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Unsere anwendungsorientierte Forschung deckt den wertorientierten Einsatz von Künstlicher Intelligenz in Organisationen, Design Thinking und Autonomes Fahren ab. Die drei Themenschwerpunkte bearbeiten wir sowohl in wissenschaftlichen Beiträgen als auch in praxisorientierten Projekten und Lehrveranstaltungen.

CIO2028

The Future of the Chief Information Officer in the Digital Financial Services Industry

Goal and Research Questions

Digitization, specifically digital products or services and new levels of automation, is increasingly becoming an important success factor for banks and insurance companies, i.e., the digital financial services industry. Traditionally, the Chief Information Officer (CIO) has been responsible for digitization, i.e., the use of information and communications technology (ICT). His position has been controversial in some companies for several years: On the one hand, more and more budget and responsibility for ICT is shifted to the business departments. On the other hand, there are new positions such as the Chief Digital Officer, who is responsible for digitization. In addition, the companies have created the function of a Chief Data Officer, who is responsible for maximizing the value of data. Based on this background, the CIO2028 research project aims to provide answers to the following research questions:
  1. What is the future role of the CIO in the Financial Services Industry (in competition with IT competence of the Chief Digital Officer and the Chief Data Officer or in the business)?
  2. What went well and what should be improved regarding managing digital transformation?
  3. What are the key competences of the CIO in corporate IT in the future?
  4. How will IT be managed and steered in an increasingly digitized Financial Services Industry?
  5. What is the most effective setup for digital transformation?
  6. What does the target operating model for the IT of the future look like?
The CIO2028 research project focuses on CIOs from Germany and Switzerland.

Composition of the research team

The research project team consists of a core team including Walter Brenner, Sudip Lahiri, Maria Crameri, Barbara Brenner, and Nikhil Manda (?). In addition to the core team, 10 to 15 CIOs and CDOs from banks and insurance companies are invited to participate in this research project. They contribute their knowledge, experience, and their view of the future to ensure that the results can be implemented and have an impact on practice. CIOs who wish to participate in the research project are asked to contribute as follows:
  • 2 conversations or interviews of 1 - 1.5 hours each (Challenges/Practices/Models), of which one interview of approximately 2 hours will be included in the book
  • Participation in the three workshops physically or virtually
  • Interview of approximately 2 hours as contribution to the book (dialogue)

Research method, documentation, and publication of results

The CIO2028 research project is conducted as applied research. The research process consists of several rounds of interviews with CIOs from companies in the banking and insurance industry and roundtables (workshops) in which the results of the interviews are discussed and consolidated. This may be accompanied by a survey of CIOs from the banking and insurance industry to broaden the knowledge and experience base for the project. The results of the project are documented or published
  • as presentations in the form of slide decks for the preparation of the roundtables
  • as a book authored by Brenner, Lahiri, and Crameri
  • as (short) articles for the dissemination of the contents of the research project
  • as posts and videos in social media

Structuring of the research project and major milestones

The research project will be structured along the three thematic blocks: Challenges, Good / Best Practices, Vision, and Future of the CIO. A roundtable / workshop will be dedicated to each of the three thematic blocks to discuss and consolidate the ideas of the participating CIOs:
  • Roundtable 1: Challenges of the CIO and Corporate IT / Banking of the Future
  • Roundtable 2: Good / Best Practices Managing IT in Digital Financial Services Industry
  • Roundtable 3: Model(s) of the CIO of the Future (Vision/Options)
In preparation for the roundtable / workshop, preparatory interviews will be conducted with all participating CIOs, which will be processed for analysis by the research team.

Benefits for the CIOs involved

By participating in the CIO2028 research project, the involved CIOs gain insight into how CIOs from other banks and insurance companies operate, which challenges they face, and how experts from the University of St.Gallen, HCLTech, and Mario Crameri see the future. The CIOs can openly exchange views with colleagues. In addition, publishing the interviews in a book distributed worldwide is a good opportunity to promote their IT and the respective company. At the end, each participant will receive a letter signed by Walter Brenner, Mario Crameri and Sudip Lahiri confirming their participation in the research project.

