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In March 2022, Nemus, an environmental planning, development, and sustainability consultancy based in Lisbon, Portugal, released a series of non-fungible tokens (NFTs) through its Nemus Earth initiative with the goal of supporting reforestation in the Amazon rainforest. The company purchased at-risk land in rainforests, created collectible NFTs, and used proceeds from the NFT sales to support reforestation and economic and social development in the region. While some purchasers praised the company for the NFTs, other stakeholders, such as activists, government officials, and Indigenous groups, questioned the efficiency and legality of the project. By July 2022, Nemus faced a decision: should it continue with its mission, or should it leave it up to governments and non-governmental organizations to lead the way on climate action? Could NFTs be effective in protecting rainforests, or were there better alternative paths?

Two branches of sound-related machine learning are emerging: sound detection and analysis, and AI-powered sound creation. In industries such as security, health care, retail, and media production, these acoustic technologies have significant potential for business value creation. The author describes several applications of sound detection and analysis technologies powered by machine learning and suggests ways business leaders can capitalize on them.

Many organizations fail to make good decisions about their data monetization strategies because their leaders lack a shared understanding of how to discuss them. This confusion can impede efforts to derive more value from the company's data assets through improvements or innovation. This article shares a simple, practical matrix approach that can help remove friction from discussions about monetizing data and identify areas of business opportunity, now and in the future.

Once scaled, online platforms reconfigure value to remain competitive. Reconfiguration in online platforms may take a different form than in pipelines, as online platforms are intermediaries that generate network effects among the sides they connect. They also face stiff competition from other spheres due to lower barriers to entry. Why and how do online platforms reconfigure value? By examining 13 Indian online platforms that have achieved a certain level of success (such as tipped markets, investor confidence, or profitability), this article offers four strategies for reconfiguring online platforms: enhance interactions, enhance capabilities, offer new services, and nurture new transactions.

Recent years have brought major technological breakthroughs in artificial intelligence (AI), and firms are expected to invest nearly $98 B in 2023. However, many AI projects never leave the pilot phase, and many companies have difficulties extracting value from their AI initiatives. To explain this contradiction, this article reports on a study of 55 projects implementing AI in organizations. It shows that organizational challenges in implementing AI projects are a result of a paradoxical tension created by two different perspectives on data science work: craft and mechanical work. Executives, managers, and data scientists should actively manage this tension to enable and sustain value creation through AI.

A perennial challenge for executives in established firms is deciding how and when to respond to emerging technologies. This article demonstrates that the way emerging technologies play out in established industries differs according to how the business system is affected. Some have primarily a supply-side effect (on how a firm in the industry creates its product), while others have a primarily demand-side effect (on how users consume the product). Supply-side effects play out over relatively long periods of time in a predictable way, with incumbent firms executing similar strategies though at different speeds. Demand-side effects are faster-acting and more volatile, with incumbents often experimenting with a range of different business models as they seek a viable way forward in a changing market. By understanding these important differences between supply-side and demand-side effects and being able to anticipate the typical patterns of responses from incumbents, executives can make better choices in how and when to invest in emerging technologies.

Web3 technology is described as trustless in that interactions and transactions do not require trusted third parties and instead rely on smart contracts and the immutability of the decentralized blockchain. Thus, in contrast to earlier iterations of the web, Web3 users are asked to trust the technology itself rather than the human intermediaries. On its face, this shift to a trustless web calls into question the traditional conceptions of and requirements for trust. However, in this article, we caution against claims that advocate distrusting Web3 on the basis that, despite how quickly Web3 technology is advancing, the psychological processes through which people perceive and make sense of the social world remain fundamentally unchanged. Drawing on the psychology of trust and the evolution of web technologies and associated objects of trust, we argue that Web3 is not so trustless after all. We also highlight opportunities for brands to build trust in Web3 technology, including key considerations in leveraging opportunities and directions for further research. Overall, this article provides critical guidance to brand managers, policy advisors, and academics seeking to understand, build, and trust Web3 technology.

With many knowledge workers working remotely, digital collaboration tools are more important than ever, but how do they affect creativity and innovation? The authors studied the content of communications generated through the use of these tools at a manufacturing company and found that these technologies can support the formation of both loose, open networks and tightly controlled, closed groups with different implications for creative output.

Companies are increasingly embracing the idea of helping nontechnical employees those with deep business-area expertise learn to automate mundane, repetitive, time-consuming processes through minimal training in low-code and no-code technologies. This article describes the benefits and challenges that six companies AT&T, Dentsu, Johnson & Johnson, PwC, Voya Financial, and Wesco experienced in their forays into the citizen automation movement.

Two months after his January 1, 2021, appointment as chief executive officer (CEO) of NewCo, the code name for the soon-to-be spun off managed infrastructure services portion of International Business Machines Corporation (IBM), Martin Schroeter was faced with the daunting prospect of creating a distinct strategy and identity for a huge company in a very short time frame. Announced on October 8, 2020, by IBM's CEO, Arvind Krishna, NewCo was a high-stakes strategic move to "create value through focus" and "increased agility to focus on evolving customer needs and delivery excellence." Once spun off, NewCo would immediately become the world's leading managed infrastructure services provider, with US$19 billion in revenue, 90,000 employees, and 4,600 customers, including more than 75 per cent of the Fortune 100 across 115 countries. With the spinoff expected to be complete by the end of 2021, Schroeter had only a few months to craft a strategy, recruit a leadership team, define a new identity for the company, ensure a "strong strategic relationship" with IBM, and bring thousands of customers and tens of thousands of employees into NewCo. Success required many pieces to come together rapidly and seamlessly. Schroeter was unsure how best to address these challenges. All he knew was that failure was not an option and that time was short.

