Heidelberg Materials’ Dominik von Achten on AI’s Ultimate Advantage
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Heidelberg Materials’ Dominik von Achten on AI’s Ultimate Advantage
Heidelberg Materials’ Dominik von Achten on AI’s Ultimate Advantage -
Reckitt’s Kris Licht Believes AI Transformation Starts with People
Reckitt’s Kris Licht Believes AI Transformation Starts with People
Heidelberg Materials’ Dominik von Achten on AI’s Ultimate Advantage
A conversation with BCG CEO Christoph Schweizer and Heidelberg Materials CEO Dominik von Achten
Heidelberg Materials, a global giant in heavy building materials and solutions, recently embarked on a bold effort to future-proof its 152-year-old business. A key part of this journey is embedding AI into its core physical operations, including plans to roll out more than 100 autonomous vehicles by 2028.
CEO Dominik von Achten is leading this charge, taking a highly scalable approach to how AI can drive top-line growth and secure a competitive edge. The following interview has been edited for concision.
Christoph Schweizer: Dominik, technology and sustainability are central to Heidelberg Materials’ impressive results. You have a very particular approach to growth. Tell us more about that.
Dominik von Achten: I think for Heidelberg Materials, our approach is first profitability, and then it’s transformation along two lines: sustainability and automation through AI. I think we have to secure the first to get to the second. The industry had some challenges in the past, in terms of being a big carbon emitter and being one of the most offline businesses on the planet. And where there are challenges there are big opportunities, and at Heidelberg, we grab them.
You say you are one of the most offline businesses out there, yet you decided to take on AI with an enterprise-wide approach. What drove that decision, and how is that working now?
We traditionally very much thought in pilots, but we realized that if you really want to change things you need an approach that’s scalable. There are big pockets along our entire value chain primed to be addressed with automation through AI. We can apply it everywhere—starting in the quarries, moving through the labs and production sites, and extending to logistics, sales and distribution, and the customer interface. The upside of being so offline is that we have countless opportunities to implement AI. We pilot, but with an immediate plan for scale. For example, in North America, we now run 12 plants remotely from a single location. I think we are the first company piloting at this scale in North America, and our goal is to apply this model to most of our operations globally.
One of our convictions at BCG is that enterprise AI transformation requires the CEO to lead by example. What are you doing to show your team that AI is a priority and that you’re personally involved?
Well, the good news is I don’t need to signal much. I’m really excited myself. To put that enthusiasm into action, we created formats like the CEO AI hour, where I connect directly with colleagues around the world who have built something with AI, not just pitched an idea. It cuts through the traditional hierarchy. While it shows my personal commitment, the real goal is to prove that in a company of 50,000 people, AI empowers everyone.
After a record 2025, Heidelberg Materials is leading the industry. How does AI factor into your broader financial and strategic philosophy?
Initially there was this idea that AI could be used purely for efficiency gains, but I think we probably underestimate the speed and precision it can bring. For us, the real differentiator is top-line growth. I’m pushing hard to use AI on the commercial side and in product development because it can completely transform the customer experience.
As for the workforce, I don’t buy the narrative that it will just eliminate jobs. Instead, AI agents will work side by side with our people to elevate what they can do. Ultimately, the biggest unanswered question isn’t about job loss but ROI: Will the massive gains in speed and efficiency outweigh the new costs of running the AI itself?
Heidelberg Materials embraces several flavors of AI—predictive, generative, agentic, and now physical. How do you think about these different arenas, and where do things go from here?
Physical AI really comes to life in our ready-mix plants. Concrete mix designs are essentially like recipes, and we have thousands of them per plant. What we’ve done to connect this physical world with AI is collect 180,000 historical recipes from around the world to build a unified platform.
We are now using this massive data set to train algorithms that optimize the quality, safety, and margins of our mixes. Ultimately, the future is about taking the data the physical world gives you and using AI to leverage it into better products.
You are combining new AI technology with your historical knowledge of these materials. Is that where your real competitive advantage lies?
Exactly. It all comes down to what we call domain know-how. For example, in our R&D centers and our labs, we typically have 30-day, 60-day, or 90-day testing periods to see if the concrete curing is right. But with all the historical data we’ve assembled, we can now train algorithms to simulate that outcome with extreme precision and speed. I am convinced it will eventually free us from needing to do those physical tests altogether. That is a huge improvement, and we can only do it because we have that historical domain data.
