Industry Report Webinar: Hyperscaling Insights Impact

Hello, everyone, and welcome to our webinar, hyperscaling insights impact. It’s a pleasure to have you guys with us here today in our final webinar of the year, co presented by Insights Platforms. My name is Carolyn Woods from Market Logic, and I am just gonna do a short introduction before I hand it off to today’s speakers. So as I said, this is a co presented webinar with Insights Platforms. Specifically, we are focusing on a recently published industry report, aptly named hyperscaling insights impact. So first up in the agenda will be Mike Stevens, the founder of Insight Platforms, who will be, covering this report, at which point, we will bring in Olaf Lentzmann from MarketLogic Software to give his perspective on it and open it up into an audience q and a. So, a tight agenda here. I should have everyone out in forty five minutes, but we have a lot of ground to cover. So without further ado, I will invite Mike to unmute his microphone and take it away with the survey presentation. Thank you. Thanks, Callie. Thanks for the introduction, and, thanks to all of you for being here. I hope this is gonna be, stimulating and informative. And this is a collaboration between, MarketLogic Software and Insight Platforms. For those of you who don’t know Insight Platforms, I lead this, business. It is really a a knowledge hub and repository for all things related to insights and technology where those things come together. And my background is I spent a long time in b to b research, so I understand this, that the market the complexity of conducting this type of survey quite well. So just a a brief introduction about this idea of hyperscaling insights. It’s like, well, you know, why is it? It’s we really we landed on this because there seems to be growing demand for insights. There’s, you know, a thirst for knowledge, for data throughout organizations, and there’s often a bit of a a kind of bottleneck challenge in getting the data to the right place at the right time. So we wanted to understand those users of insight, not so much the people who are collecting data, analyzing it for their full time job, but the people who are at the coalface, marketing teams, brand teams, product innovation teams. How do they use insights? How do they want to work with data and evidence for decision making? And, you know, what are the opportunities and the challenges that they face? It’s not easy to get good quality data for this type of thing. You know, you can’t just go out and post polls on LinkedIn. You need to be quite structured about this. So, you know, we’ve taken a very methodical approach, as you’ll see on the next slide, to how we designed the sample, the data collection, and the survey. This was an online survey with a couple of hundred people. And how this was done, we partnered with a company called Emporia Research. Emporia is really a hybrid between a high end business to business panel and, expert network. You might know GLG, those types of people. They target people very specifically on LinkedIn based on job titles and profile in order to fulfill your sample requirements. So who do we speak to? We wanted to speak to people in brand and marketing roles and in product and innovation roles across North America and in Europe and in large and very large enterprise businesses. So you can see here, you know, the the way that we broke this down. We got pretty much what we were looking for. Personally, I’ve done a lot of work in b two b. Very happy with the quality of the data that underpins this. It’s always really important. People gloss over this stuff, but it’s so important to, you know, to be transparent and to share what this is based on. Otherwise, you’re, you know, you’re just drawing conclusions out of the air. Now the breakdown of the types of companies, you can see very much consumer focused, lots in CPG, retail, media, entertainment. This is really the focus that we wanted to understand because it’s consumer data, consumer insights. That’s really the the main currency that we’re talking about, we’re dealing with. So, hopefully, that’s given you a sense of who’s inputting into this project because, you know, you need to know, who you’re actually paying attention to here. Okay. So what did we find out? Well, we asked them all about their use of insights, how much they rely on it, the share of decisions that they make using data and insights, and then what are some of the barriers and opportunities to, yeah, using that in more, more occasions, in more depth. What’s maybe reassuring for those of you who work in insights and data is ninety percent of these teams that we spoke to say that consumer research data and insights are either you know, they’re either totally reliant on them or very heavily reliant on them to do their job. So, you know, insights are highly valued. I think that for decades of trying to shift to being consumer driven, customer driven, user centric rather than, you know, product driven has really landed. You know, most people who work in these types of roles, marketing, innovation, recognize that you need to build, you know, communications or product with a good understanding of the target audience and the consumers. Now what’s perhaps a little disappointing is that even though those people say this stuff is really important, only about sixty percent of their decisions are informed by data and insights. So, you know, the other forty percent effectively are flying blind, going according to gut feel, maybe influenced by the highest per paid