Market Logic at CiMi.CON Evolution 2025

Thank Thank you. Good morning, and I’m very happy to be here and speak to you about the era of always on intelligence. A bit of context, I’m a co founder and responsible for product and innovation with Market Logic. What we do is provide our customers with an enterprise platform to bring together their intelligence and insights and use AI to turn it into actual support and advice for actions and outcomes. And that’s the lens that I’m coming from. But I don’t just want to show you our product, I will use it to illustrate a bit. I want to take a step back and zoom out a bit and also talk about what is this journey that there is from we get the information until we bring it to life and activate it and help people to actually do a decision with it. And how does this information lend itself to being supported by AI? What can AI do today and how does the shape of what it can do today differ across the stages of this journey? Very early, what can it do with always on agents automatically later through the process how can it rather be a companion to the human Ultimately still we humans need to call the shots and have the ultimate say and we need to do our job still. It doesn’t do the job for us. So that’s the lens how I wanna approach this. And just starting with, let’s say the past, we know AI is a big change, big shift, a big transformation. Maybe we don’t feel it every day yet. But it slowly is coming and I’m convinced that in one or even two years time when we meet again here, the world will look substantially different. Back in the day before AI, a lot of the Challenge around what do we do with all the data we have was answered by good old knowledge management, we have to bring it together, we have to support our stakeholders, we have to make it accessible, democratize it. And that’s all nice and well, but still very limited and has challenges. Now with technology, with AI, we have the ability to activate it much better. To actually bring it to life for people who need it in the moment where they need it, to contextualize it, to augment them with this intelligence, and to help them turn it into actual business growth. That’s our ultimate goal and that’s also what we heard in the talk before. So we can really begin from, or turn from being more managers, curators of information to helping shape the future and shape the growth of the company. And well, the good old problem that we all know is in the past, well, we had always the issue of limited data access. Even if data is technically available, it is hardly available centrally in the place in the moment when it’s needed. Then that means either you need a lot of human support through experts, CI experts, MI experts, insights experts to help work through those things or people turn out to make their guesses and do more decisions based on gut feelings. Ultimately that leads to slow action, to waste to missed opportunities, to wasted spend, etcetera. We all know that. Now what we think with new technology, what we can do is and what we will see come to life now is to bring about this always on intelligence. So to bring a layer into this journey that with the help of tech enables to optimally make sense of the information. Starting from we get the information pulled into the system and contextualize and make sense of it, but then also bringing it into the moments, into the decisions where people actually need it. And while this still sounds a bit abstract, maybe the way we think about it actually partitions this in two very distinct steps. That’s also how our product which we call DeepSights implements it, and there’s the first phase which is very important, which is always on learning as we like to call it. So what can we do with all the information streaming in even before we are in the situation of a specific business problem? What can technology do other than just storing it somewhere in a database and making it searchable for later? And there’s a lot that can be done now where AI can actively, proactively already begin to make sense of data in the background. Then we come to the second step which is we have a concrete situation, we have a concrete context and challenge, and how can we now use technology to give advice and to help and support finding the best decision given circumstances. And this is of course where The AI human connection and interaction plays the role. So let’s start with the always on learning. As I said, in the past what you would do is collect all the data and then of course in a specific project context, somebody would start evaluating it and looking at it, maybe looking at dashboards, but only then begin to really think about it. But now with AI, what can be done is we can deploy what we call always on agents. This is technology running in the background automatically without waiting for anybody to trigger it, without any explicit request, but rather that continuously scans what comes in and continuously evaluates what is coming in in terms of its content. What does it actually say? What are the new things in all these data points we get in the articles and the reports and whatnot? And then not just route that into the proper database or put it onto the proper dashboard, but rather peel out all these individual facts and learnings and correlate them with all the other information that’s been streaming in and figure out how ‘s really new, what didn’t we know before, what’s maybe old news. And so we can kind of unlock the facts from the containers from their reports, dashboards, documents and begin to work at that level and unlock that granularity of understanding. So we can have AI go and hunt and scout for specific things like have it look for trends maybe in the market, maybe competitive trends, maybe products, maybe ingredients, whatever it may be, and spot them as they emerge in the incoming information, begin to profile them and always begin to then call out what is really new, what we’ve learned in this moment about this piece of information. So that sets the foundation. Now we have we have moved from a static database of data points. To an active living compounding body of information and knowledge that grows and understands what is really news and new news versus what is old news or what are changes here. This can all run on autopilot if you will in the background. Of course, it needs to be set up, it needs to be steered, needs to be reviewed by experts to make sure What it does is within the realm of your intentions, but essentially, technology can do the heavy lifting here. But now once we have that, we want to move on to the situation we actually do have a concrete business situation where we need information, where we need advice. And the Swiss Army knife for this of course is as we all know and interact with talk to the AI to help me with my problem. And it all started very simple in the past where you would go in and maybe ask a question that comes back with an answer, a bit like ChatGPT, but the difference here being that now this is all grounded in exactly that information basis and exactly the intelligence and insights and knowledge you have that you trust. So to prevent the hallucinations, really guardrail it around the mission that you’re trying to talk about. But you can go beyond that and you can use it then to also help you, for example, not only give factual answers, but evaluate ideas or hypotheses you have, give you recommendations, and again, all doing that within the context of what you know and what also your strategic guardrails are. I’m not sure how much and how deeply you engage with all those developments in AI and for example, chat GPT and the likes, but there have been tremendous