Business leaders need to face a reality where AI isn’t just a buzzword, it’s the engine driving real business impact. Volvo Group is leading by example by weaving AI into the fabric of its operations, seeking tangible, transformative, and sustainable impact. In this exclusive interview, Robert Valton, Head of Data, Analytics & AI, Volvo Group Connected Solutions, shares valuable insights on the game-changing nature of AI, one of the key enablers to offer tailor-made end-to-end solutions to customers and to achieve Volvo Group’s long-term ambitions to be 100% safe, 100% fossil-free, and 100% more productive.

How did Volvo Group approach the implementation and scaling of AI technology? Can you share a few use cases that showcase the value of AI at Volvo?

Volvo Group has great products like trucks, buses, construction equipment, and marine and industrial engines. There has been a lot of focus on the products, but we also turn towards services and solutions where we can really utilize AI. For example, an iPhone has around 10 sensors like GPS, accelerator, gyroscope, and barometer. A truck has 10 times as many sensors as the iPhone. Imagine the possibilities we have with the truck’s data. I want to highlight that AI is a game changer, especially when you utilize real data. 

With synthetic data or transfer learning you might leverage AI with minimal or no data initially, but if we have real data, we can bring the full value of AI. 

To create value, you need to balance both data and AI technology, and you need to have a business need, a challenge to solve. It’s not always clear how to formulate the question, to know about the possibilities and the value of data and AI.  

This is our aim in the Volvo Group to help articulate this need, both the spoken and unspoken, addressing both the known and unknown questions.  

Traditionally, we have used AI for autonomous driving connected to a product. We have continued to explore AI around the product. For example, predictive maintenance to understand the product’s lifetime and its components. With AI, we can predict when a truck needs to go in for preventive service before the components malfunction to ensure we always have the truck on the road delivering goods. But this is still around the product. If we expand it to the driver or the operator, we can support driver training with fuel and energy coaching through digital twin technology.  

We also use AI for transportation optimization to understand if there are bottlenecks in a transportation flow. Generally, 50% of trucks in Europe transport empty. So, 50% don’t have cargo and 25% of trucks are standing still. That means we have a lot of underutilized capability and capacity. If we can utilize AI to address this, we can deliver more cargo with the existing fleet, which is better for the environment. 

You can use AI to solve all problems, but not all problems deserve AI.  

Sometimes deploying AI can be too cumbersome, expensive, and complex. We should always adjust the tool to the problem, so we are efficient. The right tool for the right use. Another aspect is AI for internal efficiency. For example, we have a lot of coding in-house, and AI is our coding buddy to verify and test code. You can also use AI to automate manual tasks or quality issues in a process.  

Generative AI (GenAI) has taken the business world by storm this year. For organizations who don’t know where to start with GenAI, how should they go about implementing the technology?

Start, try, and explore. Many people talk about GenAI but they haven’t tried it. So, I often ask in different meetings, “Have you tried AI technology? Have you tried for example AI tools like ChatGPT or DALL-E?” just to get an understanding.  

We decided to have a bit of a lean approach to this in our organization so we created AI in Action, a series of events where we explore how AI can support us both with internal efficiency and our services. We invite our entire organization, and we start with an inspirational event having presenters demonstrating how we can fully use AI in our context in a safe and compliant way. We have discussions about compliance, ethics, and all those questions that need to be on the table before trying out AI.  

We didn’t do this from a technology perspective, we did it from a business perspective. So, we started to ask our business stakeholders what their pain points are, focusing on that rather than on what AI can do. This was a super interesting journey because everyone’s eager to start using AI now. But let’s not forget why. So, we went back and talked about this, and found different pain points that we could address with AI. We have since calibrated some and decreased some, and now we are on four of those doing a hackathon.  

The great thing was that when we started this, we had 600 people in our organization globally who joined this inspirational event. You get the energy and passion from the complete organization, it’s not a top-down directive. It’s building the data-driven culture and transformation journey. It requires that you think of AI as more than GenAI, more than a tool or service. This is all about leadership, strategy, value, data, and compliance. Here we need to navigate and make sense of it.  

There is a lot of buzz around hiring a Chief AI Officer (CAIO). Do you think it’s time for board-level representation of AI?

The answer is connected to the size and the kind of organization you have. But I would say that we will see more CAIO roles in companies in general and at least one board member with an AI focus. Appointed owner of data, analytics & AI at C-level with the right focus and mandate will enable the company to be a leader in the “data to value” transformation.  

AI is more than a technology, it’s something that goes cross-organization and balances technology and business.  

If everyone now has access to AI, what makes you unique compared to other companies? What customer relationship would you like to have? That’s also something that you need to reflect on. Would you just like a digital interface for all your customers? Would you like to have a more personal interaction somewhere? That’s why I believe that AI should be on the top management and board level. If you handle that right, it gives you an advantage. If you don’t, it will probably be the end of your business. 

As an AI leader yourself, what challenges have you encountered with AI governance?

There are different maturity levels in an organization, and you need to have the dynamics to balance that. You need to talk about and address what should be centralized and distributed. You need to make sure that you build and support a data-driven culture, that everyone’s on board, and you have to figure out the right way to work. But at the same time, avoid having a lot of people reinventing the wheel. In an ideal setup, you would have one truth of information that is free-floating in the organization.  

