The world we live in today is digital, connected, and mobile. Digital technologies have invaded every aspect of our lives and the business world. Today, over 62% of the world's population have internet connection,

According to Peter Diamandis, cofounder and Executive Chairman of Singularity University, technology is now democratized. When something is digitized it begins to behave like an information technology. In the past, powerful technologies were mainly available to large organizations or governments. Now, technology has become more accessible and affordable to a broader range of people. This has also created a higher potential for entrepreneurs to disrupt industries and challenge the stronghold of large corporates. Globally, there are about 1.35 million tech start-ups trying to achieve exactly this.

Diamandis explains this disruption in terms of the 6D framework on how exponential technologies go from being deceptive, where few people see them coming, to disruptive, where they became the main way of doing things in the world, as shown in Table 8.1. (Diamandis and Kotler 2016).

Reflecting on this framework, we can take a lesson from the well-documented demise of Kodak. Once a market leader in the photography industry, Kodak was the first to invent the digital camera in 1975. However, the conservative leadership team decided to forgo it in favor of maintaining the traditional camera and film industry, which existed in a scarcity environment. Analog cameras have a significant dependency on the physical device, as well as film—both of which place a limitation on the number of pictures taken, related costs, and the need to have them developed by a specialized provider. The business model for this was heavily dependent on post-sales continuous investment, requiring consumers to buy and develop the film at a high margin. In 1996, Kodak had a $28 billion market capitalization with 95,000 employees. The company avoided risking their existing position, but did not recognize that their core business was shifting.

When digital cameras hit the market, consumers did not want to take pictures in the traditional way anymore. They were able to take an unlimited number of pictures, without having to be concerned about cost of film or developing pictures (see Figure 8.1). Unfortunately, Kodak went bankrupt in 2012 after a downward spiral. During that time, smartphones became increasingly popular, incorporating sophisticated digital cameras that were able to produce relatively high-quality images. Adoption of digital photography skyrocketed, and once digital photo-sharing apps hit the market, consumers were able not only to take as many images as they liked, whenever, wherever, they were also able to share them online with millions of people. Now the photography industry that had been limited by physical scarcity shifted completely into an exponential environment.

In the same way that technology became democratized and disrupted industries, people are also increasingly heading towards the convergence of the physical and the digital (see Figure 8.2). At first, we developed technology to help solve problems, which in turn created new problems, so we developed more technology to solve those problems as well. Human-operated machines supported our daily activities and work, for example, in agriculture and industry. As we progressed, we started to outsource our physical and mental capabilities, with devices such as the mobile phone taking over the need to remember phone numbers and addresses. Once we gained more access to technology capabilities, we moved beyond human external devices and became more open to converging with technology to enhance our capabilities. Consider solutions such as prosthetics, bioprinting, or even the exoskeleton. As we venture deeper into convergence, where machines are gaining the ability to learn and develop themselves, we are seeing new innovations in human–machine interfaces. For example, two such innovations are developments at Neuralink, a brain–computer interface venture founded to allow humans to keep up with the advancements of technology, and Myndplay, an interactive mind-controlled media player and platform that will eventually gain the ability to read our brainwaves to execute commands. Many researchers believe that we will reach a point in the future where technology intelligence will surpass our ability to control and understand it. Personally I believe mankind will remain at the forefront, simply due to our humanity—empathy, creativity, and consciousness.

In any case, with each step forward, we have relinquished a small portion of ourselves in exchange for predictable, preprogrammed outcomes. Our tools could begin to perform larger and larger tasks on our behalf, allowing us to devote our time and energy to other, more desirable tasks.

A strong understanding of emerging technologies becomes increasingly critical as we move further into the hybrid world of digital and physical. This offers many organizations the opportunity to upgrade their business processes in such a way that their potential customers can connect with them very easily, as well as better assess how technology could be applied in business. Although there are a wide range of emerging technologies that impact the business world, this chapter will explore five top disruptive technologies that I assess as most relevant for digital business transformation implementation at the moment.

Artificial Intelligence

The story of “Clever Hans” stands out in my mind every time a discussion on Artificial Intelligence (AI) comes up (and it does come up quite often in my line of work). I often find myself in discussions with people—experts, industry leaders, or even friends and family—on AI and its potential impact in our lives. Almost everyone has an opinion on the topic, sometimes very strong and polarizing opinions, perhaps due to the popularity of referencing AI in mainstream media. Since the 1920s, Hollywood movies have been using AI as a protagonist, and often even antagonist, solidifying a mass perception of the technology, its capabilities, and potential impact on mankind. Movies such as Her, Ex Machina, A.I. Artificial Intelligence, and more recently Mother/Android, tell stories of a powerful AI that has the potential to experience consciousness and overpower humans.