person’s opinion, done on historical precedent, whatever it is. You know, there’s all sorts of reasons why data doesn’t get used. Not enough time. The data’s not in the right place. We wanted to dig into this to understand, you know, what’s really going on because it’s more of an issue. It’s more problematic in product and innovation teams than in marketing teams. Now, you know, we defined what we meant by, you know, data insights consumer research, you know, quite thoroughly. But we also asked people to define what they meant by it as well. And you’ll see in a minute the different sources and types of data that these different teams are using. But, you know, about two thirds of brand and marketing decisions actually use data of some kind, whereas it’s really more like around a half for product and innovation. There are, you know, understandable historical reasons, I think, for that. If you look at the way that consumer insights research has developed over the decades, a lot of that was a function of marketing, trying to understand and support and, you know, reporting lines within organizations into marketing teams for for consumer research and consumer insights teams. For product innovation teams, often, those processes feel can feel a little bit different. They can be product driven. They can be, you know, this is our expertise and our capability and our, you know, our experts know what needs to be built for the market, not often as well informed by data and insights. But in some cases, partly a structural reason that the research and insights team hasn’t really built itself around the needs of innovation teams in quite the same way. Now a little bit, small. I think that you can maybe zoom in on this, with this, application. But what’s interesting is the types of data and insight that these two different teams are using. So the purple bar is marketing and brand teams. And from the top, you can see very heavily reliant on analytics, metrics, operational performance data, CRM. And if you, you know, you see what’s happened to the MarTech landscape. You see what’s happened to the kind of datafication of marketing with digital teams over the last fifteen, twenty years, what you’ve got is an enormous number of metrics and dashboards and, you know, and KPIs that are tied that are very, very performance based. It may be open rates. It might be click rates. It might be all of that kind of digital data. That really skews the, you know, the the data and insight sources that are being used by marketing teams. When you go below that, it really starts to drop off quite dramatically. So, you know, you get down to the sort of competitor intelligence surveys, customer panel data, qualitative research, much, much lower usage rates than that accessible digital type, measurement data. Innovation teams, product teams, actually a very, very different story. It’s almost not quite, but it’s almost flat. If you look down that, series of red bars, they’re almost all within kind of twenty percent of each other, suggesting a much, much more diffuse range of data sources for product development and innovation. So product teams are talking to customers, doing qualitative research more often. They’re consulting competitive intelligence. They’re going and, you know, looking at survey data for feedback in order to drive their decision making. So it’s a mixed message. You know, you’ve got product teams who are not using as greater share of data and insights in their decision making. But when they do, they’re using a much broader spread than marketing teams. So, you know, you could you could arguably, pick your evidence to support which of those teams you can say is more insights driven or or consumer centric. But interesting challenges for people who provide either advisory services or insights and data within organizations because these teams have different behaviors, different requirements, and different ways of working. Now what does bring them together, they both want to use more consumer data and insights. They’re both eager to drive up that share of decisions. So we ask them, in an ideal world, what share of your decision would you be using data and insights to to inform them? Now with the best will in the world, with the greatest systems and the best visibility, it’s unlikely that a hundred percent of decisions will ever be taken taken that, you know, rely on, data and insights. There’s always gonna be a, you know, a subset that just don’t need it because of the nature of the decisions. But there’s a big growth opportunity here, something like a twenty point gap between where these teams are now and where they aspire to be. So sixty seven percent today, brand and marketing teams using, this, you know, data for decisions, they wanna be in the mid eighties. Mid fifties today for for product teams, they wanna be in the mid seventies. So that is a sizable gap. You think about the effectiveness and, you know, how that could improve the quality of decision making by closing that gap. That’s an enormous opportunity and, you know, quite a a nice aspiration for CMI teams, for research teams, for providers of data, you know, as, you know, either the data itself or the platforms that enable it to try and close that gap. So, you know, there’s no doubt these teams, when you ask them about if you could up the share of decisions informed by data, that it would improve business performance. We asked them across the board, what are these, you know, big key indicators of of commercial performance? Would you deliver more effective advertising? Would