Mahei Li
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Dr.
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Li
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Mahei
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mahei.li@unisg.ch

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Generative AI in Customer Support Services: A Framework for Augmenting the Routines of Frontline Service Employees

2024-01-06 , Philipp Reinhard , Mahei Li , Christoph Peters , Jan Marco Leimeister

Customer support service employees are facing an increased workload, while artificial intelligence (AI) appears to possess the potential to change the way we work. With the advent of modern types of generative AI, new opportunities to augment frontline service employees have emerged. However, little is known about how to integrate generative AI in customer support service organizations and purposefully change service employee work routines. Following multi-method qualitative research, we performed a literature review, conducted workshops, and interviewed IT support agents, managers, and AI experts. Thereby, we examine AI augmentation for frontline service employees in the context of IT support to carve out where and how GenAI can be leveraged to develop more efficient and higher-quality customer support. Our resulting framework reveals that especially adapting solutions and retaining knowledge is subject to a high degree of AI augmentation.

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German Standardization Roadmap on Artificial Intelligence

2022 , German Institute for Standardisation (DIN e. V.) , German Commission for Electrical, Electronic & Information Technologies of DIN and VDE (DKE) , Ernestine Dickhaut , Mahei Li , Christoph Peters , many other authors (see chapter 11) , Wolfgang Prof. Dr. Wahlster , Christoph Winterhalter

On behalf of the German Federal Ministry of Economic Affairs and Climate Action, DIN and DKE started work on the second edition of the German Standardization Roadmap Artificial Intelligence in January 2022. With the broad participation and involvement of more than 570 experts from industry, science, the public sector and civil society, the strategic Roadmap for AI standardization was thus further developed. This work was coordinated and accompanied by a high-level coordination group for AI standardization and conformity The standardization roadmap implements a measure of the German government’s AI Strategy and thus makes a significant contribution to “AI – Made in Germany”. Standardization is part of the AI Strategy and is a strategic instrument for strengthening the innovation and competitiveness of the German and European economies. Not least for this reason, standardization plays a special role in the planned European legal framework for AI, the Artificial Intelligence Act. This Standardization Roadmap AI identifies the requirements in standardization, formulates concrete recommendations and thus creates the basis for initiating standardization work at national level, and especially at European and international level, at an early stage. In doing so, the Roadmap makes a significant contribution to the European Commission’s Artificial Intelligence Act, supporting its implementation. The Standardization Roadmap AI focuses on nine key topics, which are addressed in Chapter 4: → The Roadmap begins with the basic topics, such as terminologies and definitions, classifications and ethical issues. They are the basis for AI discussions and are thus the central core of the Roadmap. → The security/safety of AI systems plays a crucial role in widespread use of AI solutions. Only a more in-depth consideration of requirements for operational safety and information security, for example, can enable the comprehensive use of AI systems in business and society. → Another key topic, and the basis for the broad market success of AI, is testing and certification. This requires reliable quality criteria and reproducible test methods that can be used to verify the properties of AI systems. They are a key prerequisite for assessing the quality of AI-based applications and contribute significantly to explainability and traceability – two factors that build trust and acceptance. → Another challenge in the use of AI, especially for small and medium-sized enterprises, is the integration of AI technologies in organizations. The focus here is on sociotechnical aspects such as human-technology interaction, humane work design, and requirements for business structures and processes, which are all examined in the Roadmap. → The fields of application of AI are extremely diverse. AI technologies are used in almost all business and application areas and offer great potential. To cover a broad spectrum of applications, the Roadmap considers industry-specific challenges for the following five sectors in particular, in addition to the cross-cutting issues mentioned above: Industrial Automation, Mobility, Medicine, Financial Services and Energy / Environment. The present Roadmap outlines the work and discussion results for all nine key topics and provides a comprehensive overview of the status quo, requirements, and needs for action.

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Nutzergenerierte Dienstleistungssysteme zur digitalen Transformation von Organisationen

2019 , Agarwal, Nivedita , Bästlein, Moritz , Böhmann, Tilo , Ernst, Sissy-Josefina , Fritzsche, Albrecht , Grotherr, Christian , Hoffmann, Holger , Klemm, Pablo , Leimeister, Jan Marco , Li, Mahei M. , Möslein, Kathrin M. , Peters, Christoph , Sarpong, Benjamin , Saxe, Sebastian , Schmidt, Thorsten , Schymanietz, Martin , Wurfbaum, Moritz S. , Semmann, Martin , Ziegler, Dirk

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Dynamic Solutions in Service Systems

2018 , Li, Mahei Manhai , Peters, Christoph , Leimeister, Jan Marco

With the rapidly increasing number and complexity of service demands, service providers need to become even more flexible and faster. To accommodate this, the service system needs to dynamically reconfigure its required resourced based on the context of both service providers and the customer. The resulting chosen service system configuration is called a dynamic solution. Our research question is therefore as follow: How can we implement the characteristic of dynamic solutions based on the service system model (SSM)? We demonstrate our model using a real-life citizen-based produce delivery service. We contribute by being able to quantify service system configurations and compare different configurations dynamically. Keywords: Service operations, operationalizing

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Take the Wheel - Technology-driven Change in the Energy Sector

2019 , Li, Mahei , Müller, Jennifer , Ernst, Sissy-Josefina , Peters, Christoph

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Nutzungszentrierte Gestaltung von HI-basierten Dienstleistungen am Beispiel des IT-Supports

2021-08-23 , Schmidt, Simon L. , Li, Mahei , Peters, Christoph , Leimeister, Jan Marco , Bruhn, Manfred , Hadwich, Karsten

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Publication

Digitale Servicesysteme

2019 , Li, Mahei , Peters, Christoph , Leimeister, Jan Marco , Zierau, Naim , Gassman, Oliver , Sutter, Philipp

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From Service Systems Engineering to Service Innovation - A Modeling Approach

2019-06-10 , Li, Mahei , Peters, Christoph

Due to the advent of digitization, service innovation has become even more important for both business and service research alike. Current service systems engineering approaches have employed a recombinant perspective that follows innovation mechanisms to leverage existing company resources for new service innovations. Employing these innovation mechanisms is still challenging, since there is little support on how to structure and identify these mechanisms. We propose a model-based service system engineering approach to structure existing resources into one formal model, enabling the formalization of service innovation mechanisms. The formalized service innovation mechanisms allow for a graphical illustration and enable future research to apply functions to analyze how innovation impacts entire or specific parts of service systems. Furthermore, the mathematical model enables an object-oriented value-driven perspective on service systems and is basis for graphical software tools. We contribute to literature by formalizing service innovations and its mechanisms in the context of service systems and by combining concepts of service innovation and service systems engineering. We do so by a) formalizing service innovation mechanisms and b) demonstrating the application of formal service innovations along one specific software implementation case. For practice, the service system model can with simulating the effects of service innovations.

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Künstliche Intelligenz und menschliche Kompetenz zur Automatisierung und Personalisierung von Dienstleistungen am Beispiel des Support

2020 , Li, Mahei , Bronner, Esther , Peters, Christoph , Leimeister, Jan Marco , Bruhn, Manfred , Hadwich, Karsten

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Einführung von Crowd-Based Support Dienstleistungen zur Verbesserung der Softwareeinführung

2018 , Li, Mahei Manhai , Billert, Matthias Simon , Dellermann, Dominik , Peters, Christoph , Leimeister, Jan Marco , Meyer, Kyrill , Klingner, Stephan , Zinke, Christian

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