Thingtesting, a brand discovery and testing digital community devoted to uncovering and exploring direct-to-consumer brands, had just received seed funding and was contemplating a second year of growth. The new year brought many challenges, as founder Jenny Gyllander had to decide how to scale her fledgling company. As the company matured, she had to decide what role she would play going forward: should she continue to operate as head curator and content developer of her own reviews or open up her platform to invite and crowdsource the reviews of her followers. Her value proposition positioned her at the nexus of several different audiences: millennial consumers eager to try new brands, venture capitalists eager to invest in them, and the companies and brands themselves, who were interested in seeing their new products succeed. What was the best path to monetizing the business? As Gyllander navigates the emerging landscape of influencer marketing, she needs to decide how to transform her online audience into a co-creating community and how to best wield her own influence to create value for her followers and other constituencies in a way that could fund a profitable business model.
In July 2019, Green Trend Holdings Co. Ltd. (Green Trend) faced a difficult situation with regard to flaws in the preparation of its proposed initial public offering on the Stock Exchange of Hong Kong. According to the remedy measures raised by the underwriter facilitating the initial public offering process, Green Trend needed to raise additional capital of ¥1 billion with a proposed annual interest rate of about 30 per cent. Of course, the company could have postponed the initial public offering; however, without the long-awaited return following a successful initial public offering, all the efforts taken in past years would be in vain. The uncertainty of the capital market would further complicate the future road to an initial public offering. Did Green Trend have better alternatives?
In recent years the world has witnessed a growing wave of entrepreneurial ventures in developing economies. CB Insights reports as of March 2020, developing economies have produced 160 unicorns, equivalent to 35 percent of all unicorns in the world. Much of the success of these unicorns can be attributed to entrepreneurs having increasing access to financing. Our study, however, found that access to financing varied significantly across developing economies and remained as a key challenge for entrepreneurs in most of those economies. Differences in legal structures, operating conditions, talent pools, and available capital sources created myriad conditions for entrepreneurs to navigate when seeking funding to build and scale their ventures.
Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by the latest technological advances in artificial intelligence and machine learning, deepfakes offer automated procedures to create fake content that is harder and harder for human observers to detect. The possibilities to deceive are endless-including manipulated pictures, videos, and audio-and organizations must be prepared as this will undoubtedly have a large societal impact. In this article, we provide a working definition of deepfake together with an overview of its underlying technology. We classify different deepfake types and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, expose deepfakes early, advocate for legal protection, and leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter deepfake tricks as we appreciate its treats.
The range of topics and the opinions expressed on artificial intelligence (AI) are so broad that clarity is needed on the the field's central tenets, the opportunities AI presents, and the challenges it poses. To that end, we provide an overview of the six building blocks of artificial intelligence: structured data, unstructured data, preprocesses, main processes, a knowledge base, and value-added information outputs. We then develop a typology to serve as an analytic tool for managers grappling with AI's influence on their industries. The typology considers the effects of AI-enabled innovations on two dimensions: the innovations' boundaries and their effects on organizational competencies. The typology's first dimension distinguishes between product-facing innovations, which influence a firm's offerings, and process-facing innovations, which influence a firm's operations. The typology's second dimension describes innovations as either competence-enhancing or competence-destroying; the former enhances current knowledge and skills, whereas the latter renders existing skills and knowledge obsolete. This framework lets managers evaluate their markets, the opportunities within them, and the threats arising from them, providing valuable background and structure to important strategic decisions.
Machine learning holds great promise for lowering product and service costs, speeding up business processes, and serving customers better. It is recognized as one of the most important application areas in this era of unprecedented technological development, and its adoption is gaining momentum across almost all industries. In view of this, we offer a brief discussion of categories of machine learning and then present three types of machine-learning usage at enterprises. We then discuss the trade-off between the accuracy and interpretability of machine-learning algorithms, a crucial consideration in selecting the right algorithm for the task at hand. We next outline three cases of machine-learning development in financial services. Finally, we discuss challenges all managers must confront in deploying machine-learning applications.
Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature-coupled with our experiences as practitioners-suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.