In the early 1900s, retired math teacher and horse trainer Wilhelm von Osten presented Europe with a fascinating discovery—a horse that was claimed to have performed arithmetic and other intellectual tasks (Craw 2021). Hans the horse would tap out answers with his hoof and reliably arrive at the correct answer to complex problems, including solving math problems, telling time, identifying days on the calendar, differentiating musical notes, and spelling out words. The discovery became the “viral” sensation of its time, as people gathered to see the intelligent animal perform these feats. However, there were also those who doubted the intelligence of Hans, and a commission was set up to assess if this was an elaborate hoax by von Osten. Although the Hans Commission conducted a thorough evaluation, they could not find evidence to support their suspicions. Eventually, what they discovered was far more intriguing—the horse provided correct responses by responding directly to involuntary cues in the body language of the human trainer, who was entirely unaware that he was providing such cues. This came to light when the commission discovered that Hans was not able to provide the correct response when the trainer was unsure of the answer.

Von Osten had attempted to reproduce human intelligence in a non-human entity, in this case the horse, working under the assumption that intelligence is uniquely embodied by humans, and that it could be created through training. It also excludes critical elements of intelligences that are developed through social, cultural, and experiential contributions. In my view, Hans the horse displayed emotional intelligence while reading the trainer’s subtle reactions, a critical element in the realm of intelligence that cannot be excluded. Although there have been a multitude of developments in the field of AI, there is still a rather narrow view of what constitutes intelligence and how it could potentially be reproduced.

Artificial intelligence can be defined as the automation of cognitive processes—the science and engineering of making intelligent machines and using computers to understand human intelligence (McCarthy 2004). It is a field that combines computer science with large, reliable datasets in order to facilitate problem-solving. Machine learning and deep learning are two sub-fields that are frequently mentioned in the context of AI. These disciplines are made up of AI algorithms that aim to develop expert systems that can make predictions or classifications based on the data they are fed.

It is important to understand the differences between AI (automation of cognitive processes); machine learning (an approach to achieve AI that teaches computers the ability to do tasks with data, without explicit programming); and deep learning (a specialized technique to implement machine learning). These terms are often used interchangeably in the business environment but carry distinct meanings and implications (see Figure 8.3).

• Artificial Intelligence    Artificial intelligence, also known as machine intelligence, is intelligence that can be understood by another intelligence, as opposed to natural intelligence, which is demonstrated by humans and animals. Designing intelligent devices and systems that can creatively address problems that are often treated as human prerogatives. As a result, artificial intelligence refers to the ability of a machine to mimic human behavior in some way.
• Machine Learning    Machine learning is a subset of AI that includes techniques that allow computers to recognize data and provide AI applications. In machine learning, a variety of algorithms (e.g., neural networks) are used to solve problems.
• Deep Learning    This is also known as deep neural learning or deep neural network. Deep learning is a subset of machine learning that makes use of neural networks to evaluate various factors in a manner akin to that of a human neural system. It has networks that are capable of learning from unstructured or unlabeled data without the need for external supervision.

Research and development in AI has existed since the early 1950s. Although John Von Neumann and Alan Turing did not coin the term “artificial intelligence,” they were the founding fathers of the technology that underpinned it, making the first transition from computers to nineteenth-century decimal logic. Since then, AI has developed in several waves (see Figure 8.4), as AI capabilities have increased over time.

It is only in the past decade that AI development has truly accelerated and its application exploded in the business world across industries. This is because we now have the right foundational elements in place to support this rapid acceleration—unparalleled processing power, unlimited storage capacity, global connectivity, low cost of technology production, as well as mass acceptance and adoption of emerging technology.

In more recent years, AI has made its way into the mainstream business landscape as a necessary component of the digital business transformation roadmap. However, there is still a great deal of misunderstanding in the business world about what AI is and how it contributes to the digital transformation of an organization. Often, while advising leadership teams on their organization's digital transformation strategy, I encounter strategic goals that are too vague to realize, for example “implement AI-based solutions to realize xx% revenue gains” or “implement AI to increase productivity and efficiency.” These broad, high-level strategic objectives reveal a lack of understanding of the capabilities of AI-based solutions and how they can truly add value to businesses (Lardi 2021).

Artificial intelligence solutions assist organizations in a variety of ways, including the automation of redundant activities, the digitization of processes, and the fast analytics of data, particularly large datasets. Organizations can also combine different datasets to gain rapid insights and knowledge that can be used to help them make more informed business decisions. Furthermore, AI-based solutions provide scalability in a traditional business environment that is otherwise relatively inflexible. For example, increasing the number of digital access points, such as chatbots and virtual assistants, to provide more hyper-personalized customer interactions and support.

Artificial intelligence is a critical emerging technology that could help organizations reimagine consumer experiences and business processes, as well as unlock revenue growth and cost savings at the expense of their competitors. However, AI-based solutions should not be implemented as part of the transformation roadmap unless there is an appropriate and specific problem that can be addressed. This means initiatives based on their value and viability by ensuring there is a clear, concise description of the problem and the desired outcomes before starting. In addition, the data, both for training AI systems and real application, must be viable.

Data is at the heart of AI systems, and it has become a prerequisite for the deployment of AI solutions, particularly for any type of data analytics application. Organizations need to determine whether the data is balanced (free of bias), exhaustive (captures all relevant variables), diverse (captures rare situations), and of sufficient volume, among other things. I usually advise companies to develop an effective data strategy at the outset of AI implementation, which includes the definition of data requirements; gathering or acquisition of high quality, contextual data; as well as storage, management, and security requirements.

