AI, Data, & the End of Gut-Feel Strategy: Why Strategy is Becoming More Data Driven

Hello everyone and thank you for joining us for this webinar today called AI Data and the End of Gut Feel. Why Strategy is Becoming More Data Driven. I'm Ted Jackson, co founder of clearpoint Strategy and my very special guest today is Matthias Mangels, President and Chief Revenue Officer of Rethinking. Well, I don't want to date us Matthias, but I think we've known each other for more than 25 years working together in this strategy execution space. I'm excited to have this conversation today. Before we get started, I just wanted to remind our attendees, please submit any questions you may have into the question section at any time. I'll do my best to weave them together into this webinar. We're also recording today, so if you can't attend live or you want to share it with some colleagues, you, you're welcome to do so. Let's get started. Matthias, welcome and thank you for joining us. Why don't you start by telling us a little about Rethinking Works.

Thank you Ted. Thank you first for this opportunity to share some of our ideas, thinkings around the AI enablement of strategy management. I thank you personally and I can just say, I mean we have been friends and colleagues for so many years and things have been evolving so fast, you know, around all this data, data analys, analytics, data support and strategy management. And so I would like to thank you once more and the clearpoint organization. Coming to your question, Pat, Organizations are always under a transformation mode or in a transformation mode. So they are in a certain point A and want to evolve into a point B. So they want to rethink, organize and re structure their products, services, their say teams, organization structures, processes. And so Rethinking Works was created as a platform to support organizations in this movement, coming back from point E to point B, from A to B, thinking about say what kind of risks and opportunities exist on this journey and supporting on this platform. Rethinking Works within this journey of migrating from A to B, what are say tools that they might use, how they could control these risks, optimize the opportunities, integrate. On this platform, Rethinking works with subject matter experts. So organizations are really organizations experts that really understand the subject, can coach, can mentor, can support, but also create a community. So a platform is excellent to create a community, an internal community within an organization, perhaps a community around an entire value chain, but also creating around these communities education programs so education, competency, build out and so on. So as you see the platform is to support this migration from A to B it was enabled from the start, from the first design thinking with AI. So when I say AI with data analytics, with data structures, data analytics, and it has, I mean been from the start supported by transformational themes. So an overarching transformation theme that's on the platform is strategy management. But there are 13 other sub items to strategy management that are other transformational thinkings themes that organizations want to go through from Blue Economy to say, go to new markets, new innovation of product and service developments and so on. So they are themes that are created under this umbrella mean what will drive us forward will be strategy management. So that's in a nutshell, what the Rethinking Works platform enabling. So enabling transformation from A to B and all these different topics. That's our essence, that's our purpose as, as an organization.

That's great. Thanks for the, thanks for the deep dive for those who, who don't know much about Rethinking works. So let's talk about that strategy management space and let's talk about data, because one of the main places you find data Is within the KPIs or within the measures of a strategy. So tell me what it's like in your world helping an organization come up with KPIs now versus even five years ago.

Great. Fantastic, Ted. I mean this has been a challenge. We have been practitioners, ted, in this industry for so many years. And I can remember very well, ted, when with the Balanced Scorecard Collaboratives. And I can remember very well that when with the Balance Core card collaborative, say 30 years ago, later on Palladium, we worked on and supporting companies on designing their KPIs, on understanding the right KPIs for the right, say, balance, Corkard structure and so on. And so we used to use, let's put it this way, libraries. What kind of libraries? We had internal libraries. We bought some libraries of KPIs, we tried to collect some libraries from the market, from the sector, from trade associ. And we also created our own KPIs looking into our processes, understanding the challenges, the key success factors around these processes. So that was the way we normally say, came to the perfect or best or appropriate KPIs for our say, performance management. How has this changed? It has changed, I would say dramatically. I mean with the AI enablement of KPI thinking, structuring and so on. Today we have this opportunity not only to very quickly find out globally. Globally, I mean internationally, I mean with all different say aspects of KPIs, applications by sector, to tap and to have the KPI information that perhaps best fit, best fit our objective, best fit what we want to measure. So this AI, say enablement and even AI support and looking into the right capabilities, the right key success factors. And I would say not only understanding the lagging KPI, but much more the leading KPIs. So the lagging KPIs, very often there are some trade associations or your own company or your shareholders or your stakeholders are sort of enforcing them on you. But then all the leading ones, this possibility to tap into all this knowledge, all these exercises is fantastic. So the word has changed and I would say, I mean, just coming to clearpoint here, Ted, in a moment, you have within clearpoint, say, enabled this possibility of really helping organizations that are using clearpoint to really tap, understand very quickly what are suggested possible KPIs for a certain objective in a certain sector and so on. So the word has changed a lot and it's much easier. But we have to curate and we have to understand what are really the best, say, best fit for a certain challenge an organization has.