you grow market share? Would you increase your product launch success rate? All of those things. You can see massively for both teams, they’re all saying, yes. Yes. Yes. We would be more effective if we were able to unleash the power of insights for more decision making. So, generally, strong agreement, more data into decisions, better decisions, better commercial outcomes. You know? It’s, it’s reassuring that these teams think in this way. But, you know, maybe that’s not really the new news. The challenge is how do you get there? How do you get beyond the current status quo to up that share of decision makers? There are some big barriers that stand in the way. So those look a little bit like this. And if you group them, the majority are systemic. You know, we hypothesize that it could be about culture. We hypothesize that it could be about, you know, workflow interactions with different things. In reality, what really bubbles up is not the stuff so much about skills or about drowning in data. It’s about getting the data out of these different places into one place. So data’s in multiple systems. It takes too long to find and access the data because of the different formats. Maybe some of it’s in a PowerPoint. Maybe some of it’s in spreadsheets. Maybe some of it’s in paper copy. Who knows? There’s lots of different ways where, you know, teams need to draw that data together in order to build a robust decision making architecture. But, you know, too many formats. So trying to reconcile, trying to wrangle data, we’ve all been there. We’ve got conflicting, sources that we’re we’re trying to reconcile. So those are the main challenges, and those are systemic. Those are, you know, data, technology, structural challenges. Much, much more important than things like just sheer overwhelm with too much data or having the the practicality of having to manage external vendors or not having the right kind of skills. So data silos and systems, by far, are the biggest set of barriers to driving more effective decision making with data. It’s different, though, between the marketing and the innovation terms. This, I think, is quite striking. You look at these gaps. Where there are the big gaps? So data’s in multiple systems. The brand and marketing teams, we know they’re overwhelmed with their MarTech stacks. If you look at the any any of these surveys, any of these measurements that come back from these billing, you know, consolidators, it says underutilization is a massive challenge for the marketing tech staff. Lots and lots of systems, lots and lots of data, not enough kinda integrating it into one place. That is you know, that’s a big issue for for marketing. So not surprising that the, big gripe is it’s in too many different systems, too many different formats. Innovation teams, interestingly, if you look right down the bottom right, our team lacks the skills to use insights effectively. That’s, you know, double the rate of people saying that in product teams compared with marketing teams. So there’s maybe an education job to be done for people who work in analytics and insights and data research, focus those efforts on educating and upskilling in product and innovation teams, give them the confidence to be able to work with more data. But there’s also a structural challenge where those teams are much, much more likely to say the research team, the CMI team, is a bottleneck to getting access to the data they need. So apologies to those of you who work in CMI, and you I know you have a million different masters that you have to service, but the product teams, by and large, feel it much more than marketing teams. And that may be down to the same historical factors where CMI teams, research and insights teams were reporting as part of that same organization. So, you know, there are differences here for people who want to design an organization that’s going to drive effective, you know, hyperscaling of insights and data. These two teams actually require slightly different, approaches. So we didn’t set out to, prove this point, but it did come back quite strongly that those organizations or those teams that have access to insights data in a unified platform and an ecosystem actually find it easier to make decisions, find it quicker to find data. So, you know, we asked people what they wanted, and this is there’s some really rich data here from the open ended feedback. We asked people, what changes are gonna help you drive, you know, more, decisions from from data? And bubbling up to the top, the counterpoint to the barriers they identified were all about centralising data, making it easy, being able to visualise it in one place, you know, being able to, in some cases, automatically generate insights. You can see there. A generative AI assistant for insights coming up as a small subset. But still, spontaneously, some of these recommendations that are about connecting data, making it accessible in one place, integration is by far the biggest demand that people have because that disaggregation siloed data is a real problem. But we also were able to cut this data by those teams that had access to a knowledge management platform for insights who were regular users of knowledge management systems, specifically for insights. Now the frequent users, that’s the big purple blue blob there. They’re people who are using, insights platform for knowledge management at least every week, often every day. That’s compared to the rest of the sample. So you can see there, there’s a quite a substantial uplift, you know, another, a good