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8 Tech Trends to Watch in 2024 by Bernard Marr, Futurist

Every company is now a tech company – with the exponential growth of computing and the value of data presenting itself as crucial to any organization’s success. Tech and business leaders alike understand the importance of having their fingers on the pulse of the latest tech trends or risk falling quickly behind and losing out on vast potential.  

World-renowned futurist, influencer, and thought leader in the field of business and tech, Bernard Marr, explored eight top tech trends that business leaders need to watch in 2024.  

 

1. Ubiquitous Computing

 

The first and foundational trend is ubiquitous computing. This was first enabled by cloud computing which allows as much data as you want to be stored in the cloud and processed almost immediately, and further enhanced with 5G connectivity. Edge computing is also part of this, allowing data to be processed directly on devices, making it a more efficient process.  

The exponential growth of computing power means that we now have reached the physical limits of how much computing power we can squeeze into a microchip. However, even this is changing.  

Bernard Marr noted: “Quantum computing is something that leverages the bizarre things that happen on a subatomic level where particles can be in more than one place at the same time. Instead of normal computers that process things sequentially, quantum computers can do it simultaneously, making complex computing tasks about a trillion times faster than the supercomputers we have today.” 

Eventually, quantum computing will be available on the cloud. Companies are already building these tools and integrating them into their cloud offering. Bernard projects that quantum computing will experience a big leap forward in 2024 and that in five years, it will be an important contribution to computing overall. Other data storage systems like DNA storage – where data is stored on DNA strands – will also contribute to ubiquitous computing.  

 

2. Datafication of the World

 

The second trend Bernard points out is datafication. Data has become an essential business asset to organizations. It is available at unprecedented levels – around 75 zettabytes of data, 90% of which was generated in the last five years. However, less than 1% of data is used in a meaningful way. In organizations, less than 0.1% of data is used. Data is also growing exponentially and is expected to grow to over 225 zettabytes by 2025.  

Using the example of how Netflix leverages data to drive its recommendation engine and create content, Bernard explained the importance of using the data you have to drive decision-making and business strategies.  

Another example is the use of satellite data by companies in the construction industry – in combination with machine vision technology – to keep tabs on their competitors’ progress and how they stack up against it.  

Crucially, Bernard highlights the importance of data literacy. He said, “When it comes to data, there is a challenge where we are lacking a lot of data literacy in our organizations. We have lots of people that don’t particularly like data. So, companies often build these big data lakes and warehouses, put nice analytics on the front end, and say that you can be your own data scientist and answer all your questions. But this doesn’t happen.  

What we need to do is more handholding. We need to put in place more help for people to understand what data they have and how they can use it.” 

Bernard shares the example of how Shell tackled this problem by building a data café at their headquarters. Here, employees can sit down with a data scientist to present their business challenges and questions and get help on how data can help them. Data scientists can help them understand what data they have access to and how best to use it. 

“It’s basically data translators. People that sit between business and data functions to help business units understand how to better use data in their everyday operations.” 

 

3. The Artificial Intelligence Revolution 

 

Ubiquitous computing and datafication lay the foundation for artificial intelligence (AI). Bernard emphasized that this is by far the biggest trend right now. This is where organizations should invest currently.  

“Every organization now is an AI business. AI is transforming pretty much any industry.” 

Citing Amazon as an example, Bernard talks about how AI is transforming the physical retail experience. Amazon Go stores utilize facial recognition technology and other AI tools to enable a smoother, easier shopping experience for their customers who can just pick stuff off the shelves, put it in their bags, and walk out as they are automatically charged for the items they bought.  

Another creative use of AI is in the fishing industry. A Norwegian salmon farming company connected their sea pens over 5G to a data center that allows them to track feeding systems with facial recognition technology to ensure all the fish are fed an equal amount and therefore mature at a similar rate. The connected sea pens also have a circular swimming system, in tandem with machine vision technology, that allows them to analyze the health of the animals, detect diseases, and isolate infected fish before the disease spreads to the entire population.  

On generative AI specifically, Bernard noted the significant advances in not only AI-generated text and images but also videos and voice. Though there are ethical concerns around generative AI, it can also be used for good such as in medical research to develop new drug formulas.  