Yeah, I think what we've seen is the traditional lagging KPIs are pretty standard and maybe even haven't changed as much. But right when, when Chat GPT came out, we immediately built like a custom GPT that could say, put in your job title and your industry and it could tell you, you know, 10 KPIs that would be helpful. And then we were like, okay, well how could we build that into to clearpoint? And it became more of a free flow, kind of ask me anything. So you could be on an objective and say, what would be the best way to measure this? And it could look at the context of that objective within the strategy and worry about the items that just that you were talking about that are a little more tricky. They're like, what are the leading indicators based on my approach to the strategy or my organization's approach to strategy. And so we found it to be, at minimum, a great brainstorming buddy.

You.

Know, to put it mildly. Tell me a little bit about target setting as well. Have you seen the process of target setting changing in the last few years? Do, do you use AI for target setting? Like what, what, what do you see the, the impact on, on setting targets across your organizations?

Great point, Ted. Let's think how we did it in the past. I mean, we got the historical data from our organizations and said, well, when you think about the target, let's think from the historical. What could make, say, sense? Management leaders in general said well, that's not enough. We want to think out of the box. We want to think to the future. Let's do some benchmarking, let's find those that are the best and breed in our sector and so on and let's compare to those and see how perhaps we should fit our targets to those or they should inspire us. In some cases they were very inspireful. So really management really and leaders really try to put them as targets and that's the way we mostly work. So historical understanding, benchmarking, comparing some inspiration and take it as an inspiring target and let's work in that direction and let's see if we get it and perhaps we will overdo it, perhaps we will be far apart, but let's try all the best. That has changed a lot, I would say, Ted. Why? Because, well, we have had in the past all this shell based scenario analysis and even say foresighting, future benchmarking, comparing some inspiration and take it as an inspiring target and let's work in that direction and let's see if we get it and perhaps we will overdo it. Future say developments with the whole data analytics support, you can today and I can give you one or two examples, you can today very quickly put all your historical database, link your historical database with the databases of your sector with databases of say thinking out of the box, really foresighting scenarios. You can choose the scenario that you think will be the most likely one. There are early signs that that will happen. So these early signs will trigger say perhaps thinking around what's the most.

From.

The organization's perspective, the more realistic one that will happen, that will probably happen. And from their design going out from the historical data to the future targets, future possible targets. So AI has really changed very much here from a more say, from a more say, let's put an inspiring target, let's put an inspiring target. But yes, with good justifications what will possibly happen as developments and with this make it more possibly realistic, make it more say in line with your developments and with your sector, your economy, your regional developments. Give you one very quick example that, that we have been exposed to, we have been working the Emirates on designing thinking of their strategy in the healthcare area. So healthcare is an area where things evolve step by step. There are so many new treatments, there's the well being thinking and so on. And so targets exist from your historical development. And so the Emirates said well, we really want to think innovative, we want to think different, we want to guarantee that our citizens have the best global it's not global experience when it comes to well being. So what is this? So it's like you start with designing these future scenarios, AI enabled and from there you think around what is possible as being over the next five, 10, 20, 50 years, a realistic target for the well being for a citizen in the uae. So that's just one example how you can really try to make it more realistic, more possible. But for sure, looking into the past, understanding how this, how these developments, how these waves of developments are, will happen or could happen.

