Despite massive investments in artificial intelligence, most pilots fail to achieve full business impact. We argue these failures stem from a critical oversight: Organizations treat employee and customer AI adoption as separate challenges rather than recognizing their interdependence. By analyzing past technology adoptions and interviewing employees and customers on current AI considerations, we identify consistent patterns where success hinges on simultaneously addressing both stakeholder groups’ needs across different implementation stages. This paper introduces an integrative framework that maps management strategies to the intersection of the Gartner Hype Cycle’s stages (Hype, Disillusionment, Enlightenment) and key stakeholders (Employees, Customers). Informed by theories of social contagion, socio-technical systems and the service-profit value chain, we distill our findings into a managerial toolkit. Our framework yields five recommendations: (1) set realistic expectations early to avoid credibility-damaging overhype; (2) invest in employee reskilling during disillusionment; (3) offer tangible customer benefits to maintain trust; (4) give employees agency in shaping AI applications; and (5) maintain competitive customer value. We demonstrate how organizations ignoring these principles experienced failures, while those applying them achieved sustainable integration. Our framework offers managers actionable guidance for navigating AI’s unique challenges. Unlike previous specialized technologies, AI broadly touches customer interactions and employee workflows simultaneously, meaning failures in one group rapidly cascade to the other. Organizations recognizing and managing these interdependencies from the outset can overcome daunting failure statistics and realize AI’s transformative potential.
Management scholars have recently rekindled their interest in approaching management mainly as a craft rather than as an exact science. If management is indeed a craft, then it is only reasonable to consider alternative modes of teaching that better reflect this nature. Against this backdrop, we introduce a framework for stimulating a craft-oriented pedagogy in management education, comprising four modes of teaching craft in management education. This approach would arguably foster a profound recognition of management-as-a-craft and cultivate future managers into becoming reflective crafts(wo)men.
Ecosystems have become an important topic in business research and practice. While researching ecosystems has gained popularity, there is not much discussion among educators about how to incorporate ecosystems into business education. An ecosystem in this context is defined as an agglomeration of interconnected individuals, entities, and regulatory bodies in each geographic area. Incorporating ecosystems into a business education curriculum can help educators better prepare students for a complex and rapidly changing workplace. Based on a case study of an actual workshop developed around ecosystems, we define an ecosystem and outline the benefits of exposing students to ecosystems. We then provide specific strategies for implementation in a higher education setting.
Although both organizational innovation and culture have been extensively studied by management researchers, there has been comparatively little research done on how to measure innovation culture and align it to maximize performance. Part of the reason for this is purely pragmatic. Organizations are heterogeneous, making it difficult to run large studies that differ in terms of size, product, market, and so on. Furthermore, the dynamic capabilities that manifest in cultures of innovation are causally ambiguous and deeply held within organizations. As a result, studies have typically focused on the link between innovation and culture within a single organization or a particular metric of innovation culture, for example, technology transfer leading to IP output, both of which are easier to measure across different organizational contexts. The problem is that innovation culture is multidimensional and prevailing studies are unable to provide an inclusive picture of what drives innovation culture as well as its downstream value chain impacts. The result is a somewhat myopic approach to measuring and, consequently, improving innovation culture that circumvents the holistic systems approach to the opportunity.This article highlights the outcomes of the ETHOS project undertaken by the Department of Energy (DOE). It compares and contrasts the innovation culture scores across six of the 17 DOE Labs, considering innovation drivers involving leadership, resources, knowledge management, and process. Further, the ETHOS study correlates objective innovation culture scores with Lab performance and reveals that a 10-point change in culture score can lead to a 3x increase across select KPI performance. The article concludes with a multi-point plan to open an organization’s innovation vault.
