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Montreal Toheed Society (MTS), a community-based organization founded in 2012, has adopted various social media technologies to capture member interactions and to make the work of its board of directors more efficient and effective. The organization attempted to meet its needs by using JomSocial at first, followed later by Viber. MTS was partially successful in reaching its social media goals by implementing the Viber platform. The organization is now considering migrating to a new platform to overcome some limitations of the Viber technology. Two options for a new social media platform are Telegram and WhatsApp. In order to ensure a successful migration to a new platform, MTS must consider several questions: What should be the governing structure of the new social media platform? What would be the optimal balance of user freedom versus control? What sort of content should be allowed on the platform? Given its structure, how can MTS find the resources to manage a larger digital community?

Align Technologies was a leader in disrupting the orthodontist and dental markets with the original development of clear plastic aligners, which allowed for customized solutions to malocclusions, the incorrect alignment of teeth and jaws. Unlike traditional braces, plastic aligners enabled more flexibility and customization by combining digital and physical technologies that were convenient and attractive than other alternatives. As the company began to enter its third decade, the leadership team was dealing with the challenges of new competition, expiring patents, how to improve international growth and how best to increase operational efficiency.

Mark Cooke, Global Head of Operational Risk, needed to decide between a traditional regulatory control system and a new regtech system to manage non-financial risks. Non-financial risks failures such as money laundering and tax evasion had cost HSBC billions of dollars in fines and settlements over the previous decade. In response, HSBC had hired thousands of risk and compliance staff and invested billions in traditional control systems. Cooke, however, could not be sure HSBC's traditional methods were sufficient and he was worried they were not sustainable. Cooke recently ran a pilot test of a regtech solution that promised to warn of problem areas in advance, and do so at a much lower cost than traditional systems. The regtech solution provider was a small startup and the technology was not fully developed. Cooke wondered how much he could trust a technology from a startup company with a handful of employees and almost no revenue to meet the needs of a world-leading bank with global operations.

In 2014, the Internet of things (IoT) was still considered in its infancy stage. Over the next two years, the IoT matured and represented a considerable amount of uncertainty for the entire electronics industry. In 2016, the industry was at an inflection point regarding the technology, and its largest competitors had to make a choice: risk investing in research and development for a potential return (or failure), or stay the course and let competitors assume the risk. The chief executive officer of Samsung Electronics Co. Ltd. (Samsung), Boo-Keun Yoon, was in a position to take a stand in regard to the IoT and invest US$1.2 billion, which could have a major impact on the company's future. It was up to Yoon to decide.

By the end of 2018, a recent business program graduate had found that his first social enterprise had been a success, and he wanted to start his next venture in the Kenyan agricultural market. He identified three challenges in this area-climate change, lack of market access, and ineffective education systems-and he wanted to try to address one of these with a new business. Case A explores how he needed to perform a market assessment to understand the opportunities and constraints of the Kenyan market, identify the resources and processes that would be available to him when starting the business, and form strategies for performing customer research.

Supplemental to case W19471 With his primary research on the Kenyan agricultural market completed, the entrepreneur needed to analyze the new data using design-thinking frameworks, and then formulate a value proposition that used his resources and processes to address the large-scale improvements identified in Case A.

Sunrise Construction Ltd. (Sunrise) had a critical problem with the firm's Time Tracking System (TTS) business processes. The management team deemed the TTS fix a top priority for the firm since the failing system was crippling the firm's operations. Ben Smith, Vice President, Operations for Sunrise was given the responsibility of presenting a solution at the next monthly management meeting to fix the TTS problem. Problems with the TTS escalated leading to substantial indirect costs such as ill-informed decisions regarding resource availability, project costs and payments which negatively impacted the firm's operations, reputation and increased the risk of regulatory litigation. As a result, the sustainability of future business operations relying upon the TTS was brought into question. Furthermore, data integrity issues, redundant processes and bottlenecks with the current TTS were estimated to directly cost the firm $21,000 annually. Smith hired an Information Systems consultant to help him navigate the faulty processes of the TTS, determine key system requirements and evaluate potential off-the-shelf, custom and process re-engineering solutions. They had to work quickly to determine a solution as the next management meeting was fast approaching and the firm was struggling.