Rough schedule

  1. First round of interviews: January/ February 2023
  2. Roundtable (Workshop 1): March 2023
  3. Second round of interviews: June/ July 2023
  4. Roundtable (Workshop 2): August 2023 (after summer vacations)
  5. Third round of interviews: November/December 2023
  6. Roundtable (Workshop 3): January 2024
  7. Writing of book: January to April 2024
  8. Publication of book: 3rd/ 4th quarter 2024 (depending on publisher)

Costs for CIOs to participate in the research project

There are no costs for the CIOs participating in the CIO2028 project, except for expenses by attending the roundtables / workshops. The project is funded by courtesy of HCLTech.

Academic independence

The research team states that HCLTech will not influence the research methodology and the content of the results. The cooperation with HCLTech is listed on the corresponding website of the University of St.Gallen. All essential results of the research project will be publicly available in the published book.

Ansprechperson

Walter Brenner

Ansprechperson

Barbara Brenner

Sicherer Verkehr durch Digitalisierung

Es ist zu erwarten, dass die Nutzung der Potentiale der Informatik (Digitalisierung) als derzeit dominierende technische Entwicklung, in Zukunft einen wesentlichen Beitrag zur Erhöhung der Verkehrssicherheit leisten kann. Die Digitalisierung der Fahrzeuge, der Verkehrsinfrastruktur und ihre Vernetzung erzeugen ständig mehr und qualitativ bessere Daten, die Grundlage digitaler Interventionen sein können, um Fahrverhalten zu verbessern und damit die Verkehrssicherheit zu erhöhen. Digitale Interventionen können beispielsweise Informationen über das Fahrverhalten sein, aktive Eingriffe von aussen, um die Geschwindigkeit auf die erlaubte Höhe anzupassen oder die Information von Polizei oder dem Vorgesetzen bei der Übertretung von Verkehrsvorschriften.

Ansprechperson

Prof. Dr. Walter Brenner

Design Thinking

Wir untersuchen, wie die Entwicklung von KI-Systemen stärker an Bedürfnissen von Nutzern bzw. Endnutzern orientiert werden kann. KI-Technologie ist zwar ein wichtiger Teil der Entwicklung von KI-Systemen, aber menschliche Bedürfnisse, Emotionen und Einstellungen werden teils unzureichend berücksichtigt.

Weitere Informationen zum Thema finden Sie hier.

Ansprechperson

Prof. Dr. Benjamin van Giffen

Management of Artificial Intelligence

Die betriebliche Nutzung von KI erfordert eine nachhaltige und zielgerichtete Entwicklung von KI-Systemen. Wir entwickeln neue Managementmethoden, -prozesse und -strukturen, die es ermöglichen, KI sinnvoll und wertstiftend einzusetzen und somit langfristig Geschäftswert zu generieren. Wir untersuchen, wie die Entwicklung von KI-Systemen stärker an Bedürfnissen von Nutzern bzw. Endnutzern orientiert werden kann. KI-Technologie ist zwar ein wichtiger Teil der Entwicklung von KI-Systemen, aber menschliche Bedürfnisse, Emotionen und Einstellungen werden teils unzureichend berücksichtigt.

Weiter Informationen finden Sie hier

Ansprechperson

Prof. Dr. Benjamin van Giffen

Publikationen

get_appAnuschka Schmitt, Maximilian Walser, Tobias Benjamin Fahse
Konferenzbeitrag
Machine learning (ML)-based software’s deployment has raised serious concerns about its pervasive and harmful consequences for users, business, and society inflicted through bias. While approaches to address bias are increasingly recognized and developed, our understanding of debiasing remains nascent. Research has yet to provide a comprehensive coverage of this vast growing field, much of which is not embedded in theoretical understanding. Conceptualizing and structuring the nature, effect, and implementation of debiasing instruments could provide necessary guidance for practitioners investing in debiasing efforts. We develop a taxonomy that classifies debiasing instrument characteristics into seven key dimensions. We evaluate and refine our taxonomy through nine experts and apply our taxonomy to three actual debiasing instruments, drawing lessons for the design and choice of appropriate instruments. Bridging the gaps between our conceptual understanding of debiasing for ML-based software and its organizational implementation, we discuss contributions and future research.