Agentic AI is reshaping how organizations operate. As autonomous systems take over pricing, content, personalization, and supply chain decisions, the human choices that historically built brand identity are progressively displaced. We term the result agentic brand drift: the gradual, unintended divergence between a firm's intended brand identity and the emergent brand character produced by its AI-orchestrated operations. Unlike brand inconsistency or deliberate identity change, agentic brand drift is internally generated, has no triggering event, and co-occurs with improving performance metrics, making it invisible to conventional monitoring. Critically, this failure mode falls outside the scope of existing AI governance frameworks such as NIST AI RMF and ISO/IEC 42001, which govern system behavior rather than meaning coherence. A firm executing those frameworks flawlessly will still experience agentic brand drift. We theorize three mechanisms, Decision Diffusion, Temporal Collapse, and Accountability Dissolution, operating as a causal sequence, and derive two complementary frameworks, CORE and GUARD, that give organizations capabilities existing governance does not provide: identifying which decisions carry identity stakes, supplying agents with organizational reasoning behind past brand choices, and monitoring output patterns for identity coherence over time.
Marketing’s adoption of artificial intelligence (AI) has accelerated rapidly, yet performance outcomes remain inconsistent across firms using similar tools. Drawing on contingency theory and an empirically validated hybrid typology of business strategy — Prospectors, Analyzers, Low-Cost Defenders, and Differentiated Defenders — this article argues that, to create maximum value, AI must be aligned with how a firm competes. Building on this framework, the article identifies 16 AI applications aligned with the competitive priorities of each strategic type and introduces a five-stage roadmap for implementation. Integrating recent research (2020–2026) and real-world examples, this article offers actionable guidance for marketing managers seeking to convert AI adoption into sustained performance improvement.
Business education is undergoing rapid transformations thanks to advancements in emerging technologies. Business school leaders are engaging in identifying approaches to revitalize business education, preparing students to succeed in a technologically advancing workplace. Primarily anchored in the concept of digital transformation, our article provides practical guidance to help business schools integrate artificial intelligence (AI) into their curricula and infrastructure, benefiting students, faculty, administration, and leadership. We offer a roadmap that lays out current industry trends and articulates pertinent action items, related cost efforts, and associated benefits for business education. This article positions business schools to instill a human-AI augmentation mindset in students, faculty, staff, and leadership that prepares them to work with ever-changing technologies. Regularly revising business course curricula to stay aligned with evolving industry needs is key to providing topical content and meaningful skill development opportunities for students. We argue that these efforts directly enhance student employability and learning, better preparing students to succeed in the increasingly technology-enabled workplace.
Business school deans and academic leaders are making high-stakes strategic decisions in an environment defined by enrollment pressure, growing skepticism regarding degree value, intensified competition from alternative credentials, rapid technological change, and constrained internal capacity. While calls for transformation are widespread, administrators often lack a clear framework for deciding what to change, why those changes are necessary, and how to take action under institutional constraints. This article offers a practical decision framework designed explicitly for business school leaders. Drawing on a marketing systems perspective, we position business school offerings as the central object of strategy and show how stakeholders evaluate those offerings based on perceived self-interest, mutuality, and morality. The framework presents six interconnected social mechanisms that shape how offerings are interpreted, legitimized, and acted upon by students, faculty, employers, policymakers, donors, and technology firms. Rather than prescribing specific solutions, the framework helps leaders diagnose misalignment, anticipate resistance, and make defensible tradeoffs about where to invest, what to prioritize, and what to discontinue. By translating system-level pressures into actionable guidance, this article equips business school administrators to rebuild trust, mobilize constrained internal labor, and make coherent strategic decisions in a rapidly changing higher education landscape.
Digital advertisers increasingly rely on dashboards, automated bidding, and platform filters to manage campaign performance, yet these systems can conceal competitor click fraud (CCF) that depletes budgets and corrupts optimization signals. CCF occurs when rivals generate illegitimate clicks to exhaust advertising budgets, distort performance metrics, or weaken auction outcomes. We argue that CCF is not only a technical detection problem, but a set of tensions and governance problems created by opaque data, misaligned incentives, and automated decision systems. Based on interviews, practitioner responses, and expert consultations, we identify five paradoxical tensions that explain why firms struggle to assess, prioritize, and manage CCF: measurability versus unknowability, incentives to combat versus incentives to accept fraud, multidimensional harms versus perceived harmlessness, automation and AI as remedy versus vulnerability, and common competitive tactics versus competitive sabotage. We show how these tensions encourage pragmatic disengagement, underinvestment in controls, and recurring rediscovery cycles. We offer practices for building auditable advertising governance, including platform scrutiny, anomaly monitoring, traffic-quality KPIs, calibrated fraud filters, and cross-functional escalation routines.
