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This past year has been a time of reflection, new beginnings and growth at Siemens Advanta. One year ago, on January 11th, 2021, we planted the seed for our new blog and have seen it blossom throughout the year. Together with our leading experts in digital transformation, we’ve provided high quality content on everything from digital trust to how to achieve a growth mindset. To celebrate the year behind us as we kickstart the next, we put together a short-list of our 2021 blog highlights.

Unlocking the digital future is a journey with many stops – and our experts can support you in every step along the way. Here are 5 of our top blog posts to take you on a digital transformation journey, from beginning to end.

Often, the most difficult part of a journey is the beginning. It’s vital to know where to start, what you need for the road and where you’re going. To support you in these initial stages, we put together this Digital Transformation Checklist that can help the process flow smoothly:

Staying Ahead in Business: Your Digital Transformation Checklist

Every journey has its challenges, and they are best tackled when they’re expected and properly prepared for. IoT implementation is complex and requires a multilayered approach, and there are several challenges we often see pop up along the way. See what they are and how to overcome them here:

4 Challenges for IoT Implementation - And How to Overcome Them

Despite the heavy focus on technology, at its core IoT is still a people business. All too often, excessive focus on the technology itself means many leaders simply overlook the importance of planning and preparing with people in mind. And it’s one of the most common causes of failed digital transformations. So when your organization embarks on this journey, you’ll need everyone on board. To find out more about why the human factor is crucial for your digital transformation, give this blogpost a read, and remember – every ride is better when everyone on board is not just there, but enjoying it.

The People Business: Why Successful Digital Transformations Rely on the Human Factor

Obstacles are bound to arise and challenges are almost unavoidable on the journey to the digital future. So make sure you equip yourself with something that will help you and your organization tackle them head on – a Growth Mindset. We put together this blog post to explain what we mean and help you define and implement it in your teams and organizations:

What Rugby Taught Me About Growth Mindset

As of now, you took the first steps on the road, prepared for the challenges, made sure your people are on board and adopted a growth mindset. What’s next? To truly unlock the full potential of IoT, you need to realize you can’t make it on this journey alone. So, plug into the IoT ecosystem, rethink your definition of competition and start building the connected world with those around you. We’re all in this together.

From Egosystem to Ecosystem

Are you hooked and want to read more? Browse the rest of the content pieces our experts have carefully prepared for you and stay tuned for more stories, IoT tips and tricks and insights on how to get ready for the digital tomorrow. 

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Tesla is a fairly new company compared to the larger and more established original equipment manufacturers (OEMs). While traditional OEMs have been around for decades, Tesla only released its first all-electric car as recently as 2008. However, to many, Tesla car technology often appeared to be one step ahead of the rest of the car manufacturing industry ever since.

Perhaps this is why we have seen Tesla stock prices soar. Investors seem to see Tesla more as a software company similar to Google or Apple rather than an automotive manufacturer and therefore value Tesla at a price-earnings ratio of 358 versus Volkswagen at 4.7 and BMW at 4.9. They tend to like forward-thinking companies with a strong vision of the future even if feasibility of that vision has yet to be demonstrated.

Tesla's  success lies in the fact that it is perceived to be a leader when addressing CASE trends, namely connected, autonomous, shared and electric mobility. Its undoubted focus on data systems and the ability to leverage the full potential of digitalization is what makes the company a strong performer in today's market. Nevertheless, this focus on digital technology is just a part of the story.

Still, there have been multiple discussions in the automotive industry, as well as on Wall Street concerning the ability of Tesla to fully deliver on the promise it shows. To do so, Tesla must not only grow its fleet to maintain market share in a booming market for electric vehicles, but also further improve its profitability: Margins on new vehicle sales are often limited due to heavy price competition and the inability to reduce material cost.

Accordingly, it might be worthwhile to shift focus to aftersales as the future driver of profits. However, despite its superior position and high expectations, Tesla does not (yet) generate any profits with its aftersales business. While the 'average' OEM heavily relies on aftersales for their overall profits, Tesla still shows a deficit in their annual report.

This prompted us to take a closer look at Tesla’s approach on aftersales to better understand strengths and weaknesses.

We identified five different aspects that are essential for Tesla's aftersales setup.

Improving customer experience over the air

Leveraging the so called 'closed data loop' throughout its digital systems, Tesla is able to improve its product offering to the consumer even while it is in their possession. It can do this in almost the same way that Apple, for example, by issuing an upgrade or software update to the customer without the hardware needing to be brought into a service center. From the start, Tesla has focused on collecting data and using it for enhanced decision-making to continuously improve its products. This closed data loop has led to an irrefutable perception of sustained value and reputation among consumers. There is an associated risk with this success factor, however, which should not be overlooked: Since more data is collected and more functions can be manipulated over the air, the data security risks increase, as well. Furthermore, as vehicles sustain their perceived value for longer, future new car sales may be in danger since older but upgraded vehicles are still cutting edge.

Enabling hardware iterations

One major advantage is Tesla's ability to rapidly iterate its hardware. Being relatively close to the customer means that the firm can be very responsive when dealing with customer feedback and demands. Nonetheless, more product variance means increased cost of complexity. This could become a liability in the longer term, if it is not managed very carefully: Each introduced variant must be serviced and maintained throughout the product lifecycle. Establishing a strong Product Lifecycle Management will be essential for OEMs of the future if they want to control complexity costs while rapidly adjusting to customer desire. It can even be beneficial to design the vehicle in a way to avoid hardware changes. This can be achieved by realizing functions via software rather than hardware and by slightly “overequipping” the car. This approach would then later use the additional computation power, bandwidth or sensors / actuators to implement additional functions via the closed data loop.

Adapting to legislative constraints

Tesla chose a high-risk, high-reward strategy with respect to the various legislative and regulatory constraints it faces in different markets. This strategy allows the company to make very quick progress with nascent technologies, such as autonomous driving systems. Assessing the risks of potential legislation in areas like autonomous driving and data privacy has been cornerstone to Tesla's success so far. However, it necessarily exposes Tesla to significant liability risks. In many cases Tesla will set the standard by being the first. But what happens if legislation demands a different approach later on? As an example, Tesla currently faces minimal legislative coverage in the way it locks in customers to its systems and services. As the discussion around the “right to repair” demonstrated vividly, future legislation in markets, such as the EU, could open up competition and undermine the “closed ecosystem” strategy. If Tesla continues in this potentially risky manner but plans sufficient mitigation for future legislation, then it should be able to enjoy continued success.

Embracing a start-up mentality

Despite the rapid growth it has experienced, Tesla has successfully managed to maintain its “start-up mentality” in its governance setup. Unlike most major car makers, the company does not have a highly complicated governance operation. Indeed, Tesla is still running numerous strategic decisions through the firm's headquarters. This means the company can operate in an extremely agile way. However, with increasing sales volumes abroad and the potential for different legal restrictions, the central approach might complicate satisfying local customer demands. Also, as more and more spare parts are needed, establishing a local supply chain is becoming increasingly practical. Consequently, it might become even more attractive for Tesla to shift away from the centralized approach – while OEMs on the other side need to become more agile.

Service footprint and quality as Tesla’s Achilles' heel

In our analysis it became obvious that the company's service footprint and quality is not such a clear-cut case and might even be Tesla’s Achilles’ heel. Tesla is working to establish a lean service network and can provide mobile solutions to its customers. However, success in this field is limited: still, Tesla’s aftersales support needs significant improvement. Customers, for instance, criticize having to wait very long for appointments, low availability of spare parts, difficulties to reach customer service as well as low quality of the work provided. A 2019 study of Bloomberg showed that roughly 20% of customers claimed to be dissatisfied with timelines of service and adequacy of repairs. Interestingly, between Q4 2019 and Q4 2020, Tesla grew vehicle deliveries by 60% (from 112k to 180k) while store and service locations grew by 20% only.

The underlying problem seems to be a structural one: Tesla’s fleet has been growing at a pace which the aftersales network simply couldn’t keep up with. Spare parts are scarce due to most resources being used for vehicle production. The prioritization of new vehicles sales was right to grow the fleet and capture a significant market share. Now, as the BEV market booms, Tesla must continuously increase output to at least retain their current position. At the same time, it is imperative to build a strong aftersales network and significantly improve customer experience. This requires strong investments which of course will have a corresponding impact on Tesla’s balance sheet. However, the cost of failing to invest in the aftersales network might not be financial only. It could put the firm's closed ecosystem approach at risk as they would have to rely more on third-party partners to handle service requests. Dissatisfaction could ultimately also impact new vehicle sales.

Overall, Tesla is leading the pack in four out of the five dimensions we investigated. Tesla's Achilles' heel turns out to be the service footprint and quality. OEMs seeking to optimize their aftersales setup will want to learn from Tesla's best practices while playing to the strengths and quality of their existing service networks. Development potentials lie in closing the gap between them and Tesla for software and hardware product improvements, agile mentality and fast legislative adaption. At the same time, they should leverage their advantage in the service footprint and quality to provide superior customer experience. So, what should OEMs focus on immediately?