“This is such a powerful technology that I believe will impact all of your businesses from today,” he emphasized.  

 

4. The World Becomes Immersive

 

Bernard believes that the world will become more immersive largely through augmented reality, virtual reality, and other similar technologies. Companies like Bollé, the French sunglasses brand, and Dalmore, the whiskey brand, have incorporated augmented and virtual reality into their customer experience journey. Bollé developed an app with augmented reality to allow their customers to virtually try on sunglasses as well as see through them via their phone cameras for a full experience.  

Dalmore created a whole virtual journey for their customers to experience a trip to the Dalmore distillery in the Scottish Highlands to see how their whiskey is made. At the end of this virtual trip, customers receive their order of a Dalmore whiskey cocktail.  

Immersive technology goes beyond just enhancing customer experience. Formula 1 racing team, Red Bull Racing, creates digital twins of their cars, circuit simulators, and full-body haptic suits to create an immersive racing experience. This allows drivers and engineers to test out the cars before they’re manufactured and make any fine-tuning necessary before 3D printing components. 

 

5. A Digitally Editable World

 

This leads to the next trend of a digitally editable world. This means being able to manipulate the real world from a digital world. Nanotechnology enables us to manipulate materials at nanoscale, allowing scientists to create completely new materials such as graphene – the thinnest material on the planet that is also incredibly strong. This has led to designs such as bendable screens.  

Other uses include creating cultured meat, something that is already being done today. In fact, there are sushi restaurants in Japan that 3D print sushi. If they have your DNA analysis, they can even 3D print sushi with the exact nutritional values that you need.  

Technologies like CRISPR/Cas9 have opened a world of gene editing that offers a major avenue for fighting hereditary diseases and cancer. Bernard noted, “A lot of work in cancer research and other diseases research is going towards identifying gene markers. If we can identify them, we can take them out. With CRISPR/Cas9, we can do that. This is amazing.” 

However, as with any powerful technology, there are ethical concerns. Bernards stressed, “On the flip side, it’s really scary because we can modify pretty much anything about any plant or any human being or animal. If you want to improve someone’s strength, IQ, eye color, skin color, height – you can do that in the future. So, there are huge ethical concerns again, but this is coming.” 

 

6. Rearchitecting Trust with Blockchain Technology

 

Last year, there was plenty of hype over blockchain technology and NFTs, but it quickly disappeared as people realized that spending money on digitally created photos with no inherent value isn’t great. It was the same with cryptocurrencies.  

Bernard said that while there are plenty of developments in this area, there still isn’t a standard blockchain technology. There is also no interoperability. However, he believes this is coming and that this technology will have major implications in various ways.  

For example, the United Arab Emirates is putting its property documents onto the blockchain, making the house-buying process much simpler than it is now. “Lots of the middleman functions will be eradicated through blockchain technology,” Bernard warned.  

As for NFTs, while they have developed a bad reputation, some companies have used this technology in creative ways. The whiskey company, Glenfiddich, created a limited edition 43-year-old whiskey that they sell at auctions. Usually, the company won’t know what happens to the bottles once they are delivered to the auction houses. However, this limited series of just 10 bottles were auctioned with NFTs. Buyers receive a digital certificate proving that they own the bottle, which physically is located in Singapore. If they want to sell it, they simply sell the NFT. If they want to drink it, they can swap the NFT for the actual bottle which will be delivered to them. This way, Glenfiddich knows exactly how many unopened bottles there are, which can be verified digitally.

 

7. Resilience is Safety and Sustainability 

 

Bernard said that the second trend organizations should invest in is cybersecurity. Pointing out that resilience is about safety and sustainability, Bernard cautioned that keeping data safe has never been more important.  

“Quantum computing will be able to break all the encryptions we have today. If you think the data you have is nicely encrypted, quantum computers will break it. There’s now work on how we can make our systems quantum safe.” 

Today, bad actors are already stealing data sets that they know they can break into in the future once they have access to quantum computing. This is an alarming situation that needs to be addressed.  

The other part of resilience is sustainability. A huge global challenge is climate change. Bernards points out, “The cloud industry combined now has a bigger carbon footprint than the entire global airline industry because they are running huge data centers that use huge energy, resources, and water. We need to start thinking about how we make this better.” 

