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Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects...
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Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects in Business and Science

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 11010

Special Issue Editor

Dr. Johannes Winter

E-Mail Website
Guest Editor
Head of Technology Department, National Academy of Science and Engineering, Munich, Germany
Interests: artificial intelligence; data science; industrial AI; industry 4.0; business models; SME
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I cordially invite you to submit contributions to the Special Issue on `Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects in Business and Science´.

About the Special Issue: The digital age is changing production processes and value chains worldwide, which thus poses major challenges for companies. In a globally networked economy, those who do not sufficiently tap the potential of data-driven process optimization and digital business model innovation could potentially lose competitiveness in the medium term. This is an important lesson learned from the Business-to-Consumer platform economy of recent years. Using digital technologies for collecting and analysing data allows both the improvement and flexible customization of services and products, offered in a wide variety of sectors and industries. This, however, typically requires an extensive collaboration of different actors to enable access to data and technologies. In addition, the comprehensive access to and exchange of data is essential as the basis and training material for artificial intelligence and self-learning systems.  Small and medium-sized enterprises are often unaware of what data treasures they have at their disposal in their operations, and how these can be harnessed using methods of artificial intelligence (AI) and machine learning.

The aim of the Special Issue `Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects in Business and Science´ is to intensify the scientific and practical debate on the introduction of AI in business.  Accordingly, concrete application scenarios, use cases and best practices from research and various industries will be presented and examined regarding their concrete benefits for business and/or society.  In addition, this Special Issue aims to identify limits to the use of AI in business and to identify concrete recommendations for business and policy as well as research needs for the use of AI.

Dr. Johannes Winter
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • data science
  • data analytics
  • AI applications/use cases/best practices
  • business process management
  • business process reengineering
  • business model innovation
  • research needs
  • policy recommendations

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Published Papers (3 papers)

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Research

12 pages, 759 KiB  
Article
Is It Worth the Effort? Considerations on Text Mining in AI-Based Corporate Failure Prediction
by Tobias Nießner, Stefan Nießner and Matthias Schumann
Information 2023, 14(4), 215; https://doi.org/10.3390/info14040215 - 1 Apr 2023
Cited by 1 | Viewed by 1601
Abstract
How can useful information extracted from unstructured data be used to contribute to a better prediction of corporate failure or bankruptcy? In this research, we examine a data set of 2,163,147 financial statements of German companies that are triple classified, i.e., solvent, financially [...] Read more.
How can useful information extracted from unstructured data be used to contribute to a better prediction of corporate failure or bankruptcy? In this research, we examine a data set of 2,163,147 financial statements of German companies that are triple classified, i.e., solvent, financially distressed, and bankrupt. By classifying text features in terms of granularity and linguistic level of analysis, we show results for the potentials and limitations of approaches developed in this way. This study gives a first approach to evaluate and classify the likelihood of success of text mining approaches for extracting features that enhance the training database of AI-based solutions and improve corporate failure prediction models developed in this way. Our results are an indication that the adaptation of additional information sources for the financial evaluation of companies is indeed worthwhile, but approaches adapted to the context should be used instead of unspecific general text mining approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects in Business and Science)
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12 pages, 679 KiB  
Article
The Deinstitutionalization of Business Support Functions through Artificial Intelligence
by Jan Christian Bauer and Michael Wolff
Information 2022, 13(8), 352; https://doi.org/10.3390/info13080352 - 22 Jul 2022
Viewed by 3013
Abstract
Technological advances in the field of artificial intelligence offer enormous potential for organizations. In recent years, organizations have leveraged this potential by establishing new business models or adjusting their primary activities. In the meantime, however, the potential for greater efficiency and effectiveness in [...] Read more.
Technological advances in the field of artificial intelligence offer enormous potential for organizations. In recent years, organizations have leveraged this potential by establishing new business models or adjusting their primary activities. In the meantime, however, the potential for greater efficiency and effectiveness in support functions such as human resource management (HRM), supply chain management (SCM), or financial management (FM) through these technological advances is also increasingly being recognized. We synthesize the current state of research on AI regarding the potentials and diffusion within these support functions. Building upon this, we assess the deinstitutionalization power of AI for altering organizational processes within business support functions and derive implications to harness the full potential of AI across organizations. Full article
(This article belongs to the Special Issue Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects in Business and Science)
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16 pages, 266 KiB  
Article
Adopting AI in the Context of Knowledge Work: Empirical Insights from German Organizations
by Georg von Richthofen, Shirley Ogolla and Hendrik Send
Information 2022, 13(4), 199; https://doi.org/10.3390/info13040199 - 15 Apr 2022
Cited by 6 | Viewed by 4794
Abstract
Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI [...] Read more.
Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI in the context of knowledge work. Drawing on case study research in eight German organizations that have either implemented AI or are in the process of developing AI systems, we identify three pervasive changes that knowledge workers perceive: a shift from manual labor and repetitive tasks to tasks that involve reasoning and empathy, an emergence of new tasks and roles, and an emergence of new skill requirements. In addition, we identify three factors that are conducive to the development of AI systems in the context of knowledge work: leadership support, participative change management, and effective integration of domain knowledge. Theoretical and managerial implications are discussed. Full article
(This article belongs to the Special Issue Artificial Intelligence in Practice: Recent Achievements, Limitations, and Future Prospects in Business and Science)
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