The main thing that OEMs can take from the case of Tesla is that they ought to leverage digitalization even more than they currently do. Tesla clearly shows several best practices and market-leading processes with respect to customer-centric operations. Because of its world-class data-driven business systems, its operational model is able to provide rapidity of action in the marketplace that few others could even hope to match. This means that OEMs can learn from Tesla's best practices regarding digitalization in three distinct ways:

Shine with customer experience

It is not just in the automotive sector that customer experiences are now closely entwined with their digital experiences. However, in car manufacturing, there is a specific way that OEMs can harness data to improve their customers' experiences. This would include leveraging data drawn from vehicles in the field to continuously seek new ways to improve software and in-car services. Doing so opens the door to the creation of new or additional revenue streams. Will customers pay for software upgrades and new digital services? The likelihood is they will, especially if the customer experience is demonstrably improved by such offerings. What's more, OEMs can benefit from direct-to-customer sales with this model and not rely on dealerships or other third parties.

Digitizing research and development

Secondly, OEMs should learn that the feedback data they obtain from the fleet that is on the road is invaluable to their future R&D processes. With data flowing in almost constantly, analyzing it well will mean being able to quickly iterate product features that have the highest customer relevance or demand. OEMs must make this data easily accessible to product designers to enable well-founded decisions. Furthermore, R&D teams should shift to implementing new functionalities in software updates – rather than with hardware – to allow greater speed with the rollout of new features, such as autonomous driving systems, for example.

Utilize digital technology to revise risky behavior

Finally, OEMs should learn from Tesla's corporate governance model and set up a more agile organization, one that is more able to react rapidly to technological change. Digitalization means embracing a fast-moving world in which risk-taking behaviors can be beneficial as long as they're based on solid data. Unforeseen developments in society, technology, and legislation need a governance structure that is prepared for them, and only big data analysis can help to inform the future size and shape of any commercial operator in the automotive sector. For most OEMs that will mean to tear down the silos that have established over time. With the fundamental shift we see in automotive, fundamental innovation is required: Salespeople must work with R&D and product management to find a solution for one global problem instead of focusing on many smaller local topics.

In summary, Tesla is a shining example of how OEMs can challenge the status quo. At the same time, the weakness in aftersales service footprint and quality as their potential Achilles heel demonstrates the value of grown structures and established networks. Although Tesla’s growth is breathtaking, there are limitations on how quickly they can build a strong aftersales presence.

The strength of traditional OEMs has been with their existing production and sales volume. This has allowed them more room for strategic action in building a sustainable and profitable aftersales network. OEMs should seek to exploit this natural advantage to convince clients with superior service as they introduce more and more BEV vehicles. At the same time, they should follow Tesla’s example and leverage their current aftersales networks to help them unlock the potentials of digital transformation and significantly improve their aftersales by digital means.

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When I was 14, I was thrilled to take on rugby. Despite being small for my age and not particularly strong, I enjoyed the team game and discovered that the physical and mental challenges of it allowed me to push myself in new ways. Due to my physique, it would have been no surprise if my playing experiences had gone wrong, but two years into it, my team and I were participating in both national and international competitions. I wasn’t the fastest or the strongest kid on the field, but I could use my agility to contribute to the team’s success. I almost always got tackled and believe me when I say that my parents were convinced my bruises would never fade. I will never forget what my trainer once said to me around this time: “We have guys who are faster, bigger and stronger than you. Do you know why I am taking you in for the competitions and not someone else? You are a team player. You think for the entire team!” I felt deeply honored. I was very much aware of my limitations, but that sentence has shaped me forever.

Later, as an adult, I realized that rugby taught me so much more than just being a team player. Remembering certain situations on the rugby pitch, I realized that I chose to act with different mindsets. Sometimes I had the tendency to limit myself, and my fixed mindset was holding me back, but often I had the desire to learn and could act on – what I know now – a so called growth mindset.

I had experienced what it took to have a growth mindset on the rugby pitch, but later in life, I found that it could be equally applied to the business world. Nowadays, we all know that in a business sense, the world is constantly changing – just like the shifting play on a rugby field. Therefore, companies need to adapt to meet the needs of their customers and markets. Consequently, organizations must stay curious, resilient, willing to experiment and adapt. Basically, if they don't nurture a growth mindset, they are much more likely to fail.

Looking back on my rugby career, I now see a connection between what I was doing then and what I do now professionally. When I was on the pitch, it was not just about teamwork and self-confidence as a player but something else that led to success. It was the mindset.

Academics have sought to define a growth mindset, take it from Carol Dweck, a professor of Psychology at Stanford University. According to her, a growth mindset is based on the belief that anyone's underlying qualities are things they can cultivate through their efforts. “Although people may differ in every which way – in their initial talents and aptitudes, interests, or temperaments – everyone can change and grow through application and experience,” she wrote. As mentioned by Dweck, there are two main mindsets with which we can navigate our way through life: growth and fixed. Both mindsets face challenges, obstacles, effort, criticisms, and responses to the success of others. However, they deal with them in very different ways. People with a growth mindset, for example, embrace challenges and see effort as a pathway to mastery. In contrary, people with fixed mindsets will avoid challenges, give up when they encounter obstacles and feel threatened when those around them enjoy success.

That connects with my business experience today as well. The adoption of a growth mindset is important to me because there is nothing more fulfilling than seeing how people around me are developing on their journey and are helping others develop. I believe that the biggest barrier for change in oneself is very often one’s own mind.

This is something emphasized by my colleague Bettina Rotermund, Head of Strategic Marketing at Siemens IoT: “We constantly need to try out new things in order to succeed and to be ahead of the curve. If you try out new things, then the chance of failing in the first place is quite high, but we need to push ourselves out of our comfort zone and try something out.”

According to Carol Dweck’s definition, both mindsets face the same characteristics. It’s about how people deal with challenges, obstacles, effort, and criticism. Let’s have a closer look at these four areas to better understand the differences between both concepts.

Challenges

When I began playing rugby, I was smaller than the others on my team. I didn't have their experience or power. However, I was open to what the coaches told me and to finding ways where my physicality wouldn't hold me back. In fact, I used my agility rather than brute strength to help my team gain momentum. In short, I had a growth mindset that embraced the challenge of a physically demanding game where brainpower often counts just as much as brawn.

Had I had a fixed mindset, what would have happened? Most likely, I would have never discovered I could manage on my own towards the ends of games as some of the bigger players tired. I would not have found that my team player status was so valued among my teammates, and I wouldn't have gone on to enjoy so many memorable wins with them either.

Obstacles

As Dweck points out, how you handle obstacles is a key part of differentiating a growth mindset vs a fixed mindset. Overcoming obstacles, as opposed to avoiding them, is key to demonstrating a growth mindset. One of the growth mindset characteristics I found I developed as a rugby player was derived from the fact that I was often too aware of my limitations as a player. An obstacle I faced on the pitch every time I played was tackling a bigger player coming my way. However, by learning the right technique and using his body weight against him in contact, I was able to learn how to overcome this obstacle.

With a fixed mindset, I'd have failed. I could have opted for what I knew was comfortable and put in a poor attempt at a tackle, of course. However, that would have meant another player on my team having to take responsibility on my behalf and double up the defense. That's the consequence of a fixed mindset that sees obstacles as insurmountable. It's something I hear in business: that things cannot be done. However, with a growth mindset, there is no obstacle that cannot be overcome.

Effort

In rugby, work rate, or distance covered by a player during a match, counts for a lot. Teams that have more tackles tend to win, especially in close games. The try scorers and kickers may get the plaudits, but the team effort is what counts. Anyone who has played the game rather than just watched it knows that. As the American Football Coach, Vince Lombardi, once said, “Individual commitment to a group effort is what makes a team work.” Lombardi went on further, saying it is also what makes companies and even entire civilizations work.

Looking back, it now seems obvious to me that this was a behavior of a growth mindset. I'm glad I put the effort in because, without it, I'd have not seen the benefit of a team working together and, perhaps, played for individual glory. Rather like the Gestalt theory of mind, we need to see the big picture rather than micro-managing our own efforts. In short, we're greater than the sum of our parts, but with a fixed mindset, you may never realize this truth.

Criticism

As a rugby player, our coach had some open and honest conversations with my teammates and I about what we were doing wrong collectively and what we needed to change tactically. As a kid you don’t like to hear about what you are doing wrong, that’s for sure. Of course, I tried to use the feedback that my coach gave me, but was I feeling good about it? Not really, and sometimes I couldn't take the criticisms that came my way, which affected my game. A classic example of a fixed mindset.

Had I had a growth mindset back then, I would have been able to deal with constructive criticism. I would have been open to it and even have encouraged it. That being said, the experience shaped me. I now see that players with growth mindsets don't take such critical conversations personally, and this is a quality common to most highly successful athletes, not limited to rugby.

I realize now that a fixed mindset affects us all to some degree. Staying in your comfort zone often means thinking you know what’s best for you, which you might! However, without a growth mindset, you will never know for sure what else you could have learned from a fair and valid critique of your work.

The Success of Others

Let's face it, winning is great. Playing in front of your family, friends and teammates as a kid and then winning is one of the greatest feelings of happiness and motivation. Losing on the other hand, that hurts. When I was a kid, I couldn't bear losing, and I could think of enough reasons why the other team didn't deserve to win or why it was just a close call that made them win. Now I know that this is a prime example of a fixed mindset, and I was not able to acknowledge the success of others because of my jealousy and my inability to draw anything positive from it.

Instead, playing in a team with a growth mindset, I should have been able to enjoy the success of those around me. Through failure we should support each other, both other teams and other teammates, and acknowledge the sacrifices others make to get to where they are today.