 

8. Talented People Still Matter 

 

Bernard said he believes that being able to work alongside machines and guide machines is one of the most important jobs. While it’s true that data science and math skills are needed to a certain degree, the vast majority of work won’t need those specific skill sets as machines will be able to do it better than humans. ChatGPT can already write and debug programs.  

“What we need in the future is to have people that have the human skills, the soft skills,” Bernard explained, adding that data literacy, technical skills, and awareness of cyberthreats are just some of the types of skills that we need to equip ourselves with.  

Beyond these digital-related skills, we need the skills that make us intrinsically human such as critical thinking, complex decision-making, emotional intelligence, and creativity, among others.  

“These are the kinds of skills that will set people apart in the future. What we need to look for, and what we need to develop in our organizations, is people that have those skills and that are able to work alongside intelligent machines, to get the best of both worlds.” 

Bernard added, “There are huge opportunities. We need to sort out some of the problems, but I believe that all of the technologies I’ve talked about today will help us solve the biggest problems we are facing on this planet.” 

 

*The insights have been edited for length and clarity.

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Insights from Picnic’s CTO: Mastering Retail Resilience

Daniel Gebler, co-founder and CTO of online food shopping giant Picnic, provides valuable insights on how to build a resilient retail organization through the concept of anti-fragility. He also shares how Picnic navigated through black swan events with the anti-fragility mindset, leveraged customer feedback to revamp systems and processes, and improved the organization’s safety and sustainability scores with technology.  

 

RETHINKING THE ONLINE GROCERY DELIVERY SYSTEM

 

Generally, online food shopping hasn’t taken off with high delivery costs and wait times as major factors. “It’s very expensive to do food shopping online. If something is very expensive, you won’t do it. It’s not so much that you must pay higher prices for the products, you had to pay for deliveries. At this point, you also have to pay for deliveries for returns. That was not the case for other replicas.” 

To combat these obstacles, Gebler highlights the importance of accurate delivery windows and the need for faster delivery to make online food shopping more appealing. “It’s not so much that you deliver faster. It doesn’t work because you will run out of money. It’s not so much the waiting time, but the accuracy of delivery. If you say you can deliver this in one hour, then you have a much different proposition. That broke up the entire market. This is something you see again and again where everybody tries to copy what has worked in one market to another market.” 

Food shopping requires a different experience, as customers will purchase food every week, as opposed to a digital device that they will buy once every few years. “You never buy the same device again. However, food is a different dynamic. So, the entire shopping experience needs to be different.” 

Gebler proposes a new approach to e-commerce by owning both the front and back office, allowing for greater control and success. “What everybody was doing in e-commerce is only from the front office and the app, but nobody owns the logistics. That is a big issue because then you simply can’t control it. If you want to be successful in e-commerce, you need to own both the front and back office.” 

He also suggests taking a service position that is more suited for less convenient situations, such as offering delivery windows based on customer demand. “What would happen if you offered only one delivery window a day instead of ten? This model has simple logistics. If it’s not convenient for customers during that window, they can order for the next day. It’s basically a bus distribution system to get from street to street and never visit the same street twice. This is a very efficient delivery system.” 

 

REVAMP SYSTEMS AND PROCESSES TO WITHSTAND BLACK SWAN EVENTS 

 

With competition from local supermarkets and Amazon, Gebler emphasizes the importance of understanding people’s behavior in the market. He also discusses the concept of anti-fragility in business, where resilience and adaptability are crucial in the face of uncertainty and negative events. 

“If you think about uncertainty, maybe you think about probability. But there are black swan events, and we have been going through one over the last few years. The first thing that happened for Picnic was that we had 10x demand. So, most people can’t properly deliver to you from the supplier side as demand was skyrocketing. And then the entire logistics completely changed. The point of anti-fragility is that you innovate your business to become anti-fragile.” 

He defines fragility in business as a software system or logistical process that no longer grows because of an unexpected event. There is a need to move beyond traditional robustness measures and instead focus on creating systems that can benefit from unexpected events, making the organization stronger in both good and bad times. 