New research looked at the extent to which the employees of a fashion retailer followed the stocking recommendations of two algorithms: one whose workings were easy to understand and one that was indecipherable. Surprisingly, they accepted the guidance of the uninterpretable algorithm more often.

The increasing frequency of interactions we have with AI and automated technologies means it is vital to understand how those things make people feel about themselves. Why? Because how people feel about themselves affects a wide range of success factors, including sales, customer loyalty, word-of-mouth referrals, employee satisfaction, and employee performance. The authors have been studying people's reactions to automated technology for more than seven years. In this article they focus on psychological responses to AI and automated technologies that they've observed in service and business-process design, product design, and communication, and offer practical guidance to help leaders and managers figure out how best to use these new technologies to serve customers, support employees, and advance the interests of their organization.

Companies typically treat their innovation projects as a portfolio, aiming for a mix of projects that collectively meet their strategic objectives. The problem, say the authors, is that portfolio objectives have become standardized, and innovation projects are often only weakly related to a company's distinctive strategy. This article introduces a new tool to help leaders better align their innovation investments. The strategic innovation tool kit has two elements: a strategy summary framework and an innovation basket. Leaders start by clarifying a unit's strategy and determining what needs to change to achieve it. The change needs are translated into innovation goals, and leaders create their "innovation basket" by plotting each project against those goals. They can then cut projects that aren't aligned and create new ones that are. It's an iterative and creative process: Projects are adjusted to fit the strategy but can also shape it. Examples from the authors' research demonstrate how the process of creating an innovation basket gives managers fresh insight into what their innovation activities are really doing for their strategy.

Generative AI and other easy-to-use software tools can help employees with no coding background become adept programmers, or what the authors call citizen developers. By simply describing what they want in a prompt, citizen developers can collaborate with these tools to build entire applications-a process that until recently would have required advanced programming fluency. Information technology has historically involved builders (IT professionals) and users (all other employees), with users being relatively powerless operators of the technology. That way of working often means IT professionals struggle to meet demand in a timely fashion, and communication problems arise among technical experts, business leaders, and application users. Citizen development raises a critical question about the ultimate fate of IT organizations. How will they facilitate and safeguard the process without placing too many obstacles in its path? To reject its benefits is impractical, but to manage it carelessly may be worse. In this article the authors share a road map for successfully introducing citizen development to your employees.

In the realm of cybersecurity, an important concept for every leader to understand is that of Digital Crown Jewels ('DCJs'). These are your organization's most precious digital assets, consisting of the data you possess, process, and pass on that allows you to deliver on your strategy. This might include customer records, purchasing histories, employee records, finances and intellectual property information about proprietary products and services. DCJs also include an organization's data processing environment (DPE): how data flows through the organization and the processes by which the firm and its agents access and manipulate data. The challenge is this: the list of actors who pose a threat to the integrity of a firm's DPE is long and growing. The authors provide guidelines for protecting your DCJs in three categories: preventable risks, strategic risks and external risks. They end with a warning: act now, because the potential cost of failing to act is far too great.

Companies that have successfully automated their procurement negotiations are gaining a wide range of benefits, including cost savings, increased supply chain resilience, and access to an expanded pool of qualified suppliers. However, implementing a new procurement model and a different approach to relationships isn't easy. Six key practices are needed to address legitimate concerns and overcome resistance from business unit leaders, buyers, and suppliers.

While the promise of artificial intelligence (AI) is pervasive, many companies struggle with AI implementation challenges. This article presents results from a survey of 2,525 decision-makers with AI experience in China, Germany, India, the United Kingdom, and the United States-as well as interviews with 16 AI implementation experts-in order to understand the challenges companies face when implementing AI. The study covers technological, organizational, and cultural factors and identifies key challenges and solutions for AI implementation. This article develops a diagnostic framework to help executives navigate AI challenges as companies gain momentum, manage organization-wide complexities, and curate a network of partners, algorithms, and data sources to create value through AI.

Throughout history, the advantages and costs of technological innovations have been unevenly distributed between the powerful and the rest of society, assert economists Daron Acemoglu and Simon Johnson in their new book, Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. In a Q&A, they discuss what's wrong with today's approach to automation, why machine usefulness is more important than machine intelligence, and what techno-optimists and -pessimists both get wrong.

Big Tech companies such as Google, Microsoft, and Amazon have an AI advantage thanks to the vast amounts of data that they collect through their respective platforms. Using federated machine learning (FedML) technology, companies with access to relatively small data sets can join forces in collaborative artificial intelligence projects while keeping proprietary data private. FedML could be a game changer in bridging the digital divide between organizations with and without big data.

The digital data available online is currently measured in zettabytes. These vast repositories of big web data are increasingly viewed as a strategic resource comparable in value to land, gold, and oil. This big web data can be extracted and analyzed by organizations to gain a better understanding of their internal and external environment and improve organizational performance. Because of these opportunities, automated retrieval and organization of web data (i.e., web scraping) for research projects is becoming a common practice. This article outlines the data-related, technical, legal, and ethical issues related to web scraping. Awareness of these issues can help researchers save time and resources and, most importantly, mitigate the potential risk of ethical controversies or lawsuits related to the retrieval and use of big web data.