To close out, let’s bring this back to the bottom line. You’re focusing this technology on the core of your day-to-day operations rather than on just support processes. How does that ultimately impact your P&L?
There are certainly opportunities in administrative processes, and I don’t want to downplay that. But that’s something everybody can do. By applying AI directly to the core processes of our value chain, scale and differentiation become the two levers that will secure Heidelberg Materials’ competitive edge.
Heidelberg Materials, a global giant in heavy building materials and solutions, recently embarked on a bold effort to future-proof its 152-year-old business. A key part of this journey is embedding AI into its core physical operations, including plans to roll out more than 100 autonomous vehicles by 2028.
CEO Dominik von Achten is leading this charge, taking a highly scalable approach to how AI can drive top-line growth and secure a competitive edge. The following interview has been edited for concision.
Christoph Schweizer: Dominik, technology and sustainability are central to Heidelberg Materials’ impressive results. You have a very particular approach to growth. Tell us more about that.
Dominik von Achten: I think for Heidelberg Materials, our approach is first profitability, and then it’s transformation along two lines: sustainability and automation through AI. I think we have to secure the first to get to the second. The industry had some challenges in the past, in terms of being a big carbon emitter and being one of the most offline businesses on the planet. And where there are challenges there are big opportunities, and at Heidelberg, we grab them.
You say you are one of the most offline businesses out there, yet you decided to take on AI with an enterprise-wide approach. What drove that decision, and how is that working now?
We traditionally very much thought in pilots, but we realized that if you really want to change things you need an approach that’s scalable. There are big pockets along our entire value chain primed to be addressed with automation through AI. We can apply it everywhere—starting in the quarries, moving through the labs and production sites, and extending to logistics, sales and distribution, and the customer interface. The upside of being so offline is that we have countless opportunities to implement AI. We pilot, but with an immediate plan for scale. For example, in North America, we now run 12 plants remotely from a single location. I think we are the first company piloting at this scale in North America, and our goal is to apply this model to most of our operations globally.
One of our convictions at BCG is that enterprise AI transformation requires the CEO to lead by example. What are you doing to show your team that AI is a priority and that you’re personally involved?
Well, the good news is I don’t need to signal much. I’m really excited myself. To put that enthusiasm into action, we created formats like the CEO AI hour, where I connect directly with colleagues around the world who have built something with AI, not just pitched an idea. It cuts through the traditional hierarchy. While it shows my personal commitment, the real goal is to prove that in a company of 50,000 people, AI empowers everyone.
After a record 2025, Heidelberg Materials is leading the industry. How does AI factor into your broader financial and strategic philosophy?
Initially there was this idea that AI could be used purely for efficiency gains, but I think we probably underestimate the speed and precision it can bring. For us, the real differentiator is top-line growth. I’m pushing hard to use AI on the commercial side and in product development because it can completely transform the customer experience.
As for the workforce, I don’t buy the narrative that it will just eliminate jobs. Instead, AI agents will work side by side with our people to elevate what they can do. Ultimately, the biggest unanswered question isn’t about job loss but ROI: Will the massive gains in speed and efficiency outweigh the new costs of running the AI itself?
Heidelberg Materials embraces several flavors of AI—predictive, generative, agentic, and now physical. How do you think about these different arenas, and where do things go from here?
Physical AI really comes to life in our ready-mix plants. Concrete mix designs are essentially like recipes, and we have thousands of them per plant. What we’ve done to connect this physical world with AI is collect 180,000 historical recipes from around the world to build a unified platform.
We are now using this massive data set to train algorithms that optimize the quality, safety, and margins of our mixes. Ultimately, the future is about taking the data the physical world gives you and using AI to leverage it into better products.
You are combining new AI technology with your historical knowledge of these materials. Is that where your real competitive advantage lies?
Exactly. It all comes down to what we call domain know-how. For example, in our R&D centers and our labs, we typically have 30-day, 60-day, or 90-day testing periods to see if the concrete curing is right. But with all the historical data we’ve assembled, we can now train algorithms to simulate that outcome with extreme precision and speed. I am convinced it will eventually free us from needing to do those physical tests altogether. That is a huge improvement, and we can only do it because we have that historical domain data.