“With anti-fragility, you have built many infrastructure systems and mitigation steps to make processes and systems in the organization more robust. The real power of anti-fragility is that you move to a state where an external factor, even if it has a short-term negative impact on you, makes your organization stronger. If you can achieve that, then you grow not only in good times but also in challenging times, because you are prepared for whatever may come in the future.” 

 

LEVERAGING CUSTOMER FEEDBACK TO BECOME ANTI-FRAGILE

 

Picnic has designed a system to be anti-fragile by starting small, gathering customer feedback, and optimizing packaging and delivery. “How do you design a system that is prepared for anti-fragility? Firstly, if you’re a retailer, ask yourself, what are you selling? Maybe you stock 4,000 items. And at some point, you simply say, ‘We will serve more if you tell us what to serve.’ Therefore, we started to ask all our customers what kind of products they would like to have at Picnic.” 

It’s also important to find out what customers want but can’t find at the store. “If you get hold of that information, you can launch the product. If there are no suppliers for that product, you need to go one step further. If nobody’s producing what consumers are looking for, you need to do it yourself.” 

In addition, Picnic leverages AI and machine learning to improve customer service communications by analyzing past responses to provide better responses in the future. “If you put this all together in a database, if you take all the responses that agents have spoken in the past, you have a good measuring system to see what a quick response should be in the future.” 

 

OPTIMIZE FOOD DELIVERY LOGISTICS WITH AI 

 

Gebler discusses the importance of last-mile delivery logistics, including predicting delivery times and addressing customer questions. “On the logistical side, the last mile is a very important aspect. It is essentially the only real touch point that we have with a customer at Picnic.”  

He highlights the challenges of delivering to customers, such as navigating complex addresses and addressing customer concerns. Gebler discusses the importance of sharing real-time data with customers and suppliers to improve efficiency and safety in the delivery process.  

“How do you work with machine learning to create a customer experience? There are two factors at play, you need to predict how long the delivery will take, and how to make that prediction a reality. When you’re talking about AI, machine learning, and forecasting, you need to have a better prediction system. You need to also have a better operational system that makes the prediction happen.” 

 

LISTENING TO CONSUMER DEMAND TO REDUCE FOOD WASTE 

 

The environmental impact of online food delivery services is negative overall, with only 50% of food produced being consumed. Gebler also notes the potential of autonomous driving in the food industry. Although it is not yet practical for public roads, it could have a positive environmental impact in the long run. Gebler highlights the importance of avoiding waste in the supply chain, particularly with self-adapting supply chains and early consumer demand knowledge. 

“If you look at the entire food system, only around 50% of what’s harvested gets consumed. So, this is an extremely wasteful industry. If you look at physical retail, usually most supermarkets have waste, somewhere between 10% and 20%. Picnic’s is below 1%. That is something we are very proud of and what we find very important because this kind of waste is avoidable. By having a supply chain that is self-adapting and knowing the consumer demand very early on, we can avoid waste.” 

 

DATA COLLECTION FOR SAFETY AND SUSTAINABILITY 

 

Picnic has implemented a safety data collection process for self-driving cars, using data from vehicle sensors to identify areas of improvement. One of the data collection methods is the driver coaching feature, providing riders with feedback scores and suggestions for improvement after each ride.  

“The first step was to understand where we stand with safety. Let’s collect data from the vehicles. Where are people driving safely and where are they not? We have identified dangerous places that have not been given enough attention to drive safely in. It’s again about sharing data. So, what we do is give all our drivers some feedback once they have completed the ride. After a ride, our drivers are given a feedback score between 0 and 100. If you’re 80 and above, then you have actually completed a fantastic ride.” 

When it comes to sustainability, Gebler mentions the biggest challenge lies in poor transparency with third-party suppliers. “There’s a big obligation for the entire ecosystem to have more accurate data available for partners and consumers to get the full end-to-end view on sustainability.” 

 

COMPLEMENT DATA WITH VISION AND AMBITION  

 

Combining data with vision and understanding is paramount to creating a successful business. He encourages entrepreneurs to share early versions of their products with customers to gather feedback and create something meaningful. 