This is just as true playing team sports as it is working for a forward-thinking organization like Siemens Advanta. Corporate culture means adopting a growth mindset, and this helps everyone to enjoy each other's successes and savor the contributions we, as individuals, have made.

At Siemens Advanta, we have a mindset to be proud of. Of course, not everyone and especially not every individual has a mindset that is 100% and always that of a growth mindset, but our company’s focus currently is to encourage each employee to work on himself and herself and to develop constantly. Without it, the organization would not be the versatile innovator it is. Furthermore, with a fixed mindset, the various teams wouldn't enjoy each other's success to the same degree. That would be detrimental for the entire ecosystem of professional cooperation and, in the end, the commercial resilience of the company. Enjoying the success of others isn't just about a pat on the back, but also ensures organizations can change and adapt to meet all future challenges.

In sports, being able to embrace challenges, overcome obstacles, put in the effort that is required, take criticism and use it as positive feedback, and take delight in the success of others yields results. Especially in team sports, having each of these growth mindset characteristics means being able to get more out of the activity than previously expected.

Sure, a growth mindset in a team game can bring about more success on the field, but it goes further. Look at my story: Through rugby and other valuable life experiences, I have learned and have developed a growth mindset – but how do I apply that mindset today? The answer is I try to apply it to everything I do professionally. I've taken that growth mindset and continued to use it throughout my career, encouraging others to do so and step out of their comfort zones along the way.

Through my personal story and the deep dive of the individual five characteristics of the two types of mindsets – growth and fixed - it should become clear what significance a growth mindset can have for success in life, in work and, ultimately, for a company. For me, being eager to learn about curiosity in personal development is something that I am constantly learning over and over again. Especially as a leader, it’s so important to help others develop, help them to be open and curious, and to acquire new skills to constantly nurture their growth mindset.

By investing in partnerships and ecosystems as Siemens does, it is possible to develop a growth mindset that will lead to prolonged business success. As my colleague Bettina puts it, “Our markets and our environments are changing so rapidly and our innovation circle has become so short, we constantly need to try out new things in order to succeed and in order to be ahead of the curve.”

If you dare to dream, then you can step out of your comfort zone as well. I know many people will have fixed mindsets, and it is also important to accept this. No one should be unnecessarily critical of a fixed mindset if there is the willingness to change. For some, it may come easier to switch to a growth mindset than others; change is a process, not an end result! Set achievable growth mindset goals for yourself that allow you to step out of your comfort zone and to experiment with new ways of doing things. After all, experimentation is part of what it takes to obtain a growth mindset and shift away from a fixed one.

Remember that it doesn’t happen overnight, nor can one completely maintain one type of mindset only. We're all a blend of the two to some extent. As rugby players know, sometimes you need to tighten up your game while in other situations, you need to take more risks to overcome the opposition.

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Dr. Oliver Elbracht (Siemens Advanta): The Society 5.0 concept emerged from the Information Society and was initially expounded in 2016 in the Far East. In Japan, the new societal model was first defined to mean a human-centered approach to the ever-growing take-up of technology. Also called ‘super-smart society’, it aims to balance economic advancement more widely across society, for example, as well as create an opportunity to think about how certain social challenges can be resolved by digital technologies such as big data, Artificial Intelligence (AI), the Internet of Things (IoT) and robotics.

Having a closer look at the Middle East region, digital transformation programs are already in full swing: from oil fields, to manufacturing, to city infrastructure. Leaders in the region are aware of the societal and business demands and are already addressing the emerging challenges to shape the transition into the future.

I’m Oliver Elbracht, Managing Director for Siemens Advanta in the Middle East, and in the context of the event Transformena 2021 in April this year, I had the honor to sit down with several remarkable leaders from corporations in the Middle East to discuss how technology is creating a better tomorrow. Below you can find an extract from my discussion with Neetan Chopra, the CTO of Dubai Holding, Ahmad Aalem, the former Head of Strategy at The Red Sea Development Company, and Abdullah Albalushi, who holds the role of Board Advisor at The Zubair Corporation LLC in Oman.

The answers of these great panelists were certainly quite revealing, as I'm sure you'll agree with this insightful discussion.

Dr. Oliver Elbracht (Siemens Advanta): Ahmad, is focusing solely on technological solutions and their implementation enough to achieve the Society 5.0 concept?

Ahmad Aalem (The Red Sea Development Company): The Red Sea Development project is focused on creating a luxury tourism destination, that is based on the sustainable concept of regenerative tourism through a twin approach of person-centered and technological change. We are no longer settling for simply protecting and preserving the environment. We aim to enhance it. Indeed, we are committed to enhancing Saudi Arabia's natural ecosystem by 30 percent over the next 20 years. Crucially, it is technology that will enable this link between tourism, the environment and sustainability. Construction in sensitive areas may mean a modular construction method with manufacturing on-site, which needs to be sustainable, but this means you need people on board with the entire concept, not just the technology itself.

Dr. Oliver Elbracht (Siemens Advanta): How can technologies benefit people within the concept of Society 5.0?

Ahmad Aalem (The Red Sea Development Company): A good example would be our adoption of a smart destination platform, that allows guests and workers to monitor the environmental conservation enhancement across our lagoons – up to 2,400 square km of water. One of the best examples of the 52 smart technologies we've adopted is a wearable device for employees and vehicles to help maintain the balance between the environment and efficient construction. This device enhances employee welfare, too, by making sure they are supported by improved security measures, for example, by allowing an SOS signal to be sent. For now, AI and smart tech deployment is focused on our employees as we're in the construction phase, but in the future, it will center on a seamless visitor experience.

Dr. Oliver Elbracht (Siemens Advanta): The progress you're making building this unique destination truly amazes me. Now, from a smart tourist destination in Saudia Arabia to a smart city in the UAE. How does smart technology contribute to Dubai's objective to be the smartest and happiest city in the world?

Neetan Chopra (Dubai Holding): From Dubai Holding's perspective, there are two main actions. Firstly, we want to be in the arena, not on the sidelines, getting stuff done. We take action in transformation, innovation, and technology. Secondly, we support the technological ecosystem, which is enabling innovation and transformation. Further, when it comes to digitalization, you need to create magical, memorable experiences for customers – and employees alike. That is critical for the whole transformation journey. You can't be digital in the front-facing part of your business but at the same time keep your back office analogue. That doesn't give you true transformation. Additionally, we've found that investing in new business models as well as forging symbiotic relationships between start-ups and enterprises is crucial.

Dr. Oliver Elbracht (Siemens Advanta): How are businesses using digitization to manage large numbers of employees across various industries?

Neetan Chopra (Dubai Holding): Forward-looking organizations recognize that you can't do digital or transformational change without including your employees. It's critical! If you only focus on the customer receiving great digital experiences but forget about your employees that’s not a true digital transformation. The digital platform we have adopted at Dubai Holding connects all 20,000 employees. This offers them the same level of digital experience as our customers expect.

Dr. Oliver Elbracht (Siemens Advanta): The scope, scale, and economic impact of technology have been rapidly expanding in the Middle East. How have forward-thinking corporations integrated technologies in the region?

Abdullah Albalushi (The Zubair Corporation): The Zubair Corporation is one of the longest established business conglomerates in Oman. It started its business back in 1969. Of course, the business landscape at that time demanded a very different way of doing things. Then, focusing on building the infrastructure of the country in cooperation with the government was the priority. Only a few corporations were able to meet this challenge. Those which, like The Zubair Corporation, were widely perceived to be part of government and, consequently, to have a lot of social responsibility. The government's 2040 vision expects Omani corporations to continue to meet these social demands in the country, so it will shape our future as well as our past.

Dr. Oliver Elbracht (Siemens Advanta): What is your approach to future digitization as Society 5.0 becomes an increasingly felt reality?

Abdullah Albalushi (The Zubair Corporation): We inherited various complex legacy systems, often by acquiring and merging with other companies. Therefore, after 50 years of business a check was needed to re-energize the company. We found that there was a big gap with what the younger generation expects, especially with mobile and digital technology. Given that we had historically dealt directly with the government, we didn't always need any of these technologies. Due to the Covid-19 pandemic, everything moved to digital, causing business and social challenges for us. In 2019, we conducted an operational review. Based on that, we developed a digital transformation strategy. We entrusted Siemens Advanta to be our partner in the assessment of our digital strategy, and we are now 50 percent on the way down the road to delivering it. As others have said, the human element in this is creating the biggest challenges, but it also affords us the biggest opportunities. We want to create a truly human-centered ecosystem where all our business units will operate on a single platform, the foundation to innovate and shine.

Dr. Oliver Elbracht (Siemens Advanta): It has been an exciting journey you have been on – not only within Zubair but really for the Omani nation.

I'd love to continue this to experience how you and all of your corporations are making a difference to Middle East societies within the contexts of Society 5.0. I'm excited to see how the Middle East will continue to develop this human-centered, technology-based society in the years and decades to come. It’s fascinating what the upcoming decades will bring and I'm convinced that we can foster innovation that will create a bright future for us and for future generations.

Thank you very much all of you for joining this discussion.

Interested in watching the full dialog? Click here to watch the recording of the panel discussion: Society 5.0 – The Big Social Transformation.

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As the global pace of digitalization accelerates, businesses are becoming ever more immersed in technology, regardless of sector or core activity. But the rush to establish a use case and demonstrate the Return on Investment (ROI) has tempted many to overlook the ethical implications of technology.