Mehr
get_appTobias Fahse, Ivo Blohm, Benjamin van Giffen
Konferenzbeitrag
Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.

Mehr
get_appTobias Fahse
Konferenzbeitrag
disparity increases further thanks to cutting-edge machine learning (ML) algo-rithms. As business decisions based on sales forecasting are regarded as particu-larly important and a variety of other activities rely on them, accurate sales forecasting is critical to companies’ profitability. At the same time, being able to predict the next day’s sales more accurately can significantly reduce food waste and help fulfilling sustainability. Thus, sales forecasting is one of the pri-mary value propositions of artificial intelligence (AI). However, it is crucial for the acceptance and adoption of ML-based sales forecasting algorithms to per-form reliably during pandemics such as the covid-19 pandemic. Although gov-ernments’ containment measures highly impact the sales of a bakery’s products, no study has yet scrutinized incorporating the stringency of containment measures as an input variable for sales forecasting. Hence, this paper examines the performance of a ML sales forecasting system for baked goods in times of covid-19 and proposes incorporating a covid containment measurement strin-gency index as an additional input variable to increase forecast accuracy in times of pandemics. This way, prediction accuracy increases by 4.61% on aver-age. Consequently, a containment measures stringency variable should be used to increase accuracy in future pandemics. By simulating an upcoming pan-demic, it is further demonstrated how learnings from the covid-19 pandemic could be meaningfully transferred. For this study, real data is used: A Swiss bakery chain provides real sales data covering 5 years including 2 years of the covid-19 pandemic.

Mehr
get_appManuel Holler, Christian Dremel, Jennifer Hehn, Benjamin van Giffen, Gianluca Galeno
Wissenschaftlicher Artikel
Viele der wertvollsten Unternehmen der Welt betreiben ihr Geschäft auf Basis einer digitalen Plattform samt umgebendem Ökosystem. Während es in der Theorie zahlreiche Erklärungs-und Gestaltungsansätze für die erfolgreiche Umsetzung gibt, gelten diese wirtschaftlich attraktiven Geschäftsmodelle in der Praxis nach wie vor als herausfordernd. Auf der empirischen Grundlage von sieben Plattform-Innovationsprojekten und mit Methoden der Fallstudienforschung untersucht der vorliegende Artikel, welche Rolle digitale Plattformen in der Praxis spielen und wie diese Artefakte entwickelt werden können. Mit den Ergebnissen in Form von vier Einsatz- (Platform-as-a-Core, Platform-as-an-Evolution, Platform-as-an-Enabler, Platform-as-an-Add-On) und vier Entwicklungsmodellen (Methodic Problem Solvers, Methodic Strategists, Methodic Leaders, Ad-Hoc Developers) kann gefolgert werden: Digitale Plattformen können in der Praxis vielfältige Rollen einnehmen und deren Entwicklung kann mit unterschiedlicher Methodikintensität erfolgen. Für die Praxis profitieren Fach- und Führungskräfte von industrienahen Einblicken und abgeleiteten Handlungsempfehlungen. Für die Forschung wird der Wissensfundus im Bereich des Designs und der Entwicklung digitaler Plattformen erweitert.

Mehr
get_appTobias Fahse, Ivo Blohm, Richard Hruby, Benjamin van Giffen
Konferenzbeitrag
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.