“If you have a big plan and you need to do 10 things, what are the first two things that create significant value? It’s easy to say, but it’s probably the hardest thing in building a business and innovating. While we are all collecting a lot of data and talking about data innovation, the reality is that you will never have enough data. You need to complement data with vision and ambition.” 

“This is important for leaders across all industries. The main role is that you complement data with your vision and understanding of the industry. The same applies to machine learning and deep learning. While this is important to build, you need to think about how to go live with a prototype today with minimal effort.” 

“While we are all trying to think about what it would mean to launch a product and put it in the hands of millions of customers, we are usually launching too late. There’s a saying in the startup world that goes, ‘If you’re not embarrassed by the first version that you put out, you’ve launched too late.’ There’s a real power in sharing with customers that very early version to get feedback. You don’t need big teams to create something meaningful.” 

Gebler discusses the challenges of labor shortages in the industry and how Picnic addresses this issue through small project ownership and early responsibility for young employees. 

“It’s very interesting to see typically very young people have a lot of ownership. For example, we have supply chain leaders who run fulfillment centers and lead 400 people. In many cases, these leaders are around 27 and 28 years old. That’s impressive from an industry perspective. We also have a young engineer leading our entire safety operation. That is 300,000 vehicles for him to turn around.” 

 

*The insights have been edited for length and clarity. 

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What is Beyond the AI Hype for Volvo Group?

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. 

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Generali Group’s Dipak Sahoo: Why Every CIO Should Mentor Start-Ups

An increasingly tech-focused business environment demands CIOs to play a bigger role in driving investments and innovation in their organizations. One way for IT leaders to ignite that spark is to mentor start-ups and build mutually beneficial business relationships.   

In this exclusive interview, Dipak Sahoo shares expert insights on why mentoring start-ups is an excellent way for experienced CIOs to invest in their careers, share their knowledge, and gain new perspectives on emerging technologies and innovation culture.   

 
Dipak Sahoo is the Regional CIO Asia of Generali Group. As Regional CIO, he drives the implementation of IT strategies in Asia and identifies synergies between countries to improve customer experience effectiveness. Prior to that, he spent most of his career with global insurance companies in senior leadership roles in technology, operations, and transformation across APAC and Europe.
 

How do you currently collaborate with start-ups in your role?

We do it in multiple ways. For one, we are part of an innovation lab run by the likes of Accenture and others. We also directly interact with start-ups based on our needs. We also have a global innovation team. And we have innovation teams across multiple countries in the world. And we all come together as a group and engage with start-ups. In fact, I was in an innovation forum last week in Paris where we were looking at interacting with start-ups in the health tech space. We want to see how they can complement life insurance services and make meaningful customer engagement propositions for our customers.   

 

Can you share an example of a recent successful collaboration with a start-up?

One that comes to my mind is a company called EOS Microinsurance in Hong Kong. They run a microinsurance company, and what is quite innovative is that they run it on a blockchain platform. The insurance that they provide is embedded into the tasks that we typically do every day. In Hong Kong, when you take a ride on any public transport, you are entitled to certain types of insurance.  

They also have bite-sized insurance. For example, if you want to go on a hike, you could just select an insurance policy, so you are covered in case something happens to you. This model has been very successful. We also engage with start-ups in France. We have partnered with a company called Remedee Labs which works with chronic pain management. They are part of a joint venture that we have created called Future for Care.  

 

What about a collaboration that was not so successful?

I wouldn’t say there is any partnership that hasn’t gone well. There was one collaboration that we realized halfway through that it wasn’t exactly a partnership that will bring value to either party. So, we decided to end the partnership but still provided them support in terms of giving them access to our management team and subject-matter experts to refine their proposition to the market. But at a certain point, the initial focus that they had which was of interest to us wasn’t there anymore because they wanted to pivot to a different proposition that wasn’t aligned with what we were trying to do. Therefore, we amicably parted ways. 

When start-ups and companies like ours come together, we need to have a common purpose and aligned objectives.  

Start-ups expect a certain amount of expertise from our side and provide them the scale that they probably wouldn’t get in the testing phase. On the other hand, we look to start-ups to bring innovation and problem-solving skills or technology; or a proposition that enhances our market offerings.  