kind of ten or fifteen percent improvement in the share of decisions that are made. But what’s interesting is on the right, this barrier that a lot of people identified with, the speed of being able to get hold of that insights data, if they’re frequent users of knowledge management systems, much less likely to say that it’s too slow to get hold of that data by, you know, by about half as much. So, you know, speed of access, more effective decision making, it kind of proves the point that if you can connect things better, now you’re going to end up with, with better commercial outcomes. Now this is really just splitting all of the uses of all these different sources of insight by those two groups, the people who are frequently accessing data through an insights platform and then people who don’t. And you can see just the frequency of activity, the use of all these different types of data just goes up. Now there’s a kind of, you know, correlation causation thing. You know, maybe the teams that are more likely to use lots of data are more likely to centralize it through a system, or, you know, maybe putting it into a system can help people use a bigger variety of data. There’s probably truth on both sides of that, but they go together whatever the direction of causality might be. So what does this suggest for anyone who is in, you know, an insights data enablement role? There’s lots going on here. I think the strong message that comes back is just how much demand there is for for consumer insights. I hear this all the time. I work with lots and lots of different teams, insights teams, agencies, technology providers. They’re all saying the demand is huge. Enabling and if, you know, effectively supporting that demand is the challenge. So it’s there. People want to be able to use insights and data for decision making. The biggest challenge is less about skills, less about culture, although that exists. It’s much more about systems. So connecting, integrating systems and data so that you can have access to what you need at the point where you need it. And then it does look like if you can enable that, if you can put in place an ecosystem that connects those different sources, it appears to drive more use of data for decision making and more sweating of your assets, more use of the insights that are already there. So to cover some some sort of biggish implications. If you download and, Carrie, I don’t know if we’ve got the link in here or if those of you in the audience have have had this, white paper already. But in the white paper, there’s a lot more depth on both the data and the, implications here. But, you know, if you want to move forward by, you know, centralizing insights in whatever way, trying to draw things together, it’s important to do it not just by going, oh, you know, let’s get a bunch of vendors in and let’s figure out what they tell us. Take a, you know, a a a deliberate business transformation process to ensuring that you’re doing it in the right way. Figure out where you are now. So what are the common sources of data that you use? What do people want to use? How are they interacting with that data now? What metrics can you establish in order to set a baseline that you can determine improvement from in the future? Then figure out what are the decision points? What are the points where those stakeholder teams, business users really need to make use of data? Is it about annual brand plans? Is it about campaign launch? Is it about different stage gates in the innovation process where data is needed to help solidify, reinforce decision making. Figure out where those points are and ensure that you can map back to where the data is going to come from. And can it be injected directly into the systems that they’re using? Can you use an API? Can you integrate and make it usable at the point of decision? And then map those so that you’re prioritising which data sources which insight sources you need to connect initially. There’s so much choice. There is an awful lot of data that’s out there now. The website that I run, we list about sixteen hundred different tools, technologies, data sources for insights. There’s a lot more that we don’t document. So it is big. There’s a lot of fragmentation. You want to be able to tie it together, but do it in a way that’s prioritized. And then, you know, create a roadmap for change because this is about process change, people change. It’s not just about implementing some tech and saying, off you go, it’s all available. It’s about making sure that you’ve mapped the users’ workflows back to the tools that, you know, you’re gonna enable and that you’ve got a sensible, realistic roadmap for getting from a to b. Don’t expect it to change overnight. You know, then it’s a question of actually connecting, integrating. You’ve got, you know, your rollout planning, making sure you’re socializing, communicating, giving people the help and support they need, and building case studies using the data you gathered right at the start, those metrics to go, okay. We were here. Now we’re here. Being able to demonstrate that you’re growing the share of decision making with data is gonna be a powerful thing. Using success stories, different teams, they weren’t able to do this before, they are now. Making sure that you’re showcasing and, you know, taking that stuff to the wider organisation as you roll it out. Okay. So, you know, the whole point here is that, you know, consumer insights is not a department. You know? This is like a kind of lifeblood. I’m I’m probably preaching to the