I was on mute, sorry, you touched on it in the beginning. But I think that, you know, AI is, is more than equipped to take your historical data and project future data. Right? So, and that's, you know, you can, you know, anyone skilled in Excel could do that even before AI, right? But it's then the ability to, to find benchmarks. You know, we, we have a utility in the US that was using clearpoint and just asked it, you know, what would be a good benchmark for this HR data, you know, within the utility sector. And you know, it just AI researched and came back and said, you know, we found this data set that's available and in your industry it would be, you know, 57% or something like that. And I think that's, you know, it's sped up tremendously. Right. Like that can be done in minutes, not in, in hours of research. And I think what one of the things that y' all are doing also at rethinking works is getting more curated databases or data sets that are, you know, around particular industries, utility sector. And you know, it just AI researched and came back and said, or particular areas that will allow AI to pull even richer data that, you know, I would say proprietary data that could pull in to help you as well with that target setting based on your, your strategy. So it's, it's very, very interesting. I think one of the things that, that, that I see is, is organizations will build a dashboard of some sort. And let me quickly define that is a visual. Sometimes it's a big monitor on the wall, sometimes it's just a web address that people go to where they have charts of different sizes. Sometimes they're even printed up back in the old Norton Kaplan days. But how have you seen dashboards change from kind of a static set of charts to something that's, that's more powerful for, for strategy management overall.

Great, thank you. It's a great vision, but a vision that is already happening is this change of these dashboards. The dashboards, they exist for decision making. Let's say we have to take a decision with management, with our executive team. In the past we had these great graphs and great information put together on a dashboard that show the KPI, historical dpi, some forecasting. In many cases too, there were some needs to forecast some of the economic but sometimes operational KPIs. So the dashboards helped us to understand historical, but also some developments into the future today. And I would even rename a little bit the dashboard and say, well, it's like when I said we go from point A to point B. A plane flies from one location to the other location and it has something like a control tower that helps it to fly from A to B. This control tower is looking into all many variables to support the pilots and the crew to have a best journey. And so when we think about the control tower mission or purpose, it's to guide, to help, to support in any, say, situation, even emergency situation. So when we think about the control tower perhaps, or the development of the thinking around say a dashboard to a control tower, it's like managing management wants to take good decisions. Good decisions. Have to have for sure an historical background, good decision, have to understand where things are going, where we spoke about scenarios, what are future scenarios? Where are my KPIs going? Where are my initiatives, programs, projects, projects, initiatives driving my future targets, my future outcomes, my future KPIs and their developments. A control tower today is more and more going into offering all this data for a best decision making. So I look at it, I'm here. It's perhaps all red. We are on the red scenario at this moment. But it looks like it can get greenish. For it to get greenish, we want to understand, well what has to be done for it to get greenish. So control tower comes to you and say, well, you know, initiative number three and initiative number five, perhaps you should push it. Perhaps that's the opportunity at this moment. Why we have and let's now really think out of the bot, we have five. Initiative number three and initiative number five. Perhaps you should push it. Perhaps that's the opportunity at this moment, why we have and let's now really think out of the bot we have five, six AI agents. Five, six AI agents scouting developments. They are not only scouting the developments around your scenarios. What's the probability that they really happen and so on and what's the impact, but they are also scouting. On your control tower you have six AI agents, five, six AI agents scouting initiatives. They are late. You need more resources, you need to support them. More leadership is not Participating, but there are also other external factors that are having an impact on developments. They are not only scouting the developments around your scenarios, what's the probability in your initiative? So these all scouting agents, they can on your control tower show you not only where things are going, but even tell you what initiatives you should perhaps push forward or really add you're not considering that they really happen and so on and what's the impact? But they are also scouting on your control tower. You are doing this at this moment or have not considered you should consider this initiative. But even more than that, Ted, I think the development of the control tower is, and we have already algorithms. One of them is just as a map, just as a name. It's called PIC from framatome in France. We have been using that one, for instance. It even tells you not only the initiatives, it tells you this initiative have to be staffed with what kind of competency for you to be successful. Initiatives, they are late, you need more resources, you need to support them. More leadership is not successful. Even more, the competency has to be of Fred and Alice and don't forget Jane, because they really understand this and also include that subject matter expert, his name is John and he is really the guy that will help you for that initiative to happen. So imagine from a dashboard in front of this control tower that's helping you really to take decisions. I see our journey going that direction and it's not participating. But there are also other external factors that are having an impact on your initiative. So these OWL scouting agents, they can on your control tower show you say fictitious future thinking. It's happening today already. So I spoke about this French organization called framatom. So they are in nuclear power. So it's a risky sort of business. They need to have all these mitigation actions ready to go at any moment, you know, and so it's proposing, proposing, proposing, proposing mitigating actions, you know.