This is why we need to address the governance part to make sure that everyone is on the same page, that we are smart about what tools we use, what processes we utilize, what we should make ourselves, what we should buy, and who we should partner with. It’s important to have a structured approach to all those questions.  

We also need to address that we might have old truths based on gut feelings. With a data-driven approach, with a black box that contains AI, you might come with a truth that challenges the old hypothesis. That’s about trust and change management. How do you handle that? Do we have leaders that believe that we can utilize this technology? It might require that we upskill people and completely change the way with AI in the equation. My firm belief is that AI Is not only for the tech geeks, instead we should focus on the value it gives. Coming back to the question about the CAIO, I believe that we need to have people balancing between business and technology here so we can also utilize AI effectively, not just because it’s a cool technology.  

The rapid development of AI technology requires leaders to have strong AI literacy. What are the top strategies to foster AI literacy in upper management?

We need to go from PowerPoints to action, from hype to reality. It’s a great opportunity to share with the top management how this can be used as a capability to drive transformation from data to value. Give concrete examples and support top management to try themselves. They need to understand and see concrete examples in a context. And if they aren’t already, help them ask the right questions.  

One thing that’s super important is for companies to define AI.  

GenAI is just one tool in the box. There’s natural language processing (NLP), computer vision, predictive analytics, simulations, optimization, and more. I’ve been working with AI for the last 10 years, but I’ve only worked with GenAI since last year. Another thing that will be important is to understand the business disruption that will happen because of AI. How can we relate to that? How can we make sure that we have the strength to be part of that and utilize AI to bring value to our business, both for customers and for internal efficiency?  

Also, work proactively with ethics. How would you like to see AI used in your organization? For example, I work a lot with recruitment. If you have an AI that is trained in a certain way that might choose certain individuals, that is not the way to go, we should have a diverse setup that goes with the right candidate regardless of age and gender to have diverse and dynamic teams. Coming back to the data, it will be a nominator moving forward, understanding the data that you have and the value that data can provide. Then you can decide what to do with the data, you can partner up and collaborate. You need to be dynamic with the way that you proceed. 

Europe has been a trailblazer when it comes to regulating AI and data privacy. How can business leaders navigate the complexities of compliance and not abandon innovation?

I believe it’s important that we are careful. Today we have what we call narrow AI. You have Siri, for example, you ask Siri a question and Siri will answer, the typical AI that many companies work with. But the next level is general AI, where AI can move between different tasks. So, imagine if Siri started to go into other areas like autonomous driving. That’s not what Siri is built for but if her intelligence expanded, she could take on new things.  

The next level of general intelligence is super intelligence, that’s when AI will outsmart humans. And in that era, AI will be more intelligent than mankind. We must find ways to relate to the evolution of AI. So, I’m receptive to regulations stating how we should evolve AI. It’s also important that we talk about compliance, ethics, and personal data protection.  

I don’t see that it’s either or, I think we can find a balance between compliance and innovation, especially innovation and AI for good.  

For example, if I say our goal is to enable more transportation with less climate impact, that’s quite a nice goal to have, and then we balance that with being compliant. I’m convinced that we will find that balance.   

How do you see AI growing in the next 5 years? How will it transform the automotive and transport industry?

AI is a game changer. Many people compare it to electricity or the Internet, and I agree. So, we do need to relate to AI. It’s not that we can live without it. Instead, we need to relate and adopt. 

Examples of data to value, supported by AI: 

• Vehicle/machine: Secure uptime by predicting the lifetime of components and enable replacements of components before breakdown. 
• Driver/operator: Train, coach, and provide feedback to drivers and operators for optimal fuel and energy consumption. 
• Operation: Identifying anomalies like waiting time in transportation flows in real-time and automating manual steps. Potential to significantly improve transport cycles and increase operation efficiency.  
• Transportation and mobility: Predicting the power demand as a result of future charging infrastructure to enable the transformation to electrified transportation. The insights from the work are presented to the Swedish government, the EU parliament, a number of grid companies, and was also instrumental in the development of the public tool “Behovskartan” and ACEA map of common truck stop locations. 

We need to understand how this will affect the complete organization and what strategies we should have to address this.  

Everything from the value creation to data to our target architecture to our teams. Whether we should upskill or reskill, we need to have a broad picture of this. For example, I heard a statement that it’s not AI that will take your job, it’s a person who utilizes AI that will. We should also be proactive. Instead of being in the backseat about regulations, I believe as big companies, we can take responsibility and drive things, so we make sure that AI is a tool to do good things. But it shouldn’t be that AI is on top of everything.  

Connected to Volvo Group’s industry, AI has the potential to help us reach 100% transport utilization. We can have a much more connected transportation flow because the current one is really scattered. With AI and connected data, we can do a lot of good things and secure mobility. One of Volvo Group’s higher goals is to address sustainability and reduce our climate impact.  

I think this is a very interesting time that we are in. I’m not a tech guy in that context. I’m not a data scientist. I’m coming more from sales, innovation, and leadership. It has been a good recipe for me to drive this and to bridge the gap between business and technology. 

*The interview answers have been edited for length and clarity.