To close out, let’s bring this back to the bottom line. You’re focusing this technology on the core of your day-to-day operations rather than on just support processes. How does that ultimately impact your P&L?
There are certainly opportunities in administrative processes, and I don’t want to downplay that. But that’s something everybody can do. By applying AI directly to the core processes of our value chain, scale and differentiation become the two levers that will secure Heidelberg Materials’ competitive edge.
Reckitt’s Kris Licht Believes AI Transformation Starts with People
Reckitt, a global manufacturer of everyday health and hygiene products, recently embarked on a bold effort to simplify its business by focusing on core brands. A key part of this journey is embedding AI into its “Fuel for Growth” strategy.
CEO Kris Licht is spearheading this shift. He recently sat down with BCG CEO Christoph Schweizer to talk about taking a deliberate approach to how AI can create value. The following interview has been edited for concision.
Christoph Schweizer: Kris, you made a decision that AI would be central to your organization’s growth strategy. When was that point, and what triggered it?
Kris Licht: When ChatGPT was released in late 2022, I was running our global health business and was not yet CEO. I took my leadership team to San Francisco, and we spent a week immersing ourselves, figuring out what the technology could do and where it might go. That was a pivotal moment. We decided this was one of the few things worth going fast on—worth real investment and real time. And it was important that we engaged everyone in the journey.
How did you decide the best way to frame the program, what function to focus on, how much experimentation to allow, and how rapidly to scale?
The first choice was to go fast. And the second decision—and I think this is the most important choice we made so far—was not to go out and buy whatever people were selling us.
We took a first principles approach instead, and said, “We want this to change the way our whole organization works.” To figure out what that would look like, we used good old time and motion studies and mapped the work people were doing in a given domain. Before we deployed a tool, we studied what people did, where effort was being spent, or where handoffs or complexity slowed things down.
That has turned out to be a profound exercise. It helps us understand where AI can create value by enabling a different way of working.
You made the choice to go right into the heart of what makes you win, starting with marketing and R&D versus a support function. What drove that choice?
We thought these tools would change the game on what “good” looks like and what excellence requires. Marketing drives our growth, and R&D drives our innovation, so the stakes were highest there. It was counterintuitive, but starting in those domains forced a more serious conversation. It was not simply, “Where can we use AI?” but “How are we trying to change the company?” Once we had successes in these harder domains, it proved something to the organization: if it works in marketing and R&D, it can work anywhere.
One of the things I’ve observed in my own company and at many of our clients is that people say, “Yeah, we tolerate failure, and there will be some of that.” But reality can feel different. How did you manage this at Reckitt and evolve the culture?
You have to keep people focused on the long term. One use case may fail. One piece of the investment may not generate a big return. But as long as, in aggregate, you’re moving forward, you’re on the right path.
I’ve found empathy to be one of the big unlocks. You give the model a little space, you interact with it more productively, and you are less disappointed when it doesn’t do exactly what you wanted the first time. Ownership also matters. The executives who deployed AI in their functions own it because they cocreated the solution. The foundational work they’ve done is now helping us move faster in later waves of expansion through the company.
AI models get better with every release. So the question isn’t about the technology itself, it’s about how your organization embraces it. That has big implications for talent. How are you approaching this and ensuring your teams build the right expertise?
One of the things that we have to figure out is, “How do we create mastery in our more junior people?” In many industries, including ours, junior people previously developed mastery by doing a lot of the basic work.
But AI is reducing the most repetitive and least interesting parts of work, which makes many jobs better and the output more consistent. But this means we have to think harder about career paths and capability building. How do people learn more about AI and its capabilities while simultaneously developing deep functional expertise? We have not fully cracked that yet, but it is one of the most important questions.
Empathy, judgment, and an ability to bring people along are differentiating factors in an organization’s success. You’ve been at the forefront of this AI transformation. What advice would you give to other CEOs who are trying to move beyond experimentation?
Be clear about the impact you want to create, then work backward. What work has to change? What analysis is needed? What design choices have to be made? What needs to happen for the value to show up?
There are many ways to approach AI, and I would not claim there is only one right path. But for us, the first step is a hard conversation about how you want to change your company. Plenty of people have strong products, but buying technology comes far down the line.