Yes, no, it's great. It's a great analogy. The, the control tower that, that I, I picture the tower full of AI agents looking for ways to, to help in different ways. So, so we have a few questions from our audience. One is from Manuel said, what about the use of AI for validating the coherent design or the alignment of your strategy maps and scorecards across the organization, kind of thinking about it horizontally and vertically. So I'll let you go first. But we also have some things in the works at Clearpoint related to this. So I will also take a great.

So when we think one of the big challenges of say Designing a strategy, as Manuel was putting it, but also then implementing the strategy is guaranteeing that this is a team sport, that everybody is working together and that one plus one is not two, one plus one could be ten hundred or even more. So alignment is key. And with this, AI has a great can support and has a great role here. AI can spot and look at all these different agents and what are the top downs, say the deployments of objectives, KPIs and initiatives. And look if they are really coherent or if they are really enabling one another. We just did an exercise also in the Middle east around this, with the support of AI tools, really checking and balancing, so putting into the cloud the upper strategy, the strategies of several Secretaries of State, and then checking and balancing, first vertically and then horizontally, then saying, well, okay, there is this health ministry that has to collaborate with others. Are they really collaborating with transportation? Transportation at the end has a role in guaranteeing transportation for any health issue. And also the education ministry has to teach and support citizens around understanding what, what is well being and what is a health issue that you might have. So the collaboration, the horizontal collaboration can also be very strongly say enabled by these AI agents that are spotting if you miss anything, if that's not in place and why, is there a reason why it's not in place? This is a political region or is there any other, say, technical region? Why isn't allied so, so great opportunity? Manuel? I see there a big opportunity of using AI enablement for alignment.

Yeah. We've also always had inside of our software what we called an alignment matrix. You could see how all of your objectives or measures or initiatives played against each other and the ability to drill down from a goal to see everything that, that, that LinkedIn aligned to it. And you know, we've had, you know, many of our clients say, you know, they've saved several million dollars finding two different projects that were going against each other. And so we're in Q4 of this year even launching an AI kind of alignment tool to be able to look across and say, do you have, have your targets aligned, you know, at the department level that will help you reach your, your goals at the top level, you know, we'll have the ability to look for conflicts in your strategy overall. Right. You could see if you're trying to cut, you know, one process out of one side of your organization, but put the process in another side of your organization, you could save yourself a lot of effort just by identifying that overall. So we're very much looking forward to see the reaction of our customers with the ability to kind of look across departments. Most of our AI today has been within a department. So you could summarize what's happening within a department or within a particular element, but we're rolling out the ability to go farther than that. Okay, so I have another question from Osama says, does the AI help strategy people to predict when the vision will be achieved according to the current state? I'm gonna, I'm gonna start with a quick personal story about this, which I think is. Is interesting. But then, Matthias, I'll let you respond. We in 2024, at the end of 2023, we set our targets for 2024. We're really, really aggressive about it. We came short on some of those targets. And as we did our 2025 plan, this is internally at Clearpoint, we did the plan without putting it in Clearpoint, without talking to anybody about it. And then we asked AI to look at all of our past results from the past three years and to write a strategic plan for us. And. And the plans were surprisingly close. Right. And we even adjusted some of our targets based on what AI told us because it was able to, I'm going to use Osama's words, it was able to predict a little bit better than some of us optimistic, unrealistic people were able to do so. Matias, interested to hear your reaction to this similar question of how you can predict when you can achieve your vision.