But what exactly is ethical tech?

The World Economic Forum defines it as technology with a clear moral dimension. It should be designed and deployed with sufficient forethought to account for and promote human and corporate values, ethics and norms with fairness and transparency at their heart.

First, it’s a golden opportunity to promote and safeguard a trustworthy reputation and avoid damage. It’s clear that there is a huge and proven potential of digitalization as a force for good within both business and society. However, mistakes happen as the field of Artificial Intelligence (AI), for instance, is fairly new and undefined, and many companies and institutions are still experimenting. These mistakes attract a lot of media attention, such as Amazon’s HR program that evaluated job applicants’ resumes to the detriment of women, or Facebook’s AI robots that had to be shut down after they started speaking to each other in their own language. Consequently, such cases raise general unease over the use of AI in decision-making and are cause for concern. Nonetheless, it’s important to state that these mistakes give a boost to development. It’s up to us to learn from them fast and establish improvements to build ethical tech in business and society.

Developing and embedding an ethical technology mindset in your organization when undergoing a digital transformation is crucial, since the consequences of an ethical tech incident can be substantial. With evolving digital technologies, ethical standpoints must evolve, too. What effect does it have on your customers, partners, shareholders, and employees? In the long term, an ethical tech company can enjoy competitive advantages by the chance to earn the trust of stakeholders by promoting human values.

How can you embed ethical tech into your digital transformation from the very start?

Conveying a clear message on how you use data, and why you use it, should be a priority for executives committed to transparent operations. With ever growing quantities of data collected and stored from an increasing number of devices and sensors, an organization’s capacity to interrogate and analyze that data to gain valuable business insights becomes more enhanced by the day.

For instance, consumers’ personal details are now routinely collected and combined with an array of information harvested from their digital footprint. This might include social media posts, the route driven to the office, the buying history, internet search histories, and so on. The importance of data should not be underestimated; with society claiming the right to shine a light inside business practices to confirm whether corporate values align with their own, proactively ensuring transparency in how data is used becomes a business priority. This is also true for companies operating in the B2B sector since stakeholders are increasingly interested in ethical tech in the entire supply chain.

How can you establish data transparency?

An organization’s approach must be guided by a moral and human compass. To become an ethical tech company, leaders first need to establish and publicize company values. These should be based on customers’ expectations of openness and consent; The guiding principles of the General Data Protection Regulation (GDPR) illustrate that for the European Union: lawfulness, fairness, transparency, integrity, and confidentiality. That gives some guidance for the use of new technologies involving data and AI to strengthen our democracies, give people a voice and make meaning out of data to create a better future. Second, it should be ensured that the way data is used aligns with the organization’s values.

The overarching imperative for digital organizations should be to work on building trust in the security of that data and promote transparency around its usage. And that starts with policy and procedure. That’s why it’s important to build a robust data usage. This involves categorizing data methodically and keeping close tabs on all employees who have access to it. Everybody can then be aligned around your company’s approach to accountability for data, how that data is used and how it’s protected.

As a crucial part of ethical tech, cybersecurity should be baked into your plans from the beginning, with digital transformation risk assessment undertaken as your project is being developed. It should feature at every stage and needs to be preventative, detective and defensive. This reflects the reality that security is a multi-faceted and on-going challenge, and one that responsible and ethical tech companies rely on to build trust in their use of data.

By gaining user trust, companies will be able to gather more and more data to enhance their specific offerings. Many argue that the real value for users and customers will be lifted when companies start collaborating more in ecosystems, rather than trying to reap small gains through acquiring more data than their competitors. However, moving from ego system to ecosystem doesn’t mean that you share your data indiscriminately with everyone. Ecosystem means that you share information only with trusted partners who have a common and aligned approach to collaboration, where it is clearly defined who contributes what to achieve a common goal – for the good of companies and its clients.

A commitment to an unambiguous approach to privacy will help to build stakeholder trust in the protection of data. Historically, big data has been characterized by a ‘catch-all’ approach to maximize data collection, allowing organizations to quantify an individual’s everyday life to the benefit of the company collecting the data. Because organizations have been keen to exhaust all its inherent value, it’s not been uncommon to sell on or share customer data in a secondary market.

But most customers don’t fully understand the specific purposes behind collecting and sharing personal data. A fear that many users have is that their information is accessed by unknown parties and that their everyday choices are being tracked, ultimately undermining their trust in the privacy of their data. Though being mostly illegal but hard to trace, algorithms designed to influence political opinion, or to promote “fake news”, are presenting us with ever more dangerous applications that have the potential to erode privacy.

How can you establish stakeholder privacy?

Go out of your way to demonstrate good behavior and compliance, and actively seek to banish opaque policies that technically tell the truth, but don’t quite tell the whole story. Carte-blanche to use data as you like is no longer acceptable, and if your organization has plans to sell personal data onto third parties, your customers need to understand that in full, actively give their permission, and be prompted to grant that permission at regular intervals.

Giving stakeholders control over their data builds transparency and aligns your organization’s everyday activities with the values it outwardly promotes. This is already a legal requirement in some jurisdictions; in the EU, for example, the GDPR outlaws any processing or use of data that an individual has not given consent for. In fact, it explicitly states that “data subjects” can withdraw consent whenever they want, and those who hold the data are obliged to comply.

A variety of more sophisticated technologies, including AI and machine learning (ML), continue to move into everyday life. According to The Harvard Gazette, global corporate spending on AI software and platforms is expected to top USD 100bn by 2024.

In addition to some of its most visible and benign uses in buying recommendations on websites or in translation tools, AI is also already used in a wide array of more critical scenarios, from helping medical diagnoses and assessing mortgage applications to keeping driverless cars safe. As a result, human oversight is instrumental, not just to ensure that disaster can be avoided in the event of technology breakdown, but to act as ultimate arbiter of a technology that is still likely to produce further ethical dilemmas. In short, ethical tech requires humans to be responsible and accountable for the final decision, not AI. Complex decisions should be more transparent, with the roles of AI and human intervention clearly visible in the process.

How can you establish technology trust?

Organizations must establish processes to ensure that they and their stakeholders know exactly what technology is being used, where it’s being used and how. Creating trust among clients, partners and employees is dependent on ensuring methodical and consistent human oversight of critical systems.

That’s why you should encode your organization’s values. Digital technology can be developed to take account of biased training data which results in algorithms and AI decisions so that the technology operates within the bounds of your company’s values. With so much riding on client and customer trust in the fairness and ethics of modern digital systems, encoding ethical values so that they can be assessed and measured against technological business operations will help bridge the potential trust gap.

One way around developed biases is to explain how AI decisions are made. For example, with medical diagnoses that rely on AI, healthcare companies have examined ways to give each diagnosis a confidence score based on a variety of contributory medical and lifestyle factors, allowing clinical staff to follow the workings of algorithms and introduce the benefit of their medical training and experience into that diagnosis if needed. AI is not replacing human beings, it’s enhancing them. But human beings are still in charge.

Taking it a step further, Explainable AI (XAI) is bringing further transparency to decision-making. Defined as a system that can explain its decisions, as well as the rationale for those decisions and some notion of how it might behave in the future, the self-learning AI platforms that power driver-less cars, for example, now have the ability to “explain” their choices. In this case, choices such as changing lane or accelerating or braking for no apparent purpose – and how factors influencing those decisions – are weighted. Yet, research on XAI is still fairly young and many questions remain unanswered, leaving applicants of AI with the sometimes tricky trade-off between the performance benefits of an unexplainable state-of-the-art model and an explainable, but lower performing, commodity model.

Ultimately, leaders must decide how to put their company values into practice to guarantee stakeholder welfare through ethical tech. The global tech industry can only build trust by using technology in a responsible way that promotes transparency and a purpose to that technology that focuses on the good it can bring about for all of society.

There is an opportunity for leaders to gain a significant competitive advantage by making trust a business-critical issue.

 How can you establish this?

It’s equally important to drive ethical tech both from the top of your organization, and from the bottom up, too. Leaders need to communicate their company’s values and ethics, formalize them in policies and make sure technology use is aligned with those values. That process can be set in motion by making sure employees understand the commitment and dedication to ethical technology. When leaders communicate that clearly, it helps others throughout the company to buy into that sense of responsibility, and will inform ethical decisions aligned with company values.

Digital technology use cases that are specific to your company can help employees test your decision-making framework against real-life scenarios, and that framework can be used to question and adapt the way individual employees make responsible decisions. In turn, employees are then more disposed to develop a natural ethical mindset of their own, one that is more likely to manifest itself in all facets of their roles.

And that’s where the bottom-up approach comes in; by taking your cue from the Tech for Life movement – which establishes a “code of honor” for the tech community regardless of a person’s status within a corporation – you can help foster this bottom-up movement in the direction of socially responsible tech based on a human moral compass. After all, although it needs to be guided from the top in the design stage, ethical tech is a responsibility shared between each and every one of your employees across all functions.

Remember, data regulations exist to enforce broad minimum standards of protection; for example, the EU’s GDPR can penalize data misuse and breaches with fines of between 2% and 4% of worldwide annual revenue, or flat fines of EUR 10m to EUR 20m respectively, whichever is higher. Similarly, for the use of AI, the EU is acting as a driving force to establish legislation and we can expect laws and regulations to come soon. But if ethical tech comes from the bottom up and from the top with conviction, those values and ethics will infuse every decision with responsible ethical corporate values, policies and procedures that guarantee even higher standards around data use.

