Benjamin van Giffen

Title

Prof. Dr.

Last Name

van Giffen

First name

Benjamin

benjamin.vangiffen@unisg.ch

Phone

+41 71 224 3635

Gerade angezeigt 1 - 10 von 27

third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
How Audi Scales Artificial Intelligence in Manufacturing
(2024)
André Sagodi
;
Benjamin van Giffen
;
Johannes Schniertshauer
;
Klemens Niehues
;
Jan Vom Brocke
For organizations to realize maximum value from artificial intelligence (AI), they need the capability to scale it and must consider scaling throughout all stages of an AI innovation project. But AI scaling presents significant challenges, especially for manufacturing companies. We describe how Audi, a leading automotive manufacturer, scaled its crack detection AI solution and unlocked long-term business value in manufacturing. Based on lessons learned at Audi, we provide recommendations and actions for CIOs and senior leaders who seek to capture value through scaling AI solutions.
cris-layout.advanced-attachment.dc.type:journal article
Journal:MIS Quarterly Executive
Volume:23
Issue:2
URL:https://aisel.aisnet.org/misqe/vol23/iss2/5/
DOI:10.17705/2msqe.00094
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118930
metric-badges.scopusCitation 9
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
How Boards of Directors Govern Artificial Intelligence
(2023)
Benjamin van Giffen
;
Helmuth Ludwig
Artificial intelligence is top of mind, even for nontechnical business executives and board members. However, the majority of boards struggle to understand the implications of AI for their businesses and their role in governing it. We describe how some boards are addressing AI and identify four groups of board-level AI governance issues. We provide examples of effective board-level AI governance practices for each group of issues and make recommendations for establishing board-level AI governance.
cris-layout.advanced-attachment.dc.type:journal article
Journal:MIS Quarterly Executive
Volume:22
Issue:4
URL:https://aisel.aisnet.org/misqe/vol22/iss4/7/
DOI:10.17705/2msqe.00085
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118960
metric-badges.scopusCitation 19
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
How Siemens Democratized Artificial Intelligence
(2023)
Benjamin van Giffen
;
Ludwig, Helmuth
Many firms aspire to generate business value with artificial intelligence (AI) but struggle to move beyond pilots and prototypes. Based on an in-depth case study, we describe how Siemens has leveraged AI democratization to identify, realize and scale AI use cases by integrating the unique skills of domain experts, data scientists and IT professionals. From the lessons learned at Siemens, we provide recommendations for building this organizational capability and effectively addressing the challenges of adopting the latest AI technologies.
cris-layout.advanced-attachment.dc.type:journal article
Journal:MIS Quarterly Executive
Volume:22
Issue:1
URL:https://aisel.aisnet.org/misqe/vol22/iss1/3
DOI:10.17705/2msqe.00072
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118961
metric-badges.scopusCitation 36
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
Engineering AI-Enabled Computer Vision Systems: Lessons From Manufacturing
(2022)
André Sagodi
;
Johannes Schniertshauer
;
Benjamin van Giffen
This article shares our results on challenges in engineering artificial intelligence (AI)-enabled computer vision systems for manufacturing and highlights critical success factors that have proven their worth. We provide AI engineers and development teams with timely and engaging inputs from the field.
cris-layout.advanced-attachment.dc.type:journal article
Journal:IEEE Software
Volume:39
Issue:6
URL:https://ieeexplore.ieee.org/document/9830692
DOI:10.1109/MS.2022.3189904
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118927
metric-badges.scopusCitation 5
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
Digitale Plattformen in der Praxis – Einsatz- und Entwicklungsmodelle
(Springer Fachmedien, 2022-08)
Holler, Manuel
;
Dremel, Christian
;
Hehn, Jennifer
;
van Giffen, Benjamin
;
Galeno, Gianluca
cris-layout.advanced-attachment.dc.type:journal article
Journal:HMD Praxis der Wirtschaftsinformatik
DOI:10.1365/s40702-022-00899-0
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108434
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods
(2022)
van Giffen, Benjamin
;
Herhausen, Dennis
;
Fahse, Tobias
Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses. This paper offers a systematic, interdisciplinary literature review of machine learning biases as well as methods to avoid and mitigate these biases. We identified eight distinct machine learning biases, summarized these biases in the cross-industry standard process for data mining to account for all phases of machine learning projects, and outline twenty-four mitigation methods. We further contextualize these biases in a real-world case study and illustrate adequate mitigation strategies. These insights synthesize the literature on machine learning biases in a concise manner and point to the importance of human judgment for machine learning algorithms.
cris-layout.advanced-attachment.dc.type:journal article
Journal:Journal of Business Research
Volume:Vol. 144
URL:https://www.sciencedirect.com/science/article/pii/S0148296322000881?via%3Dihub
DOI:10.1016/j.jbusres.2022.01.076
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109604
metric-badges.scopusCitation 207
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
Management von Künstlicher Intelligenz in Unternehmen
(Springer Verlag, 2020)
van Giffen, Benjamin
;
Borth, Damian
;
Brenner, Walter
cris-layout.advanced-attachment.dc.type:journal article
Journal:HMD Praxis der Wirtschaftsinformatik
Volume:57
Issue:1
URL:https://link.springer.com/article/10.1365/s40702-020-00584-0
DOI:10.1365/s40702-020-00584-0
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/113201
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
Was Unternehmen von der Videospieleindustrie für die Gestaltung der Digital Customer Experience lernen können
(Springer Fachmedien, 2017-08)
Spottke, Benjamin
cris-layout.advanced-attachment.dc.type:journal article
Journal:HMD Praxis der Wirtschaftsinformatik
Volume:54
Issue:5
DOI:10.1365/s40702-017-0344-z
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/102145
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
The Anatomy Of A Design Challenge For Initiating AI Innovation
(2026-06-15)
Franziska Anna Röckel
;
Benjamin van Giffen
;
Andreas Janson
;
Jennifer Hehn
Artificial Intelligence (AI) unlocks value-creation opportunities, but its complexity makes it a challenging design material, demanding well-prepared conditions for successful innovation. One reason organisations struggle to materialize AI's potential is initiating AI innovation, which relies on interdisciplinary teams, internal data integration, incremental development, and context information. The Design Challenge, i.e., problem formulation, serves as key artefact guiding AI innovation, but its conceptualisation remains undertheorized leading to a lack of theoretical foundation and practical guidance. Our study conceptualises the Design Challenge as a boundary object enabling interdisciplinary collaboration and serving as a reference point. We performed a qualitative cross-cases analysis of 42 design challenges to surface seven key elements being verb, object of innovation, internal data, technology, target group, context and potential, generating the Design Challenge's anatomy. This advances theoretical understanding of AI innovation and provides a conceptual schema to craft design challenges enhancing the likelihood of impactful AI innovations.
cris-layout.advanced-attachment.dc.type:conference paper
Journal:European Conference on Information Systems (ECIS)
URI:https://alexandria.unisg.ch/handle/20.500.14171/130222
third-party-metrics-blocked
third-party-metrics-cookies.consent-settings
How Organizations Design Portfolio Management to Govern AI: A Taxonomy Approach
(2025)
Simon Gabriel Sturm
;
Benjamin van Giffen
Advances in artificial intelligence (AI) create game-changing opportunities and new risks for organizations. Increasing competition and emerging regulation pressure them to govern AI for strategic use. Portfolio management has been identified as a key mechanism. However, AI is different from technologies for which portfolio management has proven effective. How organizations design portfolio management to govern AI is not well understood. Therefore, we conceptualized key design decisions in AI portfolio management (AIPM) by developing a multi-layer taxonomy. We developed our taxonomy based on five exploratory case studies. We evaluated it through expert interviews and a practitioner intervention. Our taxonomy shows that many IT portfolio management characteristics also apply to AIPM. However, AI introduces complexities that require novel design decisions. Our taxonomy captures design knowledge that enables future causal / predictive theorizing on AIPM. Practitioners can use our taxonomy as a tool to establish and improve AIPM practices.
cris-layout.advanced-attachment.dc.type:conference paper
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122738

Benjamin van Giffen

Filter

Einstellungen

Sortieren nach

Ergebnisse pro Seite