In order to maximize AI-based solution applications in the organization, it is important to build a deep understanding of AI, as well as a culture of widespread acceptance of experimentation and uncertainty (Moioli 2022). The foundation of AI implementation is rapid learning, which means trying new things, learning, and adapting to create a “virtuous cycle of AI”—learning from data, experiences, successes, and failures. People are the foundation of AI-driven culture in the context of digital business transformation, which requires individuals with varying competencies in research, engineering, production, and strategy. You need to create a culture where diverse ideas are shared and adaptable problem solving is prioritized, in order to build AI-based solutions that deliver on the required business outcomes. Strong talent in AI is in short supply, and effective management of both internal and external resources will be required.

Leadership teams must ensure that the appropriate people and skills are available to define, implement, and maintain the AI systems that are being implemented. The roles and capabilities that will be required will be defined in accordance with the solution strategy that you wish to implement. If your organization wishes to implement an AI system for internal data analysis, it will be necessary to hire personnel with the necessary skills to ensure data privacy and confidentiality. As an alternative, if your company decides to develop Application Programming Interface (API) links for the necessary AI solutions, you may only require people with technical knowledge of APIs and connectivity to existing systems, which may be either internal or external resources. As an additional benefit to using external platforms, AI skills and capabilities can be acquired much more quickly than they would otherwise be.

Currently, AI solutions are primarily focused on business process problems and range from human augmentation to process improvement to planning and forecasting, allowing for superior decision-making and results. However, a key issue related to AI applications is bias, an anomaly in the output of machine learning algorithms, due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data (Dilmegani 2022). This occurs due to cognitive biases that seep into the algorithms via designers unknowingly introducing them, as well as incomplete or nondiverse datasets. In developing AI-based solutions during the digital business transformation process, organizations can identify potential biases through human-related processes or technical solutions. However, a far more effective and critical approach would be to ensure diversity in the teams developing AI-based solutions. People, particularly diverse teams of people, are usually the first to notice bias issues; maintaining a diverse AI team can help you mitigate unwanted AI biases.

The worldwide revenues for the AI market, including software, hardware, and services, are growing fast and expected to break the $500 billion mark in 2023 (IDC 2022). Artificial intelligence should be viewed as a component of your organization's digital transformation journey, and it should only be implemented when it makes sense for the business goals that you are attempting to achieve.

Key Takeaways for the Human Side of AI:

• Build a culture of AI in the organization, ensuring key stakeholders and employees understand the value of AI application in business.
• Ensure underlying data strategy is developed in preparation for implementation of the AI-based solutions.
• Continuously communicate the role of AI in the organization's digital business transformation strategy and roadmap.
• Build internal capabilities and skills in the application of AI for business.
• Ensure a diverse group of people and skills are involved in the AI solution development.

Blockchain and Decentralized Technologies

I started to deep dive into the concept of blockchain in 2014, when clients began asking questions about its application in business. The hype around blockchain and cryptocurrencies was increasing, and companies, particularly in financial services, were becoming ever more curious about its impact. I started to deep dive into the topic and discovered a significant potential to disrupt businesses and industries.

Distributed ledger technology (DLT), more popularly known as blockchain, is a digital ledger system that stores information electronically in digital format. There is a slight difference between DLT and blockchain, in that blockchain is a type of DLT (in the same way not all sticky notes are “Post-it,” not all DLTs are blockchain). However, blockchain has become the successful namesake that has overtaken the category of technology that it belongs to. Blockchain became popular for its critical role as the ledger system behind cryptocurrency systems, namely Bitcoin. However, the potential value and application of this technology has far surpassed its ties to cryptocurrencies. In the case of blockchain technology, the innovation is that it guarantees the fidelity and security of a record of data while also generating trust without the need for a third party to be trusted.

Historically, ledgers have existed since the beginning of modern times. Humanity has built a global economy based on these traditional ledger systems. We have been using ledgers to record the exchange of money or goods, as a way to secure trust and validity of these transactions. And TRUST is the fundamental currency of commerce—without it, we cannot continue to do business. Over time, as distances grew, environments and transactions became more complex, and we started to rely on multiple ledgers and eventually intermediaries and third-party validation to secure trust in the transaction, for example, the financial institutions, banks, notaries, and legal firms that we have today. These systems have several fundamental issues, which became apparent over time:

• Lag time    We can instantaneously send an email to another person anywhere in the world, but it takes two to three days to transfer funds.
• High costs    Our dependency on intermediaries has given them the ability to charge high fees for these transactions and exchanges.
• Inefficient processes    Many of these interactions contain frictions that slow the process down, for example, physical documents to prove credibility, or physical signature.
• Accessibility    There are over 1.7 billion people globally without access to traditional financial services, for example, people without proper identification documentation, from war-torn regions, from remote locations without access to physical banks, or below the poverty line.
• Corruption    The more we started to depend on intermediaries to help us secure trust, interact with one another, and develop economically, the more powerful these central groups became, resulting in the risk of corruption. “Power corrupts, absolute power corrupts absolutely.”