Ethical safeguards, policies and procedures can only go so far. To truly master the art of building trust in a burgeoning tech landscape, you need to devise ways to equip employees with a mindset sensitive towards ethical standards. The role of leaders today is to embed those values into the culture of their organization, from the bottom, up and from the top, down. That way, you’ll gain trust in how you use and collect data, in how your technology is deployed to make decisions, and in the privacy of all stakeholders; top-down and bottom-up leadership will ultimately help weave an ethical technology mindset through the fabric of your digital transformation to prioritize the relationship between technology and human values. Everyone involved in tech should apply human principles guided by an ethical compass to ensure that tech is used in a responsible way.

So, set out today to make sure your organization is becoming an ethical tech company in parallel to embarking on your digital transformation; start at the planning stage, apply ethical tech standards iteratively depending on your industry and business, and your efforts can offer a competitive advantage in the form of the trust of stakeholders, clients, and investors alike.

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The number of connected vehicles around the world is growing rapidly. It's estimated that by 2025, when 100% of all new vehicles to market will be connected, there will be more than 400 million connected passenger vehicles alone. More connected cars require connected vehicle ecosystems in smart cities to function efficiently. The market is expected to see a compound annual growth rate (CAGR) of 17% between 2019 and 2027, amid increasing population density in urban areas. According to the UN, nearly 70% of the world’s population will live in cities by 2050. So, the performance of modern networks to support connectivity is becoming even more critical.

Whether automotive industry players will be successful or not depends on how they leverage ecosystems and combine technologies like edge and cloud computing. In tandem, they can deliver a wealth of benefits, including safe and reliable autonomous driving, energy optimization and real-time navigation, as well as new data and subscription services.

To pave the way for connected vehicle ecosystems, read on!

So what, exactly, is a connected vehicle? It’s one that can be linked to different services and devices via wireless networks. This includes other connected car technology such as software, entertainment and communications, or parts of infrastructure such as traffic signals, emergency centers and navigation aids, as well as other vehicles, and even bicycles, cyclists and pedestrians.

A connected vehicle ecosystem refers to everything that will connect to the car via smart-city platforms. It’s the sum of every fixed or mobile device or sensor that can connect to tomorrow’s cars. It will be able to collect and process information on everything from location, weather and driving conditions, to parking availability, congestion or hazards ahead.

In short, both will become part of tomorrow’s inner-city networking with a seamless and continuous exchange of information and data via fixed devices, sensors, and via the edge and automotive cloud services.

As 5G grows, and eventually gives way to 6G a decade or so later, those connected vehicle ecosystems will be key to providing state-of-the-art vehicles with the ability to become sentient machines. They will rely on the transfer of data through wireless communications for everything from situational awareness to predictive maintenance.

How will this benefit users?

Broadly speaking, the term “users” will include not just passengers: fleet owners, smart cities, original equipment manufacturers (OEMs) and original equipment suppliers (OESs) will all stand to gain, too.

Connected vehicle technology will offer services including driver assistance, safety features, entertainment, well-being, as well as vehicle and mobility management. Vehicles could receive rapid product iterations over-the-air to further improve software functions. Users will be automatically connected to ecosystems to enable easier road charging and reactive traffic monitoring and management – improving the driving experience significantly.

For businesses, connected vehicle technology will also open up the potential for monetizing connected-car data with big data analytics. Connected vehicle ecosystems will also soon enable the wide-spread use of fully autonomous vehicles and will offer predictive maintenance and decentralized fleet-management features, while significantly improving vehicle condition monitoring, predictive analytics and optimized fuel/hydrogen/electricity consumption. From a strategic perspective, many of these benefits will ultimately aid a gradual move towards a vehicle-as-a-service model, where subscription models based on usage rather than ownership gain market share.

The potential for businesses and passengers alike to unlock the future of mobility is clear. But how to untap it?

The automotive sector is awash with buzzwords relating either to the edge or to the cloud. So far, key players have tended to focus on just one of these technologies. The sweet spot, however, is in the combination of both edge and cloud: each will need to work together to truly enable connected vehicles.

Despite an on-going and increasing shift to the cloud, pushing everything to the cloud is expensive. And although current cloud connectivity cannot, without latency, support uploading and processing the 1 GB of data that connected cars will generate every second, the ability to do that isn’t far away.

So, from a strategic perspective, when many functionalities can already be handled on the edge, the key question for automotive players is: which particular service categories warrant the cloud and which can be processed on the edge?

Automotive edge computing involves data processing and analytics physically close to where data is generated and collected. At its simplest, it narrows the gap between data storage and the devices that rely on it to function within a pre-determined timescale. This means that latency problems can be resolved by meeting the sub-1 millisecond reaction time that safety-critical features demand. Not only is it more dynamic and flexible than the cloud for certain applications; for many service categories, it’s also more cost-effective. When combined with IoT technology, edge computing saves bandwidth, allowing you to allocate resources efficiently.

Seeing the car as part of the greater ecosystem is key, and building your ecosystem boils down to two issues:

1. Which partners do you include?
2. How do you combine both technologies to focus on the sweet spot?

Choose your ecosystem partners well:
Many providers claim expertise in either edge or cloud technology, but choosing the right partner is not as easy as it seems. Include your entire value chain – from the very beginning, think about hardware and software providers, developers, national government organizations, OEMs, OESs and beyond. After doing so, it’s crucial to pull the network together by fostering continuous interactions, enable seamless interaction based on an open technology architecture and utilize joint data to continuously maximize potentials of the collaboration within the ecosystem.

Decide where to combine edge and cloud technology:
Which vehicle features should be in the cloud and what makes sense on edge? The crux lies in differentiating between service categories to make those decisions more manageable. Driving assistance such as collision avoidance, for example, is currently better suited to automotive edge computing, where a low latency, or fast reaction time, is needed. The edge also supports other safety-critical features such as lane discipline or traffic-sign recognition. Furthermore, vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication via Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) can be used to transmit e.g., the previously detected red traffic light to another vehicle. Navigation, on the other hand, can be dealt with by automotive cloud solutions, as can features such as energy efficiency, vehicle security and multi-level authentication. In addition, some subscription and premium data services may warrant the higher costs of cloud. In short, automotive cloud services are suitable for use cases in which higher latency is acceptable or desirable, and where subscription revenue streams warrant the cloud.

How you maximize the benefits of each technology, once safety-critical features are assured, ultimately comes down to a strategic choice: which applications and services justify the higher costs of the cloud, and which ones can save bandwidth on the edge?

The key to developing an edge-cloud ecosystem is to view vehicles as an integral and integrated part of a much bigger system. A connected car is connected to a fleet, which is in turn connected to entire smart-city platforms comprising everything from highly dynamic and unpredictable road and city users to transient static features such as adverse weather, lane closures and accidents.

With demand for smart-city ecosystems that encompass passenger vehicles and their connected vehicle platforms as part of the whole system, it’s crucial to build a partner ecosystem along the whole value chain. Prioritize the integration across the partners and focus on easy technical connectivity. Here, it doesn’t have to be an edge vs cloud decision: It’s crucial to find a way to combine them efficiently for each individual use case!

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It’s no secret that the utility industry is changing. Currently, the sector is responding to pressures from customers and regulators alike, with growing demands for a significant reduction in carbon footprint, smarter and more efficient energy networks, alongside seamless, personalized digital offerings. In addition, the impact of the COVID-19 pandemic and the global adoption of remote work has encouraged businesses and individuals alike to re-evaluate their priorities.

The result? Energy companies are having to vastly rethink how they do things – and utility data is at the core of this transformation.

Global digitization is leaving no stone unturned. Within the energy sector we are witnessing first-hand the transformative effect this is having upon the way we generate, distribute, and utilize all forms of energy. The transition from the Distribution Network Operator (DNO) to the Distribution System Operator (DSO) model has been widely adopted to address the emerging needs of a net-zero energy sector. As a result, we’re looking at the emergence of a digitized, more sustainable approach that better meets the needs of stakeholders.

The DSO’s challenge is to rollout and integrate smart technologies, accessing and utilizing associated data sets throughout a connected IT estate to accommodate and even accelerate the proliferation of Low Carbon Technologies (LCT’s).  

For example, with smart metering, connected technologies and data analytics, DSOs can benefit from increased visibility of energy flow – between both traditional and renewable sources, and consumers and prosumers – to identify interruptions and restore supply automatically, while also monitoring aggregated consumption trends.

Global utility businesses therefore must strategically invest in the right technologies, services, and partnerships to successfully deploy the System Operator (SO) model.

The benefits of DSOs are manyfold. On the one hand, they enable faster grid connections, increased insight and engagement for their stakeholders and greater flexibility in the distribution of energy. On the other, they can act as a wider catalyst for a data-driven evolution in energy with both the systematic access, cleansing and utilization of existing data to drive new value within the DSO and additionally with the provision of internal data towards external stakeholders. The growing calls for ‘open-data’ aim to support the core DSO remit of flexibility in network management, whilst enabling the development of progressive LCTs and innovative services – all of which are required for a true net-zero economy.

So, we know that the DSO model is key for moving the utilities sector into the future, and that the success of that model relies on data – but where does that data come from? The answer here is smart technologies that can capture and transmit information in real time.