Yeah, so the vision is very often very inspiring. So I give an example here. Pharmaceutical company leader in the ophthalmology area, wants to be within the first five, say, leading organizations globally, is at this moment number 10, 11. And so as a vision, we want to get there. We want to be in the far front. We want to inspire the global, say, citizens, populations around, say, what are the best treatments for ophthalmology issues, diseases, and so on. And so the vision is there and all the programs, initiatives and the KPI deployments to get their market penetration where and so on, product launch and so on. And so the AI helped, really help them to design a roadmap, you know, a roadmap, a strategy to get. But let's put it very clearly, it will probably never happened as planned. Never happened. So what was very interesting, Osama, on their thinking and on their management process is that they really mastered the continuous adjustments. So a Swiss company is selling a division that really fits very well. We did never plan for that. It was not in our radar screen. But we have the financial resources, the opportunity. It fits our portfolio. Let's go for it. So a opportunity that creeped up on the horizon and they really went for it. So my thinking there would be, I mean, you need for sure, a good design of your vision and how to get there, the roadmap. But the adjustment and I think the AI enablement are giving us the opportunity to adjust very fast, very quickly. And with this, even I would say the future thinking here is that it will adjust. The old thinking plan, do check, act, you know, quarterly thinking quarterly. It's not the case anymore. So we are taking decisions very quickly to reach our vision and to say, take a sprint attitude around strategic decision making.

Yeah, I think that's great. I'm going to do one. I have two, two questions left because I know we're running out of time. One is a little bit about, I think you, you covered it some, but it's a little about scenario planning. Right. Because I had a, a friend, I have a friend in public radio in the US who was like, I need, earlier this year was like, I need two scenarios for, for, for my budget and my strategy because one is where we, we have government funding and the other one was where government funding gets cut. And that made up more than 15% of their budget. And so it's looking at these two different plans and you were talking about the control tower and scenario planning. It's a little about the future of AI. It's like we can't predict the future, but I feel like it's helping us react more quickly maybe. What are your thoughts on kind of scenario planning and AI?

Yeah, so when you think about scenario planning, you, you create four quadrants or different, say, ways to design your future scenarios. You discuss very often, I mean, what's the probability for that scenario to happen? What's the impact? You know, and so management will look at it and will choose the scenario that thinks more likely. And with this, I mean, mitigate, mitigate whatever is possible. And if there are great opportunities, go for those opportunities, leverage those opportunities. You know, AI can help me very much not only in designing this, but in real time, optimize this at every moment. So give me hints of what to do. There is something flagging red, there's a risk coming up and so on. So the scenario design as a process, but then also the scenario management. We spoke very often about the risk maps and risks, say strategy maps actually. So, so it helps us to understand the scenarios and it helps us then to change, even to choose a new scenario. So we were really betting on that one and we were really prepared for that, but unfortunately the other one is happening. So how can we migrate from one to the other? And the AI engine is helping us with the latest data always and telling this is the scenario, perhaps this is a new scenario that's more likely. And these are even the actions that you should take to migrate from scenario A to scenario B. You know, so it's of big support, you know, I think we are, we are, as you said Ted, we are here in say big change, you know, in strategy management, maturity programs, processes and so on.

Yeah, excellent. We're going to have to schedule another two or three 30 minute sessions. We have so much to talk about. So as we wrap up, because we are out of time, we've been talking about a lot of advanced topics today, but what advice would you give someone who's just getting started with strategy management?

Ted, I would mean thinking around it first, number one. I mean perhaps take everything we just spoke into consideration, but keep it simple. Start simple, do not say overinvest in all what we just discussed. Start as simple as possible. But I would say start rich. When I say start rich means rich on your decision making. So I just come back to clearpoint and say, well, start rich. Create a good infrastructure for you management to take good decisions. Perhaps with these good decisions you will learn. So the good thing about clearpoint and about taking good decisions on your strategy and following through and monitoring them is that you will learn, learn, learn. And with this you can over time make your strategy design more sophisticated, your data structures more sophisticated. And last but not least, I would say also that you know that from our experience from all these years, a good strategy management with the good rich support, with the clearpoint as a strategy management tool will be a fantastic infrastructure to enable all of this. But do not forget the full leadership support. We know very well that it will never work out if leadership, if say the upper leadership really sponsors, engages, participates in the strategy management process, decision making and with this supports, supports this new way of thinking around generating results for an organization.

Yes, excellent. Matthias, thank you again for your time today. I'm definitely going to have you back. Really appreciate this great conversation.

Thank you, thank you all of you. Have a great one.

Yes, you too.

Bye bye, bye bye.