In 2008, someone decided to address these issues by developing a new financial ecosystem, one that would be fairer for all and more inclusive. Known only as Satoshi Nakamoto, the true identity of this person (or group) is unclear. This proposed new design for the global economic system did not rely on intermediaries to secure trust; instead it designed a ledger that was digital, open, and shared. By completely eliminating the middleman in transactions, the economic system saves time, reduces costs and risks, and increases peer-to-peer trust across a business network. This marked the launch of distributed ledger technology or blockchain.

Blockchain is basically a decentralized network of computers that records and stores data as a chronological series of events on a transparent and immutable ledger system. It can store anything of value, where the data stored acts as a single source of truth to the network. A key difference between a traditional database and a blockchain is in the way the data is organized. A blockchain is a collection of information that is organized into groups of information known as blocks, with each block holding a set of information. Blocks have specific storage capacities, and when a block is completely filled, it is closed and linked to the block that was previously filled, resulting in a chain of data known as the blockchain. All new information that occurs after that newly added block is compiled into a newly formed block, which will then be added to the chain once it has been completely completed.

By design, this structure creates an irreversible time line of data. When a block is filled, it is set in stone and becomes a permanent part of the time line being constructed. When a new block is added to the chain, it is given an exact time stamp to identify it as having been added to the chain. Additionally, when a block is created, the records are time-stamped and each block is given a “hash”—a unique mathematically generated identifier made up of numbers and letters. If the data is changed in any way, the hash will also be generated again, creating an auditable trail of records (see Figure 8.5). In addition, smart contracts, digital programmable contracts stored inside a blockchain, can be used to automate the execution of an agreement so that all participants can be immediately certain of the outcome, without any intermediary's involvement or time loss.

The blockchain is quite different from traditional databases that have been used for decades. Imagine that a traditional database is like a whiteboard located in a meeting room of an office. Anyone with access to that meeting room (i.e., admin users of the database) will be able to write on the whiteboard, modify, or erase its content. The blockchain can be likened to a book. Data stored is like the printed pages of the book. Once printed, it cannot be changed without damaging the book in some way. The pages are numbered and bound together, information is stored in a chronological order with page numbering. We cannot change the content or page order once the book has been bound and published. Just as a published book cannot be altered, a single node in the blockchain network cannot alter the information held within it.

Since its development, blockchain application has exploded across finance, business, government, and other industries. For example, peer-to-peer payment services such as Venmo are convenient, but have their limitations. Some services restrict transactions based on the location of the customer, while others charge a fee for the use of their services. Many of them are also vulnerable to hacking, which is not appealing to customers who are putting their personal financial information on the line in this manner. Blockchain technology has the potential to remove these roadblocks. In health care, there are many opportunities for blockchain-based solutions as well. For example, storing medical records on a shared blockchain in a secure way, allowing for it to be accessed by numerous individuals without undue privacy concerns or specifically identifying any particular patient.

The immutable nature of blockchain makes it an appropriate solution for provenance and real-time tracking of goods as they move and change hands throughout the supply chain. This became a personal passion for me, particularly the blockchain application in agriculture. In 2017, I was engaged by the Malaysian Palm Oil Council (MPOC) to conduct a feasibility study on the application of blockchain in the palm oil industry. The emergence of blockchain technology has had a transformative impact on the palm oil industry by creating an end-to-end traceable and verifiable supply chain. Over the next two years, I worked with the agency to develop a proof of concept, and to design and develop a pilot blockchain-based solution implementation (see Figure 8.6).

Establishing and maintaining the traceability of palm oil across the value chain was complex because of a multitude of factors—complex value chains with multiple middlemen, regulatory challenges, and lack of consumer awareness, among others. Without a transparent and robust system of traceability, it is almost impossible to establish if the palm oil was produced sustainably (following the “NDPE”—No Deforestation, no Peatlands clearing and no labor Exploitation practices). In the Malaysian palm oil industry, blockchain alleviated any doubts by creating greater transparency and auditability across the entire supply chain. Instead of having to trust the elements of a certification process where doubt may be cast due to manual processes or human error, blockchain technology offered an infallible validation system that secured trust.

During the design and development of these solutions, I came to truly appreciate the impact of the people side of technology implementation. Even at a national level, the palm oil industry includes a range of digital maturity—from plantations that utilized digital tracking, drone technology, and modern harvest management, to smallholder plantations with zero connectivity (without 2G or 3G). Processes, from harvest to plantation management, as well as sustainability certification, included manual and human-dependent activities, and were subject to related weaknesses. The design principles of the blockchain-based traceability solution needed to take into account the poor connectivity, low-tech environment and users, as well as ensuring interoperability with various technologies utilized by each plantation. As an outcome, an interoperable solution was developed with a blockchain back end, and web-based and mobile app front end that was easy to use and built to fit into the existing daily processes of the plantations (refer to Figure 8.7).