We’ve all heard about the Internet of things (IoT) and the trend of wearable tech or home devices that review data on a continual basis to create a bigger picture analysis. That same trend has translated into energy, manufacturing, and other sectors with the Industrial Internet of Things (IIoT). Focused on enabling transparency, smart decision making, predictive maintenance, asset management, and so much more, the global IIoT market was valued at USD 216.13bn in 2020. The energy and power sector is currently responsible for approximately 20% of that.

However, it’s important to remember that the data alone is not enough. In order to make the best use of the data available to them, companies will need to develop and implement detailed digitalization strategies – likely with the support of a trusted partner – which outline specific steps, the required data insights and how they will be used to drive the organization forward. By targeting business challenges, defining the suitable use case, and identifying relevant and prioritized data, each stage of this transformation plan should provide value to the data owner. It also ensures that the utility data gathered through the IIoT provides a comprehensive picture, rather than a fragmented one.

The need for digital transformation is driving utilities to change their business, operating and information models – and this change has momentum. According to Grandview Research, global spending on IIoT is expected to grow at a rate of 22.8% per year from 2021 to 2028, to over USD 1tr, with the energy sector following only manufacturing as the second largest contributor. This means that the industry is primed to incorporate energy data into their operating systems and refine the way they do things.

This will go a long way towards decarbonization and net-zero goals set by governments and corporations alike. The EU, for instance, has set a target to cut carbon emissions by at least 55% by 2030 (compared to 1990 levels). Meanwhile, the UK plans to reduce carbon emissions by 78% by 2035 and the US has promised to achieve a zero-carbon power sector by 2035. These governmental initiatives are putting pressure on power companies to change their operating models. By choosing a data-rich, efficient approach to production and distribution, utilities can take a big step towards meeting these goals.

Focusing on digital transformation and data enablement also makes business sense for utilities companies. When it comes to the traditional model for distributing power – the passive model – growing and enhancing a network requires laying costly copper pipes and building more infrastructure across the board. Without the consumption and transmission data of the digital model, companies aren’t able to build smartly and efficiently, and scaling is a costly endeavor with no guaranteed success.

Alternatively, utilities companies that are embracing digital infrastructure are already seeing a return on their investment. In fact, according to Kearney, within the first two to three years of their digital transformation, energy companies stand to earn 4% more per year. That increase jumps up to 25% per year in the long term – and includes growth across generation, distribution, sales and support services. This will be led primarily by the adoption of data-driven decision making and active asset intelligence across the board.

In terms of broader savings for the industry, the World Economic Forum (WEF) has estimated that the electricity sector could unlock USD 1.3tr in value by 2025 by leveraging service platforms, smart devices, cloud and advanced analytics. This includes 15.8 billion tonnes of net avoided carbon emissions. With these tools, the WEF believes companies could improve the asset lifecycle of their infrastructure, optimize electricity network flows, and put the customer at the center of their operations.

The value proposition is clear – but there’s still a long way to go before the utilities sector is capable of taking full advantage of these financial wins. Leaders now have a responsibility to prioritize investments in digitization and reshape how their business operates – with data at the center.

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The shift to a digital, decentralized model fueled by data is not going to happen overnight – and it won’t be without its challenges. DSOs face a particular challenge at the moment as they need to adapt to supply increasingly electrified environments – global sales for electric vehicles are projected to hit 31.1 million by 2030 –, accommodate the growth of distributed renewable energy sources, and focus on the development of local sustainable markets vs. distributed national ones. But this can’t be seen as an obstacle to development.

Investing in these transformative initiatives now – initiatives that will be made efficient with the right data and tools – will be paramount to meeting the needs and requirements of today’s and tomorrow’s consumers.

Not making these changes will do more than leave the utilities sector in the dark ages. As large contributors to carbon emissions, power companies have a big role to play in meeting the ambitious targets set by governments around the world. And without adopting a digital, data-enabled operating model, then decarbonization won’t be feasible. They are ultimately two sides of the same coin.

As utilities companies embark on their digital transformation initiatives and adopt smart, IIoT technologies that equip them with data, they’ll be on their way to meeting customer and industry expectations. With a detailed transformation plan that highlights the value of that data, business leaders will be able to use that information to make proactive decisions that set the business up for success.

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The transformative potential of IoT as an enabler of digitalization across all industries is increasingly seizing executives’ attention. However, our partner pulse survey with Harvard Business Review Analytic Services also identified certain challenges regarding IoT that potentially hinder business leaders to kick off their digital journey.

Although three quarters of respondents said they felt under some pressure to quantify and leverage the potential of IoT benefits before their competitors get there first, only 10% were able to measure the return on investment (ROI) on most of their initiatives. Nearly half were worried about change management challenges, 45% admitted to not knowing where to start with IoT, and a majority reported they were still trying to identify potential use cases.

With the underlying imperative that technology must serve business, people and our wider society, how should leaders overcome these specific hurdles to make their IoT initiative a resounding success?

Let’s have a closer look at the top four IoT adoption challenges.

90% of executives struggle to accurately measure the return on investment (ROI) of their IoT initiatives. This is one of many common IoT challenges and issues in large and complex organizations that raise executives’ doubts in the impact and value of IoT for their businesses.

The return on investment is defined “simply” by the ratio between profit and the cost of the investment based on discounted cash-flow calculations. However, it is still a huge roadblock for leaders to accurately measure and calculate it.

How to overcome this?

The first thing to remember is that leaders should only consider starting their digitalization and IoT journey once they have established a clear business goal. Companies tend to “fall in love” with a new technology but forget about the actual business value it should bring to them. The real value of digital initiatives can be seen and assessed more clearly if executives flip the question of ROI around: Instead of looking for the ROI of a specific IoT solution, they should be asking how to calculate the ROI when using IoT to solve a business problem and create a lasting impact. Only when defining the business goal of the initiative as precisely as possible, executives will be able to accurately measure whether it will pay off in the future. The way the solution solves the business problem defines the return of the initiative.

Therefore, the investment budget always needs to consider transition costs for change management, the adaptation of processes and tools and skill development next to technical costs.

Only when taking all of these aspects into consideration, the real return of your investment will become visible. More details about accurately calculating the ROI for IoT initiatives can be found here.

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The failure rate for IoT projects remains high – between 60% and 85% – reflecting the many IoT challenges. Many of them refer to the people domain, like the need of engaging all employees in the digital transformation efforts.  

As pointed out earlier, one of the main causes of failure stems from a misconception of IoT as an initiative based primarily on new technology. In truth, IoT is a people business, and the success depends on taking the employees on the journey from the start. However, executives often don’t know how to prepare their employees for what is to come.

How to overcome this?

For employees and leaders alike, some of the most challenging aspects of IoT are characterized more by the need for a cultural transformation than a technological one – this requires a structured change management approach from the very beginning. First and foremost, an integrated approach across both tangible and non-tangible elements of the transformation is needed. That means bringing together technological and process elements with social factors such as corporate and team culture. Both aspects are equally important to create employee buy-in.

Additionally, an understanding and acceptance of evolving target states – part and parcel of any digital transformation – is crucial. Here, leaders need to foster an agile mindset. During the transformation, needs and overall objectives might change unexpectedly – that’s just natural. Flexible concepts and an agile approach are needed to constantly adapt to new requirements – while not losing people along the way.

In the context of IoT projects, classic change management levers – communication, leadership, team configuration and ongoing training – have to be adapted to the digital environment. One possible starting point for change management measures is to check the employees’ readiness for change in general and digital readiness in particular. This will help to identify the organizations change capabilities and address any gaps in digital core competencies and to prepare the workforce for new and changing job requirements.

To carry the digital mindset into the organization, the successful approach of one of our clients, a leading energy equipment supplier can be adopted: the client identified digital role models in their workforce to act as ambassadors and coaches. With full access to the digital transformation strategy, their role is to report back to colleagues and help lead them through the necessary changes, meaning nobody’s left behind. In our client’s case, it gave their engineers clarity on future roles and training, and resulted in their engineers proactively engaging in the successful launch of their AI design algorithm for their parts. However, this is only one specific action that can be taken to put your employees at the core of your digital transformation journey.

Business leaders are naturally worried about the risk of cyberattacks. With more and more devices and assets connected and networked, executives are becoming more wary of an increased digital attack surface and greater vulnerability. IoT security challenges can range from customer data theft to ransomware to industrial espionage and are driven by increasing professionalization of cybercrime. As the number of vulnerabilities increases, the attack surface becomes more important to defend.

Also, connecting the OT and IT world brings along several unknown cyber challenges as the number, diversity and complexity of connections amplify: a secure linking with the cloud, the industrial control center or third-party providers need to be guaranteed while availability and connectivity of the assets need to be ensured at any time. In general, the discrepancy between the OT and IT network logic needs to be considered to define what are the challenges of IoT here.

Nearly half of respondents in our HBR study rank cybersecurity as an issue that merits close attention, and over one third report that it’s among their top concerns. 

How to overcome this?

Step one is to understand that even if a company already has complex cybersecurity defenses in place, they usually only protect the IT world. In the IoT / OT landscape there are new problems which is why companies need to adapt their approach. A common pitfall is to threaten the IT landscape by neglecting the operational technology (OT) network. This brings additional risks which the companies need to mitigate.

