This is a summary of the book titled “AI In business and economics” written by Michael Vogelsang and Isabel Lausberg and published by De Gruyter in 2024. The authors present a collection of papers from the 2023 Economic perspective of Artificial Intelligence conference. The studies examine both the present and the future of AI adoption and impact on society and industry. Early adopters especially the large companies have the chance to establish monopolies while medium-sized companies need more gradual and measurable progress on the roadmap to integrate AI. Technology teams are not strictly required for AI adoption and AI can’t replace every job. Media plays a crucial role in its perception while AI can step up to provide guidance in ambiguous forecasts.
Richard von Maydell of ETH Zürich and Christoph Menzel of the Federal Ministry for Economic Affairs and Climate Action, Berlin, discuss the rise of tech giants like Apple, Amazon, and Microsoft, noting that their reliance on information and communication technology (ICT) has led to increased market concentration. The growing use of AI in these sectors exacerbates the problem, as AI helps companies reduce costs and become more efficient, making it harder for new businesses to compete. This concentration of power can harm competition and push up prices, limiting consumer choices.
The book also explores the increasing role of intangible assets like software, data, and AI itself, which further contribute to market concentration. Dominant companies thrive while smaller competitors struggle, leading to an imbalance that hurts consumers by limiting choices and contributing to high prices. Governments are urged to update laws and policies to regulate competition in digital markets. The European Union's Digital Markets Act is one such example, though its effectiveness in handling AI's growing influence remains uncertain. Financial support for smaller businesses and encouraging data sharing could help create a more level playing field.
Medium-sized companies need a gradual, specific, and goal-oriented process for integrating AI. The KI-AGIL research project, led by Markus Feld, Wolfgang Arens-Fischer, and Marcel Schumacher of the University of Applied Sciences Osnabrück, aimed to help SMEs integrate AI into their operations. The project guided six SMEs through manageable phases called “sprints,” allowing them to build their AI systems gradually. Agile development techniques were used, focusing on flexibility and continuous improvement. This incremental approach helped businesses manage the complexity and reduce the risks of adopting AI.
Isabel Lausberg, Arne Eimuth, and Anne Stockem Novo of the Ruhr West University of Applied Sciences discuss the slow adoption of AI in management reporting. Despite AI's potential to enhance these processes, companies face hurdles like poor data integration and the continued use of traditional tools like Excel. Improving data management, building a strong AI infrastructure, and fostering a culture of AI acceptance within organizations are crucial steps. Top management support is essential to drive the necessary changes and allocate resources.
The WIRKsam project, a joint initiative by researchers from various universities, emphasizes the use of “plug and play” AI tools that require little to no coding experience. No-code and low-code approaches allow users to build applications without extensive programming knowledge. These tools make AI accessible to a wide range of users, enabling businesses to benefit from AI without relying solely on specialized tech teams.
Ed Dandalt of Wilfrid Laurier University argues that AI is unlikely to replace physicians' roles in health care due to the unique combination of clinical and management