In digital business, blockchain holds the promise of transactional transparency for businesses, as well as the ability to create secure, real-time communication networks with partners around the world to support everything from supply chains and payment networks to secure mobile communications and health-care data sharing. More than 70% of all companies across industries consider blockchain to be part of their digital transformation strategy now or in the near future. The global spending on blockchain solutions will continue to grow in the coming years, projected to reach almost $19 billion by 2024 (IDC 2022).

Key Takeaways for the Human Side of Blockchain:

• Blockchain is not a silver bullet solution for all industry challenges. A good application of blockchain technology must solve real problems for the entire business or industry. As with any technology deployment, the unique properties of blockchain needs to be assessed against the business requirements for viability and strategic fit.
• Before development, ensure the blockchain technology is interoperable with the underlying technology infrastructure, as well as existing platforms and systems in the business ecosystem.
• The blockchain solution design should be intuitive and user-friendly, and specifically ensure that it fits with the existing human activities and daily operations in the business. This is particularly important with implementation in traditional industries such as agriculture, shipping, and logistics.

Extended Reality and the Metaverse

In 2007, I discovered a new world in online multimedia platforms. Second Life, a platform that allows people to create an avatar for themselves and have a second life in an online virtual world, was my favorite at the time. At the time, virtual worlds were still in their nascent stages and I was part of an internal corporate strategy team and presented the prospects of virtual worlds to the leadership team. There was more skepticism than curiosity, understandably for the time, when the hype-and-backlash cycle of emerging technologies cast a shadow of doubt over the potential business value of online and virtual platforms. In addition, the foundation technology at that time—computing power, internet bandwidth and speed, and headsets—was still rudimentary and did not offer the “wow” experience of the extended reality environments of today.

Extended reality (XR) really refers to three distinct sets of technologies—Augmented reality, virtual reality, and mixed reality. Augmented reality (AR) is the addition of digital elements to a live view, which is frequently accomplished through the use of a mobile device or smartphone camera. Snapchat lenses and the video game Pokemon Go are two examples of AR experiences. Virtual reality (VR), on the other hand, refers to a fully immersive experience in which the physical world is completely shut out. Users can be transported into a variety of real-world and imagined environments by using VR headsets or devices, such as the HTC Vive, Oculus 2, or the Microsoft HoloLens. In addition to AR and VR, there are other experiences that are growing in popularity. Mixed reality (MR) experiences combine real-world and digital objects, and have recently begun to gain traction. Extended reality incorporates a range of these technologies to enhance the senses, providing additional information about the actual world or creating completely unreal, simulated worlds to experience. Figure 8.8 describes the three forms of XR, which differ mainly in terms of the levels of immersion and interaction with the physical world.

Unlike VR, which is entirely digital, AR derives its strength from the combination of the virtual and real worlds. When used in conjunction with the physical world, AR aids in bringing things to life in new and exciting ways by superimposing digital information, objects, and tools over people's field of vision. This makes it appealing not only for gaming, but also for employee training and collaboration, which can be transformed into an extremely robust and immersive experience thanks to the use of AR. A more efficient and cost-effective method of facilitating teamwork and instruction than meeting in person can be found in AR for business applications. This has been particularly apparent during the global pandemic, which has accelerated AR's growth by necessitating remote working.

The use of AR can help businesses save time while also decreasing their environmental impact, which is a significant benefit to employees and customers who are becoming increasingly concerned about sustainability. The wow factor captures more of your attention, and there is a greater sense of immersion. Providing complex information in a way that is easy for the learner to understand is much easier than the reverse. It improves the effectiveness of training in terms of engagement, retention, and overall enjoyment. The genius of AR lies in the fact that it allows employees to “learn by doing” in these and countless other organizations. While AR can connect workers to real-time support from remote colleagues, it can also allow them to work alone without feeling like they are actually alone. Through the use of their webcam, remote trainers can assist learners in the use of technical equipment, while the trainer uses AR to overlay specific information on the learner's screen.

In the near future, I would envision a world where handheld and other devices will fully integrate AR, offering an impactful medium for forward-thinking companies interested in collaboration and training solutions that affect their bottom line. Mercedes Benz uses the Microsoft HoloLens to give technicians specialized training, reports CNET. Doing so improves collaboration and employee performance while decreasing turnaround time. Aerospace giant Boeing deployed AR to help airplane technicians reduce errors, while the US Marine Corps uses it to train for hostile, real-life scenarios without placing marines in actual danger. The National Aeronautics and Space Administration (NASA) teaches astronauts to repair the International Space Station (ISS) using the Microsoft HoloLens, perhaps the most ubiquitous AR device.

Virtual reality technology is also not new, although global adoption has increased significantly in recent years. This is due to more affordable VR devices, as well as increased application across various industries beyond video and online games. Health care provides great use cases for VR applications, for example, in treating phobias and lowering anxiety, as well as more advanced applications for medical practitioner training and even remote surgeries. These solutions go a long way to making high-quality medical treatments available at lower cost and in remote areas.