What has helped you lead this as CEO?
Curiosity! I found myself in a panel presentation about how AI enabled the discovery of entirely new compounds. It immediately captured my imagination and made me wonder what that kind of capability could do for Reckitt.
When things get hard or when we have to revisit our assumptions, I continually go back to that moment. It reminds me that this is worth doing despite all the challenges.
CEO Kris Licht is spearheading this shift. He recently sat down with BCG CEO Christoph Schweizer to talk about taking a deliberate approach to how AI can create value. The following interview has been edited for concision.
Christoph Schweizer: Kris, you made a decision that AI would be central to your organization’s growth strategy. When was that point, and what triggered it?
Kris Licht: When ChatGPT was released in late 2022, I was running our global health business and was not yet CEO. I took my leadership team to San Francisco, and we spent a week immersing ourselves, figuring out what the technology could do and where it might go. That was a pivotal moment. We decided this was one of the few things worth going fast on—worth real investment and real time. And it was important that we engaged everyone in the journey.
How did you decide the best way to frame the program, what function to focus on, how much experimentation to allow, and how rapidly to scale?
The first choice was to go fast. And the second decision—and I think this is the most important choice we made so far—was not to go out and buy whatever people were selling us.
We took a first principles approach instead, and said, “We want this to change the way our whole organization works.” To figure out what that would look like, we used good old time and motion studies and mapped the work people were doing in a given domain. Before we deployed a tool, we studied what people did, where effort was being spent, or where handoffs or complexity slowed things down.
That has turned out to be a profound exercise. It helps us understand where AI can create value by enabling a different way of working.
You made the choice to go right into the heart of what makes you win, starting with marketing and R&D versus a support function. What drove that choice?
We thought these tools would change the game on what “good” looks like and what excellence requires. Marketing drives our growth, and R&D drives our innovation, so the stakes were highest there. It was counterintuitive, but starting in those domains forced a more serious conversation. It was not simply, “Where can we use AI?” but “How are we trying to change the company?” Once we had successes in these harder domains, it proved something to the organization: if it works in marketing and R&D, it can work anywhere.
One of the things I’ve observed in my own company and at many of our clients is that people say, “Yeah, we tolerate failure, and there will be some of that.” But reality can feel different. How did you manage this at Reckitt and evolve the culture?
You have to keep people focused on the long term. One use case may fail. One piece of the investment may not generate a big return. But as long as, in aggregate, you’re moving forward, you’re on the right path.
I’ve found empathy to be one of the big unlocks. You give the model a little space, you interact with it more productively, and you are less disappointed when it doesn’t do exactly what you wanted the first time. Ownership also matters. The executives who deployed AI in their functions own it because they cocreated the solution. The foundational work they’ve done is now helping us move faster in later waves of expansion through the company.
AI models get better with every release. So the question isn’t about the technology itself, it’s about how your organization embraces it. That has big implications for talent. How are you approaching this and ensuring your teams build the right expertise?
One of the things that we have to figure out is, “How do we create mastery in our more junior people?” In many industries, including ours, junior people previously developed mastery by doing a lot of the basic work.
But AI is reducing the most repetitive and least interesting parts of work, which makes many jobs better and the output more consistent. But this means we have to think harder about career paths and capability building. How do people learn more about AI and its capabilities while simultaneously developing deep functional expertise? We have not fully cracked that yet, but it is one of the most important questions.
Empathy, judgment, and an ability to bring people along are differentiating factors in an organization’s success. You’ve been at the forefront of this AI transformation. What advice would you give to other CEOs who are trying to move beyond experimentation?
Be clear about the impact you want to create, then work backward. What work has to change? What analysis is needed? What design choices have to be made? What needs to happen for the value to show up?
There are many ways to approach AI, and I would not claim there is only one right path. But for us, the first step is a hard conversation about how you want to change your company. Plenty of people have strong products, but buying technology comes far down the line.
What has helped you lead this as CEO?
Curiosity! I found myself in a panel presentation about how AI enabled the discovery of entirely new compounds. It immediately captured my imagination and made me wonder what that kind of capability could do for Reckitt.
When things get hard or when we have to revisit our assumptions, I continually go back to that moment. It reminds me that this is worth doing despite all the challenges.
