, Victor, feel free as a practitioner as well, what you think goes goes by the wayside, what gets elevated, That's a tough question. teams. Yeah. Yeah. That's a tough could. wish some that's a tough question, I must say, because, yeah, I'm not a fortune teller, but, if I could wish something for. in. three years, I would wish that AI helps the governments better to understand that a global standard is needed and not a regional one, neither for The US, neither for Europe. Also, we need to have something globally, which is acceptable. And maybe it's maybe less of the number of KPIs to be reported or the content to be reported, but still that we have something which all countries can confirm this is important in the sense of sustainability, be it some points in the governance area, be it some points in this s area, and maybe a broader approach in the climate and environmental area. Hopefully, AI helps to develop something in that sense that each and everyone is looking at each other's reporting and try to adapt, and to adopt. But yeah. The global standard. Will looks will global standard or not? is I think the is the wish. in our first, ESRS report 2024, our CFO. made the statement, we need a global standard because we, as a global acting company, it's. a pain. Yeah? It's really a pain point, to deal with in Europe this, in US that, and so on. And then there you do you don't have any standard in other countries, and then you get a mixture of standards from, I don't know, a bunch of, selections. And then you have a variety to report, and this is something that you can't focus on because what is important to us, we identify throughout our double met reality analysis. And then it comes to the reporting. And customers in other areas or investors in other areas, they questioning that approach because they are relying on another standard or using a mixture of standards. And this is very difficult to deal with, to be very honest and out. Yeah. Well, let's leave it at that. That is, the great piece of insight on an area of opportunity for for the regulators for, for companies. But Victor, thanks for, thank you for joining, Beatrice. Thank you for lending your insight and, and time. We appreciate it. And to the audience, thank you for spending an hour with us. Hopefully it was informative from a, research and data analysis perspective, how to make your reports more AI readable, and ultimately hearing from a practitioner on how her and her company are leveraging AI more effectively and efficiently. So thanks again. We'll see you next time, you. and have a good, good evening. Good night. Good day. Thank you. Thanks.