Virtual reality is a rapidly growing industry that currently accounts for a significant portion of all content produced around the world. Shipments of VR headsets have also increased significantly over the last two years. Virtual reality provides enterprise users and consumers with immersive experiences for collaboration and remote productivity. By 2024, VR revenues will have surpassed $12 billion on a global scale. Despite the fact that VR revenues were expected to decline slightly in 2020 as a result of Covid-19, demand has increased and is expected to continue to grow steadily. In addition, those engaging in social VR experiences spend over three hours a day in virtual reality (Social VR Lifestyle 2021).

The biggest hype word of 2021 was the term “metaverse,” which ignited a lively global debate about what it represents, whether it is already here, and who will own it in the future. As XR devices, software, and platforms improve in terms of features, user interface, accessibility, and usability, the metaverse environments will keep developing beyond the hype and generate significant business value. The term metaverse does not really refer to one specific type of technology, but rather a broader shift in terms of how consumers interact with technologies. Extended reality devices, as well as other devices such as PCs, game consoles, and phones, offer these interaction capabilities to virtual worlds that are expanding rapidly, and integrating with one another. The rapid acceleration of the metaverse was also triggered by a functioning digital economy, enabled by blockchain technology and digital currencies.

However, despite the fact that it may appear to be a world of science fiction, in the metaverse it will be possible to carry out activities that include everything from attending virtual concerts to traveling and shopping to going to the movies and even trying on clothes. It has the potential to alter the way we work, as video calls will move away from the 2D of a screen and into a virtual call-in format. There are a range of big brands joining the metaverse, including Gucci, Sotheby's, Disney, Nike, and Zara, among others, to explore new ways of engaging with consumers. For example, Gucci launched two initiatives: Garden Archetypes, an immersive multimedia experience, and Gucci Garden, a unique and interactive virtual exhibition. Visitors’ avatars become mannequins as they enter the “gardens,” and they wander through the different rooms to absorb elements of the exhibition, retaining fragments of the spaces and ending a journey as unique creations.

As part of the digital business transformation roadmap, XR solutions offer the potential to increase digital capabilities of traditional processes and tasks by increasing efficiency, and reducing costs and time. For example, during the coronavirus pandemic, companies increased the use of augmented reality devices and applications to support front-line workers, reducing the need for physical contact and the number of people required. Extended reality solutions have the potential to change the way companies operate, for example (Vigros et al. 2021):

• Manufacturing processes    They increase efficiency, reduce mistakes and need for on-site personnel in assembly and maintenance through real-time visualization of instructions, particularly critical in high-risk industries.
• Product development    They significantly reduce costs and accelerate turnaround time for creative designing and prototyping process, as well as facilitating more intuitive and simpler interaction between the manufacturer and clients.
• Collaborative working    Virtual meetings allow for deeper interaction and immersive collaboration between participants. Also, remote guidance and supervision allows for real-time support to on-site staff, reducing the need for travel, saving money and time, and reducing carbon impact.
• Customer engagement    They engage and interact with customers on a deeper level through virtual try-ons, product visualization, virtual tours, post-sale training, and support.

According to a report by Bloomberg Intelligence, this digital environment has such excellent economic prospects that it is expected to reach $800 billion by the middle of this decade, and by 2030 that figure is expected to multiply to $2.5 trillion (Bloomberg 2021).

Key Takeaways for the Human Side of Extended Reality and the Metaverse:

• Sophisticated AR tools do require a high degree of integration to perform these specific functions. Extended reality tools perform best when connected with the broader upstream and downstream processes across the entire manufacturing value chain.
• Start with small scope applications to familiarize the employee base with these new ways of learning, interacting, and collaborating.
• Utilize education potential of XR capabilities to enable faster learning and education, particularly in upskilling workforce digital capabilities.
• Follow the developments of consumers on XR and metaverse environments to understand how to add value as a business.
• Engage with experts to understand the impact and capabilities of XR and metaverse environments, to properly leverage for business value.

3D Printing and Additive Manufacturing

In the simplest terms, 3D printing is the process of making three-dimensional solid objects from a digital file. The production of 3D printed objects is accomplished through the use of additive processes, where an object is created by adding successive layers of material on top of one another until the object is fully developed. With the use of 3D printing technology, complex shapes can be created with less material than would be required with traditional manufacturing methods. The concept of 3D printing can be traced back to the 1980s, and has since been developing as researchers and organizations have explored the technology for rapid prototyping and manufacturing.