Companies should use the planning phase to embed security into their IoT project and establish threat risks with a full IoT risk assessment while projects are still being developed. Therefore, a risk-based approach that prioritizes vulnerabilities and threats can be the answer: Starting with prevention is crucial to make it as difficult as possible for an adversarial to break into the systems. If the cybercriminal finds a way in, companies need to detect the threat actor as fast as possible.

Especially in the IoT environment, it is essential to have a risk-driven strategy in place and create transparency over assets. Cybersecurity departments need to make sure to know, manage and track all endpoints and devices during the entire lifecycle. Weaknesses need to be identified and therefore, effective threat monitoring solutions implemented. Applying new principles like “zero trust” to control and verify all actors in the network as well as every data stream can help to prevent disruptions before they occur.

By connecting assets with digital environments and data analytics, IoT can be a main enabler for asset monitoring and tracking, effective resource management and maintenance optimization – especially in the manufacturing environment. Measures of predictive maintenance can for instance prevent factories from unplanned production downtimes and therefore, can ensure production and supply chain functioning. At the same time, data-driven decision making can optimize maintenance schedules and improve investment decisions.

However, the big initial question is: how to actually realize the connection between operational (OT) and information technology (IT) – especially for aging, proprietary manufacturing assets? Due to this difficulty, this is often believed to be one of the main IoT implementation challenges as it requires greenfield sites which hardly exist in reality. This makes business leaders doubt the business potential of IoT. Also, IoT implementation leads to more connected devices in the ecosystem causing higher asset diversity, volume and in the end complexity – especially when trying to adapt many diverse IoT technologies at the same time. Localizing even more assets in real time might be challenging as well.

How to overcome this?

Companies should apply a step-by-step, structured approach: Sensors and connectivity should be added gradually to existing machinery, allowing to harvest data and gain valuable insights one machine at a time. This gradual implementation allows executives to optimize existing assets and control costs. A carefully tested and piloted path can equip machines with IoT capability causing only minimal impact on the production line. Doing so, executives can demonstrate the overlooked value of IoT, which lies in upgrading brownfield environments consisting of older equipment step by step. Sensor monitoring, data analytics and predictive maintenance, for example, can be deployed on legacy assets without widespread risk to the production lines.

To find the right sensors and data collectors, might even require a certain amount of creativity – especially in brownfield environments. Established machinery is often hard to connect to state-of-the-art sensor technology. For example, to obtain data from a shop floor with machines not originally designed to deliver operational data, a camera system monitoring the shop floor could provide valuable data that can be further processed and evaluated, thus creating a potentially valuable use case.

The complexity and diversity of data often requires major efforts in processing and evaluating the information generated from assets. To overcome this, implementation teams that include IT experts, domain experts, and data scientists are needed. Furthermore, in complex cases, no company can do this alone, so a co-creation between different partners of an ecosystem can be the answer.

After tackling these initial hurdles, the potentials of IoT for asset management are limitless: Optimizing the OEE (Overall Equipment Efficiency) makes it possible to not only manage the physics of assets, but even their usage itself. Track & Trace solutions can be the foundation of an e2e supply chain, connecting inter- and intra-logistic. 

IoT has little value without an established business case, benefitting people and society. Here we’ve identified what are the main challenges of IoT faced by organizations and their potential solutions.

What they show is that a change in perspective of the way the ROI is calculated can reveal the true value of IoT; that the employees should not be treated as an afterthought; and that the machine assets should be digitalized gradually and creatively to maximize output whilst controlling costs and optimizing utilization. And all insulated by effective cybersecurity defenses to shield the data, assets and networks from attack while ensuring availability.

What executives can achieve by overcoming these hurdles presents an even greater prize than merely the sum of their parts: A resoundingly successful IoT project that generates benefit to the company and its stakeholders.  

With the confidence to face the challenges head-on, executives are more likely now than ever to be able to seize the significant advantages of IoT to leverage these advantages ahead of their competitors.

Now’s the time to seize the significant opportunities that digitalization offers.

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By setting free the power of IoT, we will be able to transform our economy – making the way we produce more efficient and sustainable and helping economies reach their ambitious decarbonization goals. The Industrial Internet of Things (IIoT) plays an important role in this context as it acts as a key enabler for sustainable manufacturing, ensuring economic efficiency as well as helping to reach extensive environmental goals.

With the following examples, we hope to give you one or the other inspiration on how you can actually make your manufacturing process more sustainable.

In a world of ongoing change and new challenges around every corner, it is crucial for innovators to always stay one step ahead. What is society concerned with? How are investors and markets adopting to new circumstances? How can I prepare my business for the next big transformation? And how can I prepare for the next digital disruption?

With climate change and scarcity of resources knocking on our doors, there is ample motivation to take action. So let´s explore the issue of sustainability in manufacturing and why it matters for seminal businesses!

React to growing awareness

In recent years, a growing awareness on the environmental impact of our economy can be seen in both public and economic arenas. Especially in the manufacturing world, it becomes clear that due to massive decarbonization goals and limited natural resources, continuing to manufacture in an unsustainable way can only go on for so long. This could particularly affect traditional companies that do not convert to sustainable manufacturing processes, as they run the risk of falling out of favor with public opinion and by that lose the high degree of trust with customers they have built up over many years.

Be prepared for policy initiatives

In addition, Green New Deal initiatives in both Europe and North America will increase the responsibility on all businesses to adopt more sustainability in manufacturing. Supranational decarbonization guidelines are paired with concrete growth and innovation strategies, making businesses that adopt sustainable ways of producing more attractive towards investors and markets. At the same time, businesses unwilling to shift to more sustainable manufacturing processes are likely to be left behind. So be prepared for governmental restrictions and face them as a chance for innovative smart manufacturing processes.

Make your business future-proof

We have now learned that businesses simply cannot turn their heads when it comes to improving sustainability in manufacturing processes. It is a chance to react to the growing environmental awareness of clients and public, contribute to supranational decarbonization goals, and meet new demands of investors and markets. It is also a chance to gain a competitive edge when incorporating smart manufacturing solutions to reach these goals. Why? Let´s find out!

In their 2018 report into IoT manufacturing, the World Economic Forum not only found that the inter-connectedness of manufacturing processes and plants would add some $14 trillion of value to the global economy by 2030 – IoT solutions will also act as key drivers for sustainability in manufacturing as well over four-fifths of IoT deployments in industry are currently addressing, or have the potential to address sustainability goals as set out by the United Nations.

With these promising numbers in mind, let´s explore three steps to increase sustainability in manufacturing by using the power of IoT. As already stated, we are aware of the simplification of the concepts presented in the following. However, we would assume they may be the starting point into your personal sustainable manufacturing journey.

Step 1: Use your data wisely

First of all, you can start treating your data as one of the most valuable resources you have. The main benefit of using IoT and IIoT for sustainable manufacturing lays in collecting exactly this data in order to work with it. Use a thought out, vertically integrated IoT solution including software, automation, and sensors to easily connect, monitor, and analyze your entire manufacturing process. Now you can start to measure, compare, and analyze your current input in order to improve your output, making it more efficient - and greener. Digital Twins of your production and your products may help to run what/if scenarios and decide for the optimum process combining economic and environmental sustainability.

Step 2: Use your energy wisely

In 2018, 37% of global energy use could be traced back to the industrial sector. Looking at the enormous challenges in front of us, it becomes clear that energy efficiency in industrial manufacturing will play a major role when reaching ambitious sustainability goals. IoT-based solutions can help manufacturers optimize their entire energy system, transforming it to become both more efficient and sustainable. As already mentioned, one of the prime advantages of IoT manufacturing is the real-time collection and processing of data. By connecting, monitoring, and comparing this set of information, you can easily identify irregularities and patterns that make your manufacturing process waste or lose energy – and take suitable measures right away. For example, intelligent devices and sensors on machines can independently recognize when they are needed, helping to prevent over- or underuse, and by that, saving energy and actively reducing your environmental footprint.

Step 3: Use your other resources wisely

A thought out IoT manufacturing solution will help connect, share, and analyze all the data running your production process and by that, will help you use your given resources more wisely. By knowing exactly the right number of resources – your raw materials, commodities, and construction materials – you can start using what you have more mindfully. Cutting down on the use of fluids, especially oil and water, is just one sustainable manufacturing example that can be expanded to include many more. Let your IoT solution help you decide which materials or spare parts to use, how to reduce over- or underproduction, or how to reuse resources or waste. This not only means less consumption, it will also run hand-in-hand with longer lifespans for production equipment and tools and make your production line leaner and more productive. Sustainable usage of resources also spans the complete product lifecycle, starting with product design over product operations until decomposition of the product and the recycling of its parts in a circular economy.

There is no doubt that great challenges lay ahead of us. Complex tasks require bold and innovative solutions. This is where IoT steps in. We have already witnessed how the Internet of Things helps us embrace and shape change. Now, we can incorporate IoT manufacturing solutions, in order to transform the way we produce in many ways, making our manufacturing process smarter, more sustainable, and compatible with ambitious and necessary decarbonization goals.

Smart manufacturing solutions thereby deliver the concrete technology to do so, resulting in various sustainable manufacturing examples, like the mindful and economical use of valuable resources like material, energy, or water. Introducing IoT into our economy also allows us to make it future proof by reacting to new societal and political requirements while also securing economic growth.