developments over the past months. And where in the past you would go in, ask a question, get an answer, now you have those reasoning models as they are called. So basically the AI just doesn’t respond off the bat, but it starts to think first and think means really it speaks to itself, if you will, which you don’t see necessarily, and tries to come up with a plan. What do I need to do now to satisfy this this task I’ve been given? What What sources should I look at? What steps should I go through? It makes a kind of mini plan of what to do and how to accomplish what you want from it. And while today these are still relatively Short, typically these tasks, the technology is now there that this become really rich and and you can go in and give it a mission to say maybe, oh, I have a conversion problem with that brand in this market. Go in, find hypotheses why that is. Go in find potential ideas what we could do about it and then the AI may be thinking for half an hour, an hour and come back with a long and very elaborate report for you that of course We still, as the human, have to look at, have to appraise, but it can do a lot of heavy lifting that previously would have taken humans days and weeks to accomplish. So there’s a lot of capability just in this kind of Swiss army knife on demand interaction you can then use either as the expert or the business stakeholders themselves can use to help them. And that is something that’s very flexible. However, there’s also very specific use cases and very specific pockets of problems where you have now AI solutions that give, if you will, bespoke approaches to help. One very current and exciting topic here is to use AI To emulate personas. So you may need to talk to a certain consumer of a certain archetype or a certain segment or you may need to talk to Maybe what can be done now is either based on segmentations or or UNA studies or on transcripts from video interviews, personas can be built and can be seeded into the AI so that you as a human can begin to have a conversation with this kind of virtual consumer retailer or the stakeholder that’s relevant to you. That of course unlocks a lot of use cases. It’s not only for inspirational interaction, but you can now begin to also get initial directional feedback on ideas you have. How would that kind of messaging maybe resonate and what would be common points of feedback from a certain segments, better understanding habits and attitudes. You could in the pharma space for example use it to talk to your healthcare professionals and understand how they think and maybe what the best channels are to engage them etc. And you can even go in and have kind of your virtual focus groups, virtual discussions, always knowing of course, this is not the replacement for reality, but it’s a super quick and easy tool to iterate and to get much closer to a good solution that you then would be testing. In the real world. A very powerful capability that’s also very approachable and very versatile for a number of use cases. Another way of having always on intelligence in the sense of you can go there twenty four seven and you want to talk to that consumer and you want to get feedback or you want to ideate and that’s perfectly possible with AI now. And now the last shape of advice I want to briefly touch on is that there are also other situations where you have a very prescriptive, clear framework how you want to accomplish a certain task. And one flagship example would be innovation. When you do innovation, you typically have a playbook. You have a certain journey, how you go about it, how you formulate your initial briefs, how you determine the insights, how you then maybe identify innovation territories, etc, and go through that journey. And that of course requires the human to be in the driver’s seat. But what AI can do here is then help in an experience that’s really tailored, that fits like a glove, this innovation process for example to keep the human team, of course in the driver’s seat to make them manage the process, to decide where to go, but at the same time leverage AI to help with ideation, to help with feedback and analysis, with improvement ideas, with creative generation, engaging personas again to get some initial feedback and so on and so forth. So a lot of the heavy lifting can be automated while still giving now the ultimate taste, experience and creativity of the human the tools to wield for maximum effect. So that is another way of bringing AI into the journey, into the value creation chain while keeping the human empowering the humans, not replacing them. So we don’t believe replacing humans is the way to go here. AI can run at the very front end to do initial analysis, but when it comes to making decisions, of course we want to be the ones who are informed by AI But who make those decisions. And now this is all not Just a glimpse in the future. These things exist and these things are being used and deployed. Also to strategic effect. It’s not just the tool to be more efficient, to save a little bit of money or time, even though that is a key aspect, which we see here in a case study with our customer Novartis for example who have been using this and already in the, I want to say, versions the worst versions of AI that we had a year ago or two years ago, there’s a lot of benefit that can be reaped from it. Benefit in terms of reducing duplicative spend and acquiring information that you find out you have already. Greatly increasing use of the assets you have in insights and intelligence, saving a lot of time, people being faster, getting faster time to insight. One highlight project that I always love to talk about is we literally had an AB test with two teams with the same mission of finding what kind of packaging is the best packaging for a certain drug in a certain market. Team A went the traditional route, made some dedicated research, spent ADK, had a certain sample size, took three months, team B did the whole thing with AI, didn’t spend a single dollar, had a much more robust sample size because they found all the data was there already somehow, and was done basically in a few weeks. So there is a big lever here and it can make a big difference both operationally but also in terms of driving, for example, the commercial strategy as Ian from Novartis says here. And that’s not only one singular use case, but that has also been now validated by Forrester, so there is significant ROI, but there’s also it’s not only about efficiencies, there’s also effectiveness, there’s also business growth impact that we see. So it does make a difference. It is harder to quantify, but it helps drive exactly better outcomes, competitive advantage, and that in turn manifests as business growth. So there’s a lot of potential here. There’s a lot of capabilities that are available to help bring this always on intelligence about, but also we have to be clear, it’s not only about technology, it’s also about finding the right ways to bring them into the organization, finding the right ways of governance and steering that to really make use of it, to really enable the teams to make use of it. So there is a journey, is a change management journey, but it’s a journey that is I believe, inevitable for all of us in all aspects. And I think it is, if you haven’t started yet, high time to engage With thinking about the strategy of how to bring this to life in your organization. So of course, we are happy to show you our solution to this challenge. So please come and see us at the booth if you want to and we’ll happily demo to you how this can look like. But other than that, with this I would conclude now here and open it for any questions that you may have.