In recent years, 3D printing methods and technologies, as well as materials that printers use have evolved significantly. The technology has improved, becoming more accessible and cost-effective for application across various industries, for example:

• Prototyping and manufacturing    The concept of “agile tooling” was created by 3D printing where modular processes enable quick prototyping and responses to tooling and fixture needs. There are numerous companies that offer mass customization services enabling consumers to directly customize objects through a simple web-based interface and order the resulting 3D printed objects.
• Bioprinting    This technology has created countless opportunities in the medical field, including versatile production of tissue-like structures for prosthetics, orthopedic implants, and more recently even printing artificial organs, helping to solve organ failure issues in patients faster. 3D printed tissues have also been developed for pharmaceutical testing as a cost-effective and ethical way of identifying side-effects and validating safe dosages.
• Construction    3D printing technology is an increasingly popular method to produce architectural scale models for faster turnaround and reduced complexity. Beyond this, 3D printer houses and buildings are also being explored as cheaper and quicker alternatives to traditional construction, with completed constructions in Germany, Netherlands, the US, Africa, and other countries.
• Digital fashion    3D fashion and virtual models have improved design workflows, setting new standards for the fashion industry. As the demand for more sustainable production increases, 3D digital fashion development eliminates unnecessary physical sampling and wasted materials, shortening production and lead times. Additionally, the digital and 3D printed fashion industry is set to skyrocket due to the increasing popularity of XR and metaverse environments.
• Food    3D printed food is still in its infancy and has a long way to go before seeing a broader adoption from professionals and consumers. However, it is a growing industry, with Fused Deposition Modeling (FDM) printing that requires paste-like inputs extending possibilities for doughs, mashes, cheeses, frostings, and even raw meats.

Additive manufacturing is 3D printing at an industrial scale that brings significant flexibility and efficiency to design, production, and manufacturing operations. Apart from quicker turnaround and cheaper prototyping processes, additive manufacturing enables companies to produce lighter parts and provides increased opportunities for mass customization. Additive manufacturing is opening up new possibilities in business models, particularly in the areas of production and customization, as well as in a variety of other areas that have an impact on people's daily activities. 3D printing and additive manufacturing capabilities are now accessible to virtually anyone, as an increasing number of “fabshops” (fabrication shops) open up worldwide. From designers, entrepreneurs, and small businesses to large organizations, rapid prototyping, cheaper production, and customized manufacturing are now easily available. In combination with secure transactions offered by blockchain technology for exchange of proprietary designs and instantaneous payments, this becomes a powerful environment for global supply chain management.

Key Takeaways for the Human Side of 3D Printing and Additive Manufacturing:

• Close engagement with people involved in the processes that would utilize 3D printing and where additive manufacturing would be critical in order to truly understand how it could be applied.
• It is also essential to map the interaction of stakeholders to address the challenges of 3D printing adoption in practice.
• Beyond application in a specific part of the business, explore the potential for new or alternative business models enabled by transformed supply chain management.

Robotics and Automation

Debates on the impact of robotics and automation on the workplace have been perpetual since the Industrial Revolution. Fears regarding the rise of automation and intelligent machines replacing people in the workplace has only increased in recent years as companies adopt technology to improve productivity and efficiency while reducing costs. The use of industrial robots in factories around the world is accelerating at a high rate, with 126 robots per 10,000 employees (Edwards 2022). This is a new high average of global robot density in manufacturing industries, nearly doubling the number from five years ago (66 units in 2015).

Frequently, the impact of technology is seen as either a positive or a negative impact for the human workforce, particularly in the space of robotics and automation (Nunes 2021). The singular view that workers are either displaced by technology solutions, or technology allows for the creation (or reinstatement) of work, is limiting and falls short of the exponential potential of the digital economy. Alternatively, one should consider redesigning organization structures and roles, reskilling employees, and reviewing incentives to work in collaboration with robotics and automation solutions. By shifting people towards focusing on high-value and complex tasks, organizations have the opportunity to increase employee satisfaction and define a clearer, more meaningful purpose-driven environment.

Robotics and automation are often referenced together, but there is a clear distinction between the terms:

• Automation    This involves the use of self-operating physical machines, computer software, or other technologies to perform tasks that would normally be performed by people. This process is designed to automatically perform a predetermined sequence of actions or operations, or to respond to encoded instructions without the need for human intervention.
• Robotics    This is the study of the design, development, and application of robots to perform tasks. These are physical robots that perform actions in place of (or in imitation of) humans.

From an industrial perspective, robotics and automation are applied to automate physical processes, utilizing physical robots and control systems. A good example of this is the vehicle assembly line in a factory that utilizes automation with robotics. In recent years, the city of Dongguan, China, made new headlines for its “unmanned” factories. In 2019, the city announced that it had replaced 280,000 manufacturing workers with 91,000 robots, resulting in savings of $1 billion. During the global pandemic, as factory doors reopened and manufacturing capacity was restored, the demands for industrial robots further increased, with sales of industrial robots increasing by 19% from the previous year, reaching $1.2 billion in 2020. In addition, a growing number of service robots for low-touch environments, such as contactless service robots, disinfection robots, and temperature-taking robots, are becoming increasingly popular because they provide a safer environment for people.

