Benjamin van Giffen
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Prof. Dr.
Last Name
van Giffen
First name
Benjamin
Email
benjamin.vangiffen@unisg.ch
Phone
+41 71 224 3635

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Publication

How Audi Scales Artificial Intelligence in Manufacturing

2023 , 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.

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Publication

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.

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Publication

Management von Künstlicher Intelligenz in Unternehmen

2020 , van Giffen, Benjamin , Borth, Damian , Brenner, Walter

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Publication

Explanation Interfaces for Sales Forecasting

2022-06-18 , Fahse, Tobias , Blohm, Ivo , Hruby, Richard , van Giffen, Benjamin

Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.

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Publication

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.

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Publication

Digitale Plattformen in der Praxis – Einsatz- und Entwicklungsmodelle

2022-08 , Holler, Manuel , Dremel, Christian , Hehn, Jennifer , van Giffen, Benjamin , Galeno, Gianluca

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Publication

Was Unternehmen von der Videospieleindustrie für die Gestaltung der Digital Customer Experience lernen können

2017-08 , Spottke, Benjamin

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Publication

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.

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Publication

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.

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Publication

Becoming Certain About the Uncertain: How AI Changes Proof-of-Concept Activities in Manufacturing - Insights from a Global Automotive Leader

2022 , André Sagodi , Christian Engel , Johannes Schniertshauer , Benjamin van Giffen

In this paper, we examine Proof-of-Concept activities in the presence of Artificial Intelligence (AI). To this end, we conducted an exploratory, revelatory case study at a leading automotive OEM that constantly explores new technologies to improve its manufacturing processes. We highlight how AI properties affect specifics in project execution and how they are addressed within the focal company. We carved out four key areas affecting underlying activities, i.e., data assessment, process alignment, value orientation, and AI empowerment. With our findings, we provide practical insights into AI-related challenges and corresponding pathways for action. Drawn upon, we develop novel, timely, and actionable recommendations for AI project leaders planning to implement this novel technology in manufacturing. This shall provide empirically grounded and conceptually sound guidance for researchers and practitioners alike, and ultimately foster the success of AI in manufacturing.

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