Kevin Schmitt
Last Name
Schmitt
First name
Kevin
Email
kevin.schmitt@unisg.ch
9 results
Now showing 1 - 9 of 9
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Item type:Publication, Alpes Bank’s journey to creating value at scale with generative AIThis teaching case portrays Alpes Bank, a Swiss universal bank whose branch-centric, premium-service model is challenged by generative AI (GenAI). This teaching case follows Tamara Maurer’s board mandate to deliver a “no-regret” GenAI pilot within one quarter. After evaluating several GenAI use cases, Tamara Maurer’s team builds a retrieval-augmented generation (RAG) email assistant with an external partner (i.e., AILabs). The GenAI pilot surfaces several unexpected tensions. Nonetheless, during a limited rollout, the GenAI email assistant reduces handling time for routine inquiries by 21%. Part B focuses on laying the foundation for scaling GenAI use cases. Tamara Maurer must now help Alpes Bank move from a special-project setup to an organizational design and governance process that supports enterprise-wide scaling. Questions arise about where, in general, AI-related activities should be positioned within Alpes Bank and what new roles are necessary to govern GenAI use cases effectively.Type:journal articleJournal:Journal of Information Technology Teaching Cases - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Create Generative AI Value at ScaleCompanies that have established a new kind of internal AI organization called the “AI spine” are using the cross-functional structure to rapidly develop innovative generative AI use cases with users’ help. The spine enables greater sharing of ideas and expertise across business units, which helps spark new ideas about where GenAI can be used to improve processes organizationwide. Disciplined project governance keeps the company’s resources focused on where a positive impact from AI is likeliest.Type:journal articleJournal:MIT Sloan Management ReviewVolume:67Issue:4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, GENERATIVE ARTIFICIAL INTELLIGENCE GOVERNANCE: A STRUCTURAL PRACTICE PERSPECTIVE(2026-06-15); Teresa GrauerWe analyzed how eight organizations seek to govern Generative Artificial Intelligence (GenAI). We identified five key governance roles (i.e., technology owner, business owner, knowledge owner, risk and compliance, and end user). Moreover, we showcase that each role controls different resources that become critical at different lifecycle stages. Specifically, we demonstrate that the focal power dependence relation moves away from “the business depends on engineers to build AI” toward “engineers depend on knowledge owners, risk and compliance, and end users to steer AI.” We further showcase three organizational coordination mechanisms (i.e., AI Business Unit, AI Squads, and AI Spine) and their respective effectiveness in managing evolving power-dependence relationships in an AI governance context.Type:conference paperJournal:European Conference on Information Systems (ECIS) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Organizing at the Artificial Intelligence Frontier(2025-12-17)Researchers examined many aspects of AI. However, we still know little about how organizations can effectively allocate resources to AI. This is surprising because AI's nondeterministic nature makes it harder to fully predict resource dependencies. This raises a key question: how do organizations allocate resources to a technology when they do not always know what will be needed or when? We conducted a comparative, inductive multiple-case study of organizations that implemented AI in their customer service center to explore this puzzle. We utilize resource dependence theory to identify three responses to AI's increasingly unpredictable resource dependencies: "AI Business Unit," "AI Team," and the "AI Stream." Each reflects a different structural approach to managing AI's increasingly unpredictable resource dependencies. Our research offers insights into (1) how organizations understand AI's resource dependencies, (2) how they can structure themselves to manage these dependencies, and (3) how resource dependence theory applies to AI.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementing Conversational AI: Institutional Logic, Responses, and Constraint Absorption(2024-08); Over the last few years, Conversational AI has become increasingly prevalent in serviceoriented industries. This longitudinal dialogical action research study delves into the antecedents of why service-oriented industries implement Conversational AI in their customer service operations. Second, it elucidates the interplay of institutional logic and organizational responses in addressing resource constraints encountered during the technology's implementation. We collaborated with six Swiss retail banks and insurance companies to analyze their Conversational AI implementation practices. Our research identifies three organizational responses to absorb resource constraints: development of AI Business Unit Capabilities, formation of specialized AI Teams, and establishment of AI Value Streams. Furthermore, our research identified that a company's institutional logicfrom opportunistic and cautious to pragmatic AI utilization and innovative customer-centricsignificantly molds its organizational response to accessing external resources needed during Conversational AI implementations. Additionally, these organizational responses exhibit varying degrees of external resource-accessing capabilities (i.e., full, partial, or minimal).Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NAVIGATING RESOURCE CONSTRAINTS IN CONVERSATIONAL AI IMPLEMENTATIONS: THE ROLE OF INSTITUTIONAL LOGICS AND ORGANIZATIONAL RESPONSES(2024-06-16); In recent years, conversational AI solutions have become increasingly prevalent in customer service centers within the retail banking and insurance industry. This case study delves into the interplay and influence of institutional logic on organizational responses in addressing constraints encountered while implementing these conversational AI solutions. We collaborated with six prominent Swiss retail banks and insurance companies to analyze their conversational AI implementation practices over one year. Our research identifies three primary organizational responses to absorb resource constraints in this context: development of AI Business Unit Capabilities, formation of specialized AI Teams, and establishment of AI Value Streams. Furthermore, we identified that a company's institutional logicranging from opportunistic and cautious to pragmatic AI utilization and innovative customer-centricsignificantly molds its organizational response to resource constraints. Additionally, these organizational responses exhibit varying degrees of constraint absorption (i.e., full, partial, or minimal).Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Illuminating Smart City Solutions -A Taxonomy and Clusters(2023-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Challenges and Good Practices in Conversational AI-Driven Service Automation(2023-12); ; Conversational AI offers novel opportunities for companies to automate customer interactions. However, many companies grapple with effectively implementing conversational AI. Utilizing an engaged, consortium-based research approach, we examine the unique challenges faced by six companies in the insurance and banking sector while implementing conversational AI solutions and identify best practices to address these challenges. Finally, drawing upon the lessons learned, we offer guidance for developing conversational AI capabilities and fostering conversational AI success stories.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Demystifying Industrial Internet of Things start-ups -A multi-layer taxonomy(2022-02); Wethmar, SimonDescribed as a fundamental paradigm shift by researchers, the Industrial Internet of Things (IIoT) is credited with massive potential. In the context of emerging technologies, such as the IIoT, start-ups occupy a crucial role, as new technologies are often first commercialized by start-ups. Because of the rising importance of IIoT start-ups as drivers of industrial innovation, IIoT solutions demand deepened theoretical insights. As existing classification schemes in the industrial context do not sufficiently account for the ever more critical role of IIoT start-ups, we present a multi-layer taxonomy of IIoT start-up solutions. Building on state-of-the-art literature and a sample of 78 real-world IIoT start-up solutions, the taxonomy comprises ten dimensions and related characteristics structured along the three layers solution, data, and business model. The taxonomy contributes to the descriptive knowledge on the IIoT and enables researchers and practitioners to better understand IIoT start-up solutions.Type:conference paper