More popularly applied across various industries are software automation solutions such as robotics process automation (RPA) and intelligent automation. The application of RPA solutions as a transformational tool is becoming increasingly simple, as service providers and vendors in this field are quickly growing. In addition, there are many low or no code platforms that offer user programmable solutions to accomplish automation of business tasks. The RPA software bots can copy routine and repetitive actions performed by employees, allowing them to focus on higher value tasks. The RPA approach is effective for automating rule-based tasks that use structured digital data, and offers cost and effort savings over time. The successful application of RPA depends on having a clear scope for it and being able to identify processes in the business value chain that would truly benefit. Last year, I was engaged with a financial service company in the process of implementing the digital business transformation strategy that we had defined. The transformation roadmap included a redesign of the business operating model and process landscape. The internal IT team had decided to engage an RPA vendor to implement automation of the operations processes, which would have resulted in a particularly high-cost solution with limited benefits. Alternatively, we initiated an operations centralization initiative, where processes were standardized, and optimization and specific areas for automation identified. This resulted in an overall improvement in turnaround time and cost savings, as well as operational services that could be leveraged by the overall business. Following this, the RPA solution could be applied to the future state optimized process landscape to enable the true promise of robotic automation.

The global RPA market value is expected to exceed $2.7 billion by 2023, propelled by its ability to provide higher efficiency, improved customer experience, and greater ease in managing business operations. Additionally, the Covid-19 pandemic positively impacted the RPA market as the virtual workforce and consumer demands in financial service, health care, and the public sector demanded efficient digital services.

Intelligent automation (IA), often referred to as hyper-automation, creates end-to-end business processes that think, learn, and adapt on their own. Intelligent automation applies advanced technologies such as artificial intelligence (AI), analytics, optical character recognition (OCR), intelligent character recognition (ICR), and process mining to achieve accelerated business outcomes with minimal human intervention. Ultimately, customer experience improvements and employee satisfaction are achieved through increased process speed and resilience, reduced costs, enhanced compliance and quality, as well as optimized decision-making (Bornet, Barkin, and Wirtz 2021). Solutions provided by IA effectively create a software-based digital workforce that, by collaborating with the human workforce, creates synergies and increased efficiency. The true value of IA can be seen in areas of the business where it augments human capabilities, for example, in rapidly processing large, hard-to-manage volumes of structured and unstructured data to derive critical insights for business operations.

Intelligent automation is a term that is often used in combination with other emerging technology concepts such as AI technologies. However, it is important to have clarity on how the concepts interact and overlap, in order to best apply to solutions (see Figure 8.9).

Gartner has listed hyper-automation as a top strategic trend for 2022 (Gartner 2022). As part of the digital business transformation roadmap, hyper-automation and RPA offer powerful tools that deliver the efficiency, speed, and agility required to support the needs of digital business environment.

Key Takeaways for the Human Side of Robotics and Automation:

• Find the more appropriate or best use cases for robotics and automation that will provide a substantial return on investment within a reasonable time frame, typically 18 months or less.
• Ensure that there is a dedicated internal team that has a deep understanding for the business environment and capabilities of robotics automation solutions.
• Executive or leadership team support and sponsorship in delivering robotics and automation as part of the digital transformation roadmap is critical.
• Working in close collaboration with trained experts in this field (either external or internal) will be critical to ensure the solutions are implemented for sustainable success.
• In order to alleviate fears or misconceptions, develop clear communication for the internal workforce to explain the advantages of automation, its impact and role in the transformation journey, as well as any possible impact on jobs and skill requirements.

Convergence of Emerging Technologies

Although the development of each technology drives transformation, I believe the convergence and combination of these technologies have far greater potential for change across industries and businesses. Combining emerging technologies creates new solutions that not only leverage the advantages of each technology, but also accelerate solutions to achieve new levels of efficiency, performance, human ability, societal outcomes, and business results.

For example, the capabilities of blockchain technology, AI, and the Internet of Things (IoT) complement each other to create new solutions, products, and services. Connected devices and sensors enable real-time tracking and monitoring, as well as in-depth data gathering for a range of activities. However, enabling continuous communication, monitoring, and transaction between millions of heterogeneous devices creates challenges in data security and effectively analyzing large quantities of data. Blockchain technology offers a secure, decentralized environment to efficiently share data between peer-to-peer connected devices and sensors. This creates a more scalable, Cloud-based environment, providing secure audit trails of information coming from a sensor and making it easier to monitor connected devices. Blockchain technology also supports interoperability of various devices by providing a trusted, common communications layer, and smart contracts enable autonomous machine-to-machine transactions.

Similarly, AI-based systems do not have an accepted standard for data sharing. Today, data is produced at an exponential rate, by people, channels, platforms, and devices. In addition to the large amounts of data that could be analyzed, there still exist silos that make it hard to combine data points to derive insights. Additionally, AI models used for decision-making provide little transparency, where large datasets are pumped into systems and results are produced in the form of analysis, reports, or recommendations. This creates the issue of trust and questions the credibility of the system, particularly considering the existing challenges with datasets that may be incomplete, poorly sourced, inaccurate, or contain biases.

Combining these elements with the capabilities of blockchain technology offers clear data provenance and audit trails, transparency, and traceability of data, as well as interoperability across various platforms, and results in more trustworthy AI-based systems for analysis and decision-making. The combination of blockchain, IoT, and AI could be applied across various industries, such as in health care for patient data management, real-time care, and monitoring, as well as traceability of medicine and vaccine supply chains.