converted if you’ve joined this, but, you know, insights and data are the things that are gonna power effective decision making in organizations. You know, the organizations that really understand that do this very well. If you think about the most successful businesses, they live on data and feedback mechanisms and understanding the customer in, you know, very pragmatic, fast, integrated kind of ways. So, you know, insights are all about strategic capability that’s gonna flow through to create the tangible value. Those examples we’re talking about, more effective ads, more, you know, product launches, and that kind of thing. So if you’ve got any questions, then please put them into the little q and a tab there. If you wanna put them in the chat, that’s also fine. We’ll figure it out. But I’m gonna have a chat now with Olaf Lentzmann. Olaf is chief product officer, chief innovation officer, and cofounder of MarketLogic Software. And he’s gonna give a bit of a perspective from the from the coal face, as it were, about some of the data points that we’ve been talking about. Olaf, just do you wanna just say hello? Tell us a little bit about, you know, what you do in that in that role of expand your job title. Yeah. Excellent. Thanks. Thanks, Mike, for the introduction, and good afternoon or maybe good morning. So very happy to be here with you, and thanks for for taking us through those results, which I think were quite quite interesting with quite a few nuances that were not hundred percent expect expected at least for me. So, what I do well, we, we have established MarketLogic, a long time ago, more than fifteen years ago, in fact. And we are we’re working with many of the biggest brands in the world to help them kind of address parts of exactly this problem, how to how to bring insights to life. And, my role to a large extent is about also working with customers more on the forward looking more, let’s say, strategic innovation topics. How can we use now technology and especially all the wonderful new AI technology that’s around, to really unlock insights in a new way which was not possible for for various reasons before, and how can we actually rethink, this not so much from only a technology perspective, but really from as you also explained it, from mapping the decision points, looking from the business angle, and then going backwards, working backwards to how how can we reach those decision points and what’s really needed and what’s the best ways to to do that. And it’s it’s a very exciting time to be working on that. Yeah. That’s great. So so first question is, do you recognize this insights gap, I guess, with the organizations that you work with? And how can the sort of AI that you’re talking about help to close that gap? Yes. Of course. I mean, that is, to a certain extent, the the reason for being for for our company, the existence of this very insights gap. And we see that simply by virtue of the fact that our customers tend to have a lot of insights, a lot of information, and they they really struggle to bring it to life. They really struggle to bring it the last mile down to the moment and the situation where it’s needed. And typically, on the one end, that means you have systems, hopefully in players that that try and help with that. But also, let’s say the pre AI systems had challenges simply due to the fact that it would always require explicit engagements, being whether I as a user need to know somehow navigate to a dashboard, wait for it to load, find the right cuts, to to get my data, or go to other systems to search and and scan for insights. So it’s really a lean in activity you need to do in this kind of self serve. Or on the other hand, if you wanna, of course, engage with, the insights team, the experts, and get their their expert advice, that’s important and great, but it doesn’t scale. So, therefore, we really see that, there is still to this day quite a quite a demand. And, I think what we see is also that now with the new, AI capabilities, there’s new angles of tackling that because on one hand, it makes interaction with all the information much more intuitive. You don’t have to mitigate complex technical structures, but can just essentially ask what you need. It also makes it much more easy to make this information available in different situations and moments. And so not necessarily that people always have to go to a certain system, but it’s much easier to integrate it to existing workflows and tools. And lastly, also AI is so much better at understanding your context. So it can even then try to not only deliver the data, but also overlay them and and map them to really specific angle of of the problem you’re trying to solve. And, again, that goes then back to mapping the decision points and coming from the from the problem. Yeah. I like that concept of the last mile, you know, being able to actually make this stuff work practically. Mhmm. It’s, you know, it’s something that you hear a lot from the business that, you know, the the insights are interesting, but not necessarily, you know, applicable in the way that they need them to be. Does AI help with this kind of, you know, insights ROI? Can it help to measure or capture or or document that in some way? It is, of course, a very the holy grail elusive question always. Other one, obviously, AI helps on on the pure quantitative sides when it comes to attribution models and other things. But when it comes to the more bigger fundamental, how how did this fundamental study help drive better business, that remains tricky. But I think we