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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.

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.

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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?

The case discusses how cryptocurrencies such as Bitcoin posed a threat to Visa Inc. (Visa). In March 2019, the Kroger Co., a leading retailer in the United States, was planning to accept cryptocurrencies from consumers as a mode of payment. According to Alfred F. Kelly, the chief executive officer of Visa, cryptocurrencies were an inefficient means of retail-related financial transactions, though the company invested in the research and development of cryptocurrency for corporate transaction purposes such as cross-border payments. Should Kelly consider cryptocurrency as a disruptive force in the retail sector? Should she focus more on the retail or the business to business sector for cryptocurrency-based transactions?

This public-sourced case describes the evolution of Facebook monitoring users' activity around what potentially offensive or disturbing material could stay posted and what should be removed. Following intense media and regulatory (e.g., Congressional) scrutiny, the company revised policies and reorganized content-review teams. The case offers an opportunity to debate a key question the company faced: What should platform governance look like? As Facebook faced an exponential growth in the number of user-generated posts daily, posts that contained harmful, inappropriate content also increased, raising the need for Facebook to devise a set of guidelines that were not only applicable across many different types of posts and situations, but were also perceived as acceptable by Facebook users. The tradeoffs a platform business makes between user characteristics, intent, outcome, and norms can be explored as students put themselves in CEO Mark Zuckerberg's shoes to redesign Facebook's platform-governance policies. Students will also have a chance to examine how certain platform-governance policies can subsequently impact Facebook's future strategy: for instance, the more Facebook got involved in curating content for its users-thus serving as arbiter-the more potential to shift Facebook from a tech business to a news publisher increased, thereby subjecting the company to a different set of regulations.

In 2019, Nadine Vogel, founder and CEO of Springboard Consulting, a firm that worked with Fortune 500 companies on issues related to disability and their workforce, faced the decision of the best path forward to grow her small company. Should she build more and better expertise to expand the topics she worked on with her current clientele? Or should she explore the possibility of moving into the new market of smaller businesses?

Bringing service transformation is always a challenge for any organization. this is not anymore dependent upon the bifurcation of business as into product selling or service selling. All successful businesses are finally customer centric and experience centric. Rather They are focused on how and what the customers feel about the organization for them to want to enter into long term relationships with that company. The case of Delhi International Airport discusses the challenges faced by an entrepreneur to bring his business philosophy and values int reality. physical infrastructure is the tangible component ; behavior, attitude, empathy of the people serving the customers are intangible and critical components. Policies, Processes , Structure and Strategies are the part of the organization design. This case revolves around the complexities Rao faced and then addressed service culture issues in the face of socio economic and policy hurdles forms the learning from the case. This case posses important questions about linking the three key factors, i.e. functioning of th airport, involvement of the private player, evolution of PPP policy in India, with the vital aspect of customer service experience in the Indian airports. The case highlights the complexities of the Indian business environment where service thinking and customer centric service design are largely lacking. It focuses on the entrepreneurs decisions to align the various stakeholders to create service experience for airport customers.

Why are companies still so vulnerable to disruptive threats? The problem isn't that they don't have the right playbook. It's that well-intentioned leaders often downplay disruptive threats or overestimate the difficulty of response. In simple terms, they lie to themselves. This means dealing with disruption is not just an innovation challenge; it is a leadership challenge. Here's how to avoid the delusions about disruption and self-sabotage.

When faced with deep uncertainty, organizations often develop a habit of controlling for internal, known variables and fail to track external factors as potential disrupters. This practice lures decision-makers into a false sense of security, and it forces a narrow framing of the future. As a result, even the most successful businesses become vulnerable to disruptive forces that appear to come out of nowhere.

Technology innovation has created a new ethical paradigm. As companies specialize in delivering single components of complex systems, value chains are being reshaped across industries. Product interfaces are becoming standardized, and companies are profiting by perfecting narrow slices of the value chain. This rapid modularization has magnified the ethical implications of every business decision. As businesses work to delight consumers, they must also protect the public trust.

In 2007, Trek Bicycles (Trek) entered the Indian market. Although Trek's market share in India was small compared to its global presence, India held an important position in Trek's global aspirations, and the country's potential for growth drove Trek to enter that market. The company strongly believed in innovation and in delivering value to customers through a bundle of services and schemes. Trek operated in the country through a partnership with an Indian distributor and in 2017 set up its Indian subsidiary. The company had a first-mover advantage with respect to the sale of super-premium bicycles in India, but in 2019, after more than a decade, how could the company increase its retail network and grow the community that was connected with cycling?