Sometimes, start-ups get frustrated with the pace of progress as larger companies typically have to go through multiple hooks. I hate to say this, but there are bureaucracies that we need to deal with, sometimes to do certain things. Sometimes the technology might be working on a smaller scale. So, there are a few reasons why a partnership does not work out. It could be culture, technology, or a change in the value proposition.  

 

Should every start-up have an experienced CIO as a mentor?

I don’t think so. Irrespective of whether the CIO comes from a large company or a start-up, it’s someone who brings value to the start-up in terms of the usage of technology and scaling that technology to take on the issues that the start-up could potentially face when they start ramping up their business. CIOs can bring value in terms of stakeholder management. For example, pitching to the investors to invest in technology in an area that probably not familiar with. I don’t think it’s just the CIO, it could be any C-level. So, I don’t want this to be seen that only the CIO can add value to start-ups.  

 

What are the challenges of a CIO of a large enterprise compared to a CIO of a start-up?

For large enterprise CIOs, resources vs. demand are a constant challenge. Most of the time, we are working to reduce costs but there are market challenges. Of course, managing the bureaucracy within large enterprises is another challenge that CIOs face. Changing the culture and mindset of more traditional companies to become more innovative is always difficult. But having said that, those things are changing rapidly as we speak because I don’t think you can find any large organization which isn’t trying to change or trying to bring in a certain amount of innovation and cultural mindset into the organization. Everyone is doing that, including us.  

As we partner with more start-ups, we are learning from them constantly. We’ve created innovation teams within every organization and as they demonstrate success, that success cascades to other parts of the business as well. So, those challenges are gradually transforming in terms of being hungry for resources. How can we compete with the start-ups that are stepping into large enterprise areas?  

For start-up CIOs, the biggest constraint would be the availability of funds needed to scale the business. But what I’ve seen many of the start-ups, the problems they try to solve are there in the first place because of the size of the organization. When start-ups solve those problems, they typically try to solve the problem when they’re small. But when they start growing, can they use the same technology to manage the inefficiencies if they grow into a large enterprise?  

For example, when insurtech companies start looking like traditional insurance companies. The questions that typically would be asked of them is “How have you managed your traditional performance KPIs?” If you’re not managing that, then you are running a loss-making business, right? Therefore, how start-ups manage the performance matrix would be key. 

 

How can CIOs benefit from mentoring a start-up?

The biggest thing that CIOs can learn from start-ups is the ability to innovate – how they work and solve problems using technology.  

The second thing would be how to do more with less. How do you create, test, and pitch a product to investors with limited resources? Larger organizations should start behaving like start-ups in terms of pitching to the management to get resources. They should demonstrate that they are effectively using those resources to create something that solves real-world problems faced by large organizations. CIOs can adopt the mindset of start-ups on what drives innovation and the culture. 

Before I choose to mentor a start-up, I’ll ask myself if I can add value to that start-up. There have been times when I’ve turned down start-ups because what I can offer may not be of use to them. The key thing for CIOs who are looking to mentor start-ups is to ask themselves whether they can bring value to the start-up through their skills, expertise, and experience.  

 

On the other hand, how do start-ups choose their mentors?

There are platforms such as industry forms and accelerators that bring start-ups and industry players together. CIOs who are genuinely interested in mentoring start-ups take a personal interest in attending those forums. Those are opportunities that start-ups could use to reach out and connect with CIOs. I also think the best way is for start-ups to reach out to companies or CIOs directly through social media platforms.  

 

Some CIOs of public institutions must adhere to certain procurement procedures which could hinder start-up mentoring opportunities. What are your thoughts?

As part of the procurement process, most organizations have one criterion which is the financial health or stability of the company. That’s where start-ups probably don’t do well because they don’t have a large balance sheet as they haven’t been in business for long. What we do in those cases is consider the technology or solution they bring to the table, whether it outweighs what is being offered by others. The three things we look at are functional fitment, technical fitment, and organizational fitment.  

Ideally, start-ups should make up for what they lose in the organizational one with the technology one. The next question is whether CIOs can build a business case saying that the technology can help the organization leapfrog the competition or make organizational processes better.  

Just because there are start-ups, I don’t want to leave them out of the equation. I’ve done that many times because it brings significant value to us. 

 

*The interview has been edited for length and clarity.