To perform well in the workplace, employees must have energy: the feeling and capability of being able to act. Employees’ energy levels are influenced by their interactions with their leaders. Previous research has identified overtly toxic behaviors such as bullying or harassment which produce negative effects on energy. However, leaders may also drain the energy of those they oversee through more subtle means that to this point have not been explored by management scholars. We address this gap by introducing the concept of “deenergizing leadership behavior,” which encompasses the more obviously destructive toxic behaviors, but also more understated actions. Drawing on related literature on organizational psychology and energy, we examine three categories of energy: physical, cognitive, and emotional, and we describe the mechanisms through which leaders may—even inadvertently—deplete each type, leading to disengagement, productivity declines, and turnover.We also argue that in leader-follower relationships, the inherent power asymmetries, the heightened expectations placed on leaders, and employees’ limited ability to avoid their leaders could magnify the effects of deenergizing behavior. We hope to inspire further research into deenergizing leadership and to improve leaders’ ability to recognize deenergizing behaviors in themselves and in others. We provide a self-reflection tool to help leaders identify and address their own deenergizing tendencies, enabling them to reclaim their roles as energy boosters rather than energy vampires.
Artificial intelligence (AI) is increasingly embedded in customer feedback systems, promising speed, scale, and predictive insight. Yet many organizations struggle to integrate AI without eroding empathy, trust, and relational quality. This paper examines how managers balance AI-driven efficiency with human judgment in customer feedback management. Drawing on twenty semi-structured interviews across government, healthcare, marketing, finance, and technology sectors, we surface five interconnected findings: AI’s operational power as a high-volume extraction engine; its structural limitation of nuance blindness in contextual interpretation; the counterintuitive finding that human involvement introduces its own distortions and is not a reliable corrective; AI’s underutilized role as a governance stabilizer that depoliticizes decisions and legitimizes authority; and AI’s upstream influence in reshaping the generative conditions of feedback itself, altering what people are willing to say and to whom. Building on these insights, we develop a structured hybrid playbook outlining how managers can design, govern, and audit human–AI feedback systems. The playbook offers actionable guidance on task allocation, escalation protocols, accountability roles, and governance mechanisms that allow firms to scale intelligence without sacrificing empathy. The findings position AI not as a replacement for human judgment, but as a complementary engine for service improvement when deliberately orchestrated.
The goal of this paper is to reveal the hidden aspects of AI for sales. Since the emergence of artificial intelligence, scholars have advanced divergent perspectives on its impact on sales. While some emphasize its potential to enhance performance, others highlight its risks and unintended consequences. As a result, the literature remains fragmented and, at times, inconclusive. This ambiguity largely stems from inherent paradoxes in the application of AI to sales contexts. Specifically, visible benefits of AI often obscure hidden costs, just as apparent drawbacks conceal less obvious advantages. This asymmetry in what is observable versus what remains latent can lead organizations to make suboptimal decisions. This paper identifies and analyzes the primary paradoxes associated with AI in sales, unveils the hidden factors that lead organizations to make imperfect decisions, explains the mechanisms underlying these tensions, highlights the implications for organizations, and proposes practical, actionable recommendations to address them.