we have a few new angles here because, again, we now we can better understand what the context is, why people are looking for for something, what they’re trying to accomplish, and also later than in the life cycle, try to try to tie them back and link it back. But it’s still rather qualitative steps. But also, it gives a much clearer picture, I would say, from the inside out perspective in terms of how are the insights that we have, how are they really being used and by whom, and then what situations and context of which portions of my assets are used and which are not so much used. And that, I think, also helps a lot to sharpen, the view on what’s really required for the business and to better align maybe what is produced, and then what the focus of the insights are and what else are the needs of the business. Yeah. Yeah. Makes sense. We in the the survey, we chose to speak with product innovation teams, with brand marketing teams. It was a you know, they’re kind of big users. And, oh, yeah. Can’t speak to everybody. It’s kinda gets expensive. Are you seeing other teams starting to get hands on with with insights and data beyond those kind of big user groups? Yes. I mean, these are, of course, the the core constituencies, so to speak. But there are some other groups as well. Of course, if if you look at more strategic planning, that that also requires any weight in science, but can also more tap into these, systems then. There’s also r and d, which is, like, one step beyond the, let’s say, product innovation perspective, who can also now benefit from from this angle in a more accessible way. We see that somewhat more. And another interesting aspect, for example, for for CPG customers is also commercial teams, trade marketing, and now you can begin also new ways to maybe exchange information with your partners, channel partners, which was always, another aspiration that everybody had to also, jointly understand the customer and the shopper better. But now also here, slowly, there seem to be new avenues of making that more efficient and effective is intact. Yeah. Okay. Well, something with, with the, I guess, the friction around AI systems. I think, you know, maybe in the the early days, particularly with generative AI, a lot of anxiety, a lot of sort of legal compliance, IT, you know, barriers to negotiate. Is that still the case? Do you think that insights teams or the data teams, is it getting easier for them now, or is there is there still that that kind of challenge? Yeah. I think to be, fair, that’s the one area where AI maybe has made life more complex for the inside stimulus before compared to before. Specifically, also in conjunction with the emerging AI, Gen AI initiatives that most, customers would launch that, of course, then seek to roll out AI benefits across the organization. And then it’s on the one hand, it’s, of course, easy to get, I would say, lost in all the possibilities and and all the tools and, vendors and and things that are out there. And on the other hand, there is strong well, strong, maybe not always strong, but there’s sometimes a tension between what a specific functional department like Insights needs and a more central IT perspective who, of course, attempt to solve this, let’s say, AI revolution from a different angle and who seek to employ horizontal solutions. I think important is, in general, but very specifically also for insights to to really double down on what is the strategy the AI strategy for the function you need and what do you try to accomplish. And then from there, see what’s the maybe technical components you need or do not need, and really rather look at it from a holistic perspective. But I think there’s a lot of opportunity with that holistic perspective as well to to link it into the bigger organization now. Yeah. Are there the insights teams, are they, you know, are they starting to play a role in data governance in in organizations a bit more? Is that is that one of the sort of the new things that they’re having to adapt to? Yeah. I think there is a big emerging opportunity for that, also with tech again because it it will begin to I mean, technology helps now to, let’s say, scale out the reach of the insights. And that’s a big opportunity because the technology can also be used to basically imprint your governance and best practices and ways of working with the data. On it. So for example, maybe, yeah, give some guidance on what what data sources are to be used for certain types of questions or how certain questions are to be asked in the first place. Things that also were part maybe of the insights gap that there was a very clear and deep and expert level understanding of how to look at all those problems, which was difficult to then maybe scale and transport to all the stakeholders. And now technology can help also, I wouldn’t say automate, but but, codify part of that. And therefore, I think there’s a huge opportunity for insights teams to, put a little of their ways of looking at the world and the best practices of how to use work with data into the technology to make sure everybody benefits from it. It does it that way. Yeah. Okay. Interesting. So almost embedding that expertise into the guardrails in the products or best practices or templates or Right. And that doesn’t mean to say, of course, that I mean, the the true expertise remains with the expert, but it’s like the the the fundamentals of how how you work with insights you can really put in. Yeah. I’m conscious that I’m the only one asking questions. I know for those of you in the