Mehr
get_appWalter Brenner, Barbara Brenner
Wissenschaftlicher Artikel
Viele CIOs (Chief Information Officer) in Unternehmen haben in der Pandemie einen hervorragenden Job gemacht. In der Zeit nach der Pandemie gilt es, diesen „Rückenwind“ und die Position des CIO neu zu definieren. Vor diesem Hintergrund wurden fünf CIOs, Hanna Hennig von der Siemens AG, Andreas Maier von der AXA Schweiz, Michael Müller-Wünsch von OTTO, Rolf Olmesdahl, ehemals Raiffeisen Schweiz, Ursula Soritsch-Renier von Saint-Gobain Paris, und ein Executive-Search-Spezialist, Patrick Naef, Boyden AG, gefragt wie sie die Zukunft des CIO sehen. Alle sechs Persönlichkeiten, die an dem Dialog teilnahmen, sind übereinstimmend der Meinung, dass es auch noch 2030 CIOs geben wird und sie auf der einen Seite verstärkt Treiber der digitalen Transformation in ihrem Unternehmen sein sollten und sie auf der anderen Seite nach wie vor Verantwortung für das Funktionieren der digitalen Infrastruktur tragen.

Mehr
As expected, the digitalization of the economy and society has a major impact on the taxes of companies and on the tax department. There is no development in information and communication technology that can go unnoticed from the tax department's perspective. The digitalization of a business model can be of great importance, for example, when a digital platform is built. Putting all the facets described in this Chapter together, it is easy to see that the operational complexity for tax departments is already very high and will tend to increase further. International and digital business models meet new legal regulations and audit mechanics. At the same time, tax departments can deliver high quality financial data sets that can be used by business leaders to evaluate and steer new (digital) business models. The tax department should, therefore, increasingly be seen as a fundamental building block of a business . In interaction with other business units, external tax consultancies, technology providers and tax authorities, a new tax ecosystem is emerging, which requires new forms of internal and external collaboration. The increasing compliance requirements for companies, especially in the tax area, make it necessary to deal intensively with tax consequences before investing in a new business model. The digitalization of the tax department is progressing. New applications for the tax department are coming onto the market, new developments in information and communication technology are being used to digitalize processes in the tax department and to increase the level of automation. Data analytics; that is, the evaluation of large data sets using statistical methods and artificial intelligence, will become very important. It should always be clear that not only will companies digitalize their tax processes, but also the tax authorities will exploit all the potential of digitalization. A statement by former American President John F. Kennedy: ‘A rising tide lifts all boats’ can be applied to digitalization. Companies and government authorities will use and benefit from the potentials of digitalization.

Mehr
Effective Requirements Engineering is a crucial activity in software-intensive development projects. The human-centric working mode of Design Thinking is considered a powerful way to complement such activities when designing innovative systems. Research has already made great strides to illustrate the benefits of using Design Thinking for Requirements Engineering. However, it has remained mostly unclear how to actually realize a combination of both. In this chapter, we contribute an artifact-based model that integrates Design Thinking and Requirements Engineering for innovative software-intensive systems. Drawing from our research and project experiences, we suggest three strategies for tailoring and integrating Design Thinking and Requirements Engineering with complementary synergies.

Mehr
get_appBenjamin van Giffen, Dennis Herhausen, Tobias Fahse
Wissenschaftlicher Artikel
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.

Mehr
get_appBenjamin van Giffen, Jana Koehler, Walter Brenner, Can Adam Albayrak
Konferenzbeitrag
The algorithms of artificial intelligence are constantly being further developed and are being used in more and more products and applications in business and society. Numerous prototypes are being developed to open up the use of artificial intelligence in a wide variety of application areas. Nevertheless, only a few prototypes succeed in making the leap into productive applications that create sustainable business benefits. This paper series shows that processes and structures are needed for the management of artificial intelligence to ensure the sustainable success of AI systems.

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University of St. Gallen (HSG)
Institut für Wirtschaftsinformatik

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9000 St. Gallen

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