Artificial intelligence (AI) and machine learning (ML) may save money and improve the efficiency of business processes, but these technologies can also destroy business value, sometimes with grave consequences. The inability to identify and manage that risk can lead some managers to delay the adoption of these technologies and thus prevent them from realizing their potential. This article proposes a new framework by which to map the components of an AI solution and to identify and manage the value-destruction potential of AI and ML for businesses. We show how the defining characteristics of AI and ML can threaten the integrity of the AI system's inputs, processes, and outcomes. We then draw from the concepts of value-creation content and value-creation process to show how these risks may hinder value creation or even result in value destruction. Finally, we illustrate the application of our framework with an example of the deployment of an AI-powered chatbot in customer service, and we discuss how to remedy the problems that arise.
Practitioners are seeking information and ethical guidance related to the design and utilization of artificial intelligence (AI) across a variety of applications. Developments at Google illustrate the potential good of AI as well as potential concerns. In this article, we use Google to create a context that demonstrates the relevance of virtue for the ethical design and use of AI. We describe a set of ethical challenges encountered by Google and introduce virtue as a framework for ethical decision making that can be applied broadly to numerous organizations. We also examine support for virtue in ethical decision making as well as its power in attracting and retaining the employees who develop AI and the customers who use it. To conclude, we apply the virtue framework to Google's AI challenges and offer suggestions for its use in other organizations.
Artificial intelligence applications in cognitive computing systems can be found in organizations across every market, including chatbots that help customers navigate websites, predictive analytics systems used for fraud detection, and augmented decision-support systems for knowledge workers. In this article, we share reflections and insights from our experience with AI projects in the public sector that can add value to any organization. We organized our findings into four thematic domains-(1) data, (2) technology, (3) organizational, and (4) environmental-and examine them relative to the phases of AI. We conclude with best practices for capturing value with cognitive computing systems.
AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.
Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI's adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.
This case describes the ethical and legal issues involved in the process of using social media to collect job applicants' information during human recruitment. The scenario of this case is discussing about one security software company that uses Facebook to obtain additional information about recruiters during the recruitment process, and to decide who to hire.
This case describes that Sinyi Real Estate Inc. held the belief that “righteousness before profits” and launched the “We Are One” Project, hoping to make the community more gelled. In each stage promoted by the “We Are One” Project", Sinyi Real Estate Inc. responded to different challenges through rolling correction and continuous improvement that had achieved today's dazzling results.
At the center of one of the largest corruption scandals in Latin America, Brazilian conglomerate Odebrecht signed a leniency agreement with American, Swiss and Brazilian prosecutors in 2016 admitting to paying bribes in 12 countries. In an effort to regain financial stability and restore its damaged reputation, the Group overhauled its governance and set in motion a major organizational transformation effort. The case describes the Group's transformation journey and details various dilemmas that the new board of directors faced as the Group sought to put the scandal behind it and forge a path to the future.
In 2020, Falabella, one of the largest Latin American multi-format and multi-country retailers, faced increasing competition from the online world. The growth of online giants posed significant challenges for Falabella’s senior executives, who needed to decide how to allocate investments in this highly competitive environment. There were also growth opportunities in the brick-and-mortar business in many Latin American countries. Falabella needed to assess how to compete in the dual online and off-line world. The COVID-19 pandemic had created additional challenges for Falabella’s management team, as most shopping malls and stores were obliged to close for a long period of time, and the lower off-line sales were not expected to be fully compensated by increasing online sales. Should Falabella’s executives continue to focus on the historical strength of Falabella, fuelled by horizontal, vertical, and international expansion? Should they focus their efforts on the online business and divest some of their current business units.
In November 2016, the secretary of Mannarkkad Rural Service Co-operative Bank Ltd. (MCB) based in Kerala, India, learned that the prime minister of India had announced that large-denomination currency notes would be invalid as of midnight November 8. This demonetization move was to eradicate unaccounted for "black money" from the nation. Co-operative banks like MCB were excluded from the purview of India's central bank, the Reserve Bank of India, and as a primary agricultural credit society providing short-term credit to rural borrowers, MCB stood out from similar institutions by providing best-in-class banking services and constantly innovating to meet its vision of providing "the pleasure of personal banking" to its customers. MCB was the only bank in India to provide 24/7, 365-day banking operations through its overnight counter, and through a series of innovations, it had successfully pushed the boundaries of a rural co-operative bank to provide maximum convenience to its customers. The secretary of MCB now had to make some critical decisions: How should MCB handle the demonetization crisis with its existing and potential customers? Should MCB keep its overnight counter open? Should the secretary alert the bank's micro-ATM agents? Would MCB's parent bank provide funds? How could he address these concerns in a way that would maintain the goodwill MCB had built up among its customers over the past 27 years?