Over the past two decades, behavioral science has reshaped our understanding of human decision-making, revealing how cognitive biases, heuristics, and contextual influences affect individual and organizational behavior. The Association to Advance Collegiate Schools of Business (AACSB) has emphasized the need for business education to contribute meaningfully to societal impact. This article argues that integrating behavioral science into business education, both broadly and systematically, is essential to fulfilling this mission. Drawing on the distinction between the i-frame (individual-level) and the s-frame (system-level) approaches, we critique the current emphasis on individual decision-making and advocate for business education that also trains students to design behaviorally informed systems. This article offers concrete strategies for educators to embed dual-frame thinking in pedagogy by redesigning case studies and reconfiguring in-class exercises to engage students in school-wide, behavior-focused projects. The authors call for a reimagining of business education that equips students not only to make better decisions themselves but also to create environments in which better decisions are the default, enabling more equitable, ethical, and sustainable outcomes at scale.
The ongoing United States (US) and China decoupling has intensified uncertainty in global trade, compelling multinational corporations (MNCs) to reassess and restructure their resource dependencies and adapt to the new global environment. Drawing on resource dependency theory (RDT) and institutional theory, this study examines the impact of the US-China decoupling on MNCs and highlights their actions for navigating the resulting complexities. To address these complexities, MNCs are strategically relocating their operations to other emerging economies like Latin America, the Caribbean, Africa, India, or ASEAN nations. This process of MNC relocations will lead to a multifoci network economy, in which firms develop resilience via regional diversification and institutional adaptation. By reconfiguring their operations, MNCs not only navigate geopolitical disruptions but also enhance long-term sustainability and competitive advantage. This study contributes to the literature by providing a theoretical and practical framework for understanding how MNCs proactively manage uncertainty amid shifting global trade dynamics. (c) 2025 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Over the past 2 decades, the cloud computing market has evolved into a global oligopoly dominated by a few major players, including Amazon Web Services, Microsoft Azure, and Google Cloud, which offer highly scalable and technologically advanced platforms. The variety and complexity of their offerings necessitate complementing servicesdranging from advice on benefits and risks of cloud transformation via implementation and migration projects to continuously operating, securing, and optimizing a cloud environment. The range of customer demands is as vast as the number of ways to respond. This study synthesizes the broad range of services in this area, as well as the corresponding market demands and preconditions to provide such services. We suggest a framework designed to help the stakeholders in this market navigate a dynamic, evolving ecosystem comprised of hyperscalers, IT service providers, and their customers, and provide recommendations to address existing challenges. (c) 2025 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
Healthcare providers face mounting pressure to deliver more personalized, efficient, and accountable services, yet many struggle to translate advances in artificial intelligence (AI) in improved patient experiences. This article argues that AI’s transformative potential in healthcare lies not in technology alone, but in how it is designed, governed, and embedded within service experiences. Drawing on service management and service ecosystem perspectives, we develop a conceptual framework that explains AI-enabled healthcare services across three interrelated dimensions: service personalization; service intelligence and evolution; and service outcomes and stewardship. We contend that AI-enabled service innovation in healthcare is increasingly facilitated through modular, turnkey solutions rather than bespoke AI development. At the same time, AI innovation is shifting to the patient level through wearable devices and in-home monitoring technologies, extending service experiences beyond traditional clinical settings. Together, these developments introduce new managerial challenges related to interoperability, governance, and accountability across the healthcare ecosystem. We outline a set of organizational capabilities to address these challenges and offer actionable guidance for managers seeking to harness AI to strengthen service value co-creation in provider–patient relationships.
Managing a low-cost business model and a full-service business model, when both operate in parallel within the same company, has become a recurring challenge. Although a priori clear organizational and strategic separation could be a way to avoid conflicts, managers often deviate from this logic, as business models interact and evolve over time. This qualitative study examines how these business model “tandems” (pairs of legacy full-service and low-cost business models within a company) develop and how managers navigate resulting tensions. Using longitudinal data from three groups of airlines between 1992 and 2017, we analyze eleven business model tandems and track their convergent and divergent movements. We find that business model tandems rarely remain stably separated. Instead, their evolution may be driven by internal conflicts and managers' efforts to achieve synergies, resulting in a “synergistic thinking barrier” that limits extreme separation. The study contributes to research on business model dynamics and multiple business models, and offers managers practical guidance on when to separate and integrate coexisting business models.