audience, if you’ve got any questions for for Olaf or questions about the the work that we did, the the report, please shout. You can post them into the chat, into the q and a. We’ll try and make sure that we get to them before the end. I had I guess I had a practical question about you know, you talked about the bigger AI initiatives and some of these horizontal programs or or capabilities. If you’re in an insights role and you want to trial something in a pilot, like, you know, like your DeepSights and knowledge management solution? Like, how do you how do you go about that? How do you make that happen given all this complexity? Yeah. I think the complexity maybe is more in in figuring out the right approach and then following through with it than in the actual operational execution. Again, thankfully, AI is very quick and easy to deploy. Therefore, running pilots is a very low overhead activity from your technical and organizational perspective. Perspective. But I would say in doing so, what one should absolutely focus on is think about, first of all, what kind of data you wanna have in there and have a representative set to try that and to have a valid feeling for, what you get out and also look at what are the questions you really need to answer, what kind of insights or knowledge or you wanna get out, what kinds of decisions you want to support, and what decision points you wanna be, and how you wanna technically get there. So have really a a real world mini lab scenario that you can go through. And then I think it’s also about drilling deep in this in this exercise, because AI products have the beautiful aspect of it’s very easy to produce something that looks grandiose and impressive, but it may not always be correct or or fully grounded in reality. So it’s really important to to do that check and the deep dive, and to convince yourself once, of what it can do, what it cannot do, and have a great good feeling for for the limitations and the capabilities of technology. Yeah. Yeah. So AI is AI is not gonna threaten too many jobs right now. It’s it’s gonna push people to verifying the, accuracy of the outputs, I guess, in the new roles. One last question for me because I’m, I’m actually presenting in a couple of weeks’ time on, trends in the industry for, you know, what’s happening, next year. So, I’m gonna shamelessly pick your brains and see if you can, can give me some useful content for that. What you know, what’s where is AI going, I guess, in, you know, in the next little while for insights? Well, I mean, of course, there’s amazing number of directions there, especially also research tools. In our space, however, when it comes more to well, classically, it was more knowledge management, but not working with Insights, bringing them to life, bringing them to the decision points. I think the biggest thing we will see for sure, everybody will call it agents. That’s the word we will not be able to, avoid next year. I think what it means in reality is not that we will start and keep the AI tasks and expect it to solve them by themselves by itself, But rather, we will see, I guess, more and more complex cookie cutter standard work packages being done into end by the AI. Like, wait. Today, if you think about JetGPT, you ask a question, you get an answer. Or to our solution, you get a much richer and more detailed and more tailored to insights answer. But I think the next levels will be that you get full presentations according to your best practices of how you actually want to have them done. And that, again, will not take the job away of anybody. It will rather help people to get to eighty percent of the results in twenty percent of the time or two percent of the time, actually. So freeing people to to put more focus more time on the results strategic value adding activities instead of trolling through documents and collating charts. So I think that is going to be the next level. Of course, things will evolve from there. But, again, I think it’s also a super great opportunity to further codify methods, best practices, and to scale them out, and to make sure that things are done consistent with best practice ways. So k. Lightweight agents, I would call them maybe. And and that’s, I guess, what we will see. Great. And Okay. Well, thank you. I’ll, I’ll make some notes of my own as we know on that one. It does not like our shy audience is gonna be asking any questions for, other others. So, I think we might be close to wrapping it. Ali, over to you. Yeah. Thank you so much for that conversation, guys. Very, very informative, and I think it was a nice segue off of the, the key takeaways, that were provided in the report. You know, how to take these, statistics from product and marketing teams and, actually action them into a framework. So a lot of good stuff in there. I will share the full report once again before we wrap up. We only have a few minutes anyway. And, yeah, I think this is a great way to sort of wrap up twenty twenty four. If you want some light reading over the holidays, please download the full report. There it’s it’s, quite comprehensive, and it will feed a lot of conversations that we will be continuing to have in the New Year. So with that, I think I will just say thank you again, to Mike for sharing, your findings and Olaf for adding a bit of contextualization to that. It was very interesting. And to everyone joining us, thank you so much. Have a happy holidays, happy New Year, and we will see you for more webinars in twenty twenty five. Thank you. Thank you, guys. Bye then. Bye bye. Bye.