Now it´s time to face the challenges ahead of us. Luckily, we have just the right toolset to do so. So let´s embrace digital transformation, unlocking the full potential of IoT and reach true sustainability in manufacturing.

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Let's face it – the world needs to overcome a big challenge with regards to energy: After all, the energy consumption of cities and industries is ever-growing. According to World Bank data from 2019, the proportion of the global population that resides outside of cities and towns has fallen by two-thirds compared to the start of the 1960s. At the same time, academic studies have noted that urban areas account for about three-quarters of global energy use, something that will undoubtedly cause a problem for ongoing sustainability efforts.

Even with more and more green energy sources coming online with every passing month, there is still an extensive challenge to lower carbon dioxide levels in the atmosphere and for the nations of the world to meet their Paris Accord climate change commitments. Many businesses have committed themselves to lowering their carbon footprints to meet regulations and cover new customer demands. Siemens, for instance, was actually the first DAX company to commit on carbon neutrality by 2030 and reduced emissions already by 54% since the start of the program. Consumers increasingly turn to companies that focus on sustainable energy generation and consumption and realize that a profitable future is only possible in a sustainable world. Either way, I already see a world in which decarbonization is considered to be of huge importance in the urban realm. However, many companies still lack a holistic approach to decarbonize their energy systems.

In this article, I will emphasize the relevance of decarbonization for the energy sector and the related energy-intensive industries as well as for sustainable urbanity. Then let me show why digital twins should have a part to play in any decarbonization roadmap. Finally, I will share six identified principal success factors on how to apply digital twins to decarbonize energy systems.

Sustainable energy use is at the heart of the fight against climate change, as energy generation is responsible for a huge amount of carbon dioxide (CO₂) and other greenhouse gases that get emitted into the atmosphere each year. Only for the purpose of illustration, according to the Energy Information Administration (EIA), electrical generation accounted for some 4.13 trillion kilowatt-hours (kWh) of energy in 2019 in the United States alone. The EIA estimates that some 1.72 billion metric tons of CO₂ was released into the atmosphere as a result of all energy sources. To put it simply, that's approximately 0.92 pounds of CO₂ emissions per kWh!

Numerous decarbonization trends, such as the switch to biofuels from fossil-derived ones, already try to tackle that problem today. Others include the increasing use of wind energy, both on and off-shore, as well as the development of hydrogen-based fuel cells, or the roll-out of carbon capture and storage (CCS) technologies. These truly are groundbreaking times for decarbonization!

Nevertheless, significant quantities of greenhouse gases are still being produced every day due to “business as usual” operation of energy systems in business, housing and smart life, not to mention the mobility and transportation sectors in cities. The good news is we can keep a cap on these emissions. With the fundamental optimization of the entire energy system to known decarbonization solutions, the world will come closer to the goal of meeting its climate agreements and lower cost pressures to bear on climate goals. This is especially the case with energy systems that offer huge potentials for CO₂ reductions. Energy systems are basically interconnected networks that deliver energy to end users – both industry and consumers – on an on-demand basis. Sometimes demand may be high and sometimes it may be low, that’s just natural. Any energy system must account for this and be run in an optimized way so that over-generation of electrical energy is minimized or even avoided. In this regard, digital twins represent a powerful ally in modern decarbonization technology today bringing together two major goals for businesses: sustainability and cost effectiveness.

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There are several technologies that already exist which contribute to the reduction of CO₂ – and other greenhouses gases – from being released into the earth's atmosphere. Among these technologies, digital twins can be a way to bring all technologies together and model energy flows and changes of parameters in real time. Simply put, a digital twin is a virtual version of something that usually resides in the physical realm and is used to mimic real systems or products in industrial environments. It was once used to help designers and engineers with new product concepts but, strengthened by the Internet of Things (IoT) and Artificial Intelligence (AI) systems, it has proven itself to be an even more valuable tool. This is because it can model a raft of interconnected systems through big data analytics.

In general, one can now model energy systems and infrastructures within smart districts and utilities – and even across entire national networks, if up-scaled – by creating digital twins of the as-installed or as-planned grids. The basic idea is that digital twins help with the decarbonization of energy systems by mapping all of its aspects – from electrical production to distribution losses and localized demand – in the digital realm. It is already proving itself to be particularly effective in energy management as well as district heating, electrically powered public transport infrastructure management, and sector coupling, to name just a few areas. Therefore, it is important to know which factors in the deployment of digital twins will lead to the most successful outcomes. I identified the top six success factors to successfully decarbonize energy systems using digital twins.

1 - Don’t start without a decarbonization strategy in place

To begin with, any successful decarbonization program must commence with clear goal-setting. The modeling used in the digital twin of the effective electrical system should offer a clear decarbonization roadmap with carefully thought-through criteria about the intended outcomes. First and foremost, the monetization of the current versus the targeted carbon footprint comes into play.  Also, current and future business models focusing on either private or public goods or services will get conceptually challenged based on their inherent carbon intensity. Subsequently, the question of the future energy mix along the entire value chain of production or servicing has to fully include all suppliers and business partners – and this can even raise new questions related to the own location strategy. Finally, sustainability reporting should not be underestimated, as it even exposes management to personal liability.

2 - Always consider the whole system!

Secondly, you always need to consider the entire energy system when starting your journey and how to best optimize it for favorable outcomes. It regularly includes electricity, but also heating & cooling and transportation for a thorough end-to-end analysis of the initial situation and therefore the entire technological framework conditions. To name only a few, each site or entity calls for a multidimensional consideration of e.g., technical lifetime, maximum capacity, minimum versus maximum operating and down time, investment and maintenance cost, and of course its energy demand and relative efficiency. In other words, this attention to detail and completeness is needed to unlock the full decarbonization potential.

3 - A technology-neutral approach is vital

Having a technology-neutral approach to the decarbonization of energy related systems in place means that you should have no preconceptions about which model or configuration will work when adopting digital twin technologies. You need to start the journey with an open mind. A technology-neutral approach allows to draw flexibly from a variety of existing ideas and possible technology solutions. In short, predetermined technological pathways tend to end up with sub-optimal outcomes. Never commit too early to a single technological approach when examining all of the conceivable approaches to decarbonization because your digital modeling and AI number-crunching may surprise you!

4 - Data is the new gold

Preparing your dataset before you build your digital model(s) is something I have learned first-hand to be crucial to the entire process. In short, this means gathering, cleaning and structuring the data, but it will also require that the plausibility of the required data in real-world settings is checked. If you put garbage data into your digital twin model, then expect low-quality outputs. All too often, we have experienced these challenges when analyzing for example meteorological data being crucial for renewable solutions next to data sets about electrical consumption in buildings or feed-in profiles from photovoltaic, solar thermal, concentrated solar, wind, and hydro power plants at locations worldwide. In general, some patience paired with professional experience is needed to find, prepare and make the best use out of your data. There can never be enough data preparation and checking!

5 - Take your time!

Since digital decarbonization is obviously such a highly multi-faceted approach, better avoid looking for quick wins. Preparation of the energy system model needs care because it will be more complex – often much more complex – than you had initially bargained for. For sure, it all depends on the size of the endeavour: it takes rather a few months than weeks when adding renewables into an existing fossil power generation system as this might require spatial decoupling of power generation and consumption to handle imminent grid constraints and finally, a flexible use of battery storage systems. It might also take some time to onboard the right experts especially the ones with field expertise – but believe me, it’s worth it. In general: Success doesn’t come overnight, after all!

6 - Know your technology well

Although the decarbonization of energy systems obviously requires know-how with electrical gerneration, supply, and distribution, the final factor I recommend prioritizing if you want to achieve successful decarbonization is expertise with digital (twin) technology itself. Put simply, you require a high-level understanding of data modeling as well as expertise in smart data technologies if you want digital twins to help decarbonize your energy system. If you don't possess these skills in-house, then prioritize outsourcing them before starting your decarbonization journey. Digital decarbonization always need these most experienced practitioners coming from multiple disciplines to optimize the specific decarbonization path.

When applying a strategical approach, the impact of digital decarbonization can be quite impressive in numbers: for instance, in one of our projects with a German city with about 200,000 residents, we modeled its energy demand and infrastructure and found that 70 percent fewer emissions by 2035 would be perfectly feasible. This project also demonstrated that a 25 percent reduction in heat demand could be achieved in the same period which is a fantastic result by any standards.

In general, the main advantages of utilizing digital twins in future decarbonization strategies will be felt in basically all public and private sectors. Given that buildings account for about 40 percent of all current carbon emissions, focusing on the digital decarbonization of energy systems will make a huge difference to the climate of tomorrow. While decarbonization resonates best with the utilities sector as a whole, high energy demanding sectors such as the chemical and similar process industries, deserve highest attention as well. Benefits like running cost-minimized energy systems designed for specific locales, such as district heating systems, both conventional and renewable local utilities production facilities, community energy storage systems, and even better managed electric vehicle charging station capacity, will offer concrete saving potentials in municipalities and smart city environments.

While reducing wasted electricity – and, therefore, saving significant expenditure on daily operating costs – businesses will be able to strengthen their brand image as truly sustainable commercial entities. In the end, I would argue that there is a significant win-win for businesses that want to remain competitive and to be seen as a trusted supplier. Digital twins are, of course, only a part of achieving a best possible decarbonized future but they will become an increasingly important one.

Are you looking for more ways to decarbonize your smart district? Find out here!