Although companies spend billions on formal training for employees, most of the skills needed to perform a specific job can be learned only by doing it. This on-the-job learning (OJL) has long depended on mentorship, with experts coaching apprentices. But today OJL is under threat from the headlong introduction of sophisticated analytics, AI, and robotics into many aspects of work. These technologies are moving trainees away from learning opportunities and experts away from the action. The author describes the "deviant," rule-breaking work-arounds--"shadow learning"--that surgeons in training, police officers, M&A analysts, and others are figuring out on their own to overcome these obstacles and suggests how companies can benefit from studying them.

This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing-an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.

Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications.

This introduction to this special issue discusses artificial intelligence (AI), commonly defined as "a system's ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation." It summarizes seven articles published in this special issue that present a wide variety of perspectives on AI, authored by several of the world's leading experts and specialists in AI. It concludes by offering a comprehensive outlook on the future of AI, drawing on micro-, meso-, and macro-perspectives.

This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.

The capability of AI is currently expanding beyond mechanical and repetitive to analytical and thinking. A "Feeling Economy" is emerging, in which AI performs many of the analytical and thinking tasks, and human workers gravitate more toward interpersonal and empathetic tasks. Although these people-focused tasks have always been important to jobs, they are now becoming more important to an unprecedented degree. To manage more effectively in the Feeling Economy, managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being performed by AI, and, thus, human workers must place increased emphasis on the empathetic and emotional dimensions of their work.

Recent years have seen a reemergence of interest in artificial intelligence (AI) among both managers and academics. Driven by technological advances and public interest, AI is considered by some as an unprecedented revolutionary technology with the potential to transform humanity. But, at this stage, managers are left with little empirical advice on how to prepare and use AI in their firm's operations. Based on case studies and the results of two global surveys among senior managers across industries, this article shows that AI is typically implemented and used with other advanced digital technologies in firms' digital transformation projects. The digital transformation projects in which AI is deployed are mostly in support of firms' existing businesses, thereby demystifying some of the transformative claims made about AI. This article then presents a framework for successfully implementing AI in the context of digital transformation, offering specific guidance in the areas of data, intelligence, being grounded, integrated, teaming, agility, and leadership.

There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles-causal reasoning, randomization and experiments, and employee contribution-that would be both economically efficient and socially appropriate for using data science in the management of employees.

How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid-human-to-AI and AI-to-human-sequential decision making; and aggregated human-AI decision making.

As Shanghai MarcPoint Information Technology Co. Ltd. (MarcPoint) celebrated its fifth anniversary, its founder was quite pleased by what the company had achieved. MarcPoint was a start-up that offered marketing research services by analyzing user-generated content (UGC) with big-data technologies. The company had been successful and grown steadily since its inception in 2013. It was founded upon the realization that UGC was disrupting traditional marketing research and that big-data analytics provided the technological means to analyze the UGC efficiently and effectively. In 2018, the founder reflected on what MarcPoint's next steps should be: What technologies should they pursue? Which markets could they target for growth in the next five years? Should they try to transport MarcPoint's success to overseas markets? All in all, what needed to be done to sustain MarcPoint's growth and maintain its leading position in the turbulent technical and business environment?

The U.S. healthcare sector is inadequately prepared to deal with the reality of cyber threats. The increasing use of smart medical equipment and mobile devices is making healthcare organizations more susceptible to ransomware and other types of malware. The size and complexity of operations, coupled with the presence of numerous legacy and incompatible systems, make it difficult to implement effective cybersecurity measures. The daunting nature of the problem often results in an if-it-ain't-broke-don't-fix-it stance among senior healthcare leaders. The preponderance of healthcare related laws, compliance regulations, and security guidance frameworks serve to complicate the cybersecurity challenge further and too often results in senior leadership assuming a state of blissful ignorance. This study sheds light on the key factors contributing to the chaotic state of affairs and presents a roadmap to a more deliberate and proactive approach to cybersecurity risk management.

Located in Bengaluru, India, Xoxoday.com was an online marketplace for experiential gifting. The company started by offering social gifting, and selecting corporations as its target market. In 2013, the company decided to cater to both the business-to-business and business-to-consumer markets and entered into the niche sector of experiential gifting. The value proposition of the start-up was to provide the best gifting solutions for customers while delivering an experience that would build memories. Heading into 2016, Xoxoday.com aimed to grow by acquiring new customers. It promoted its experiences extensively on social media platforms, but the company's hard work was not paying off. Xoxoday.com needed to figure out what changes needed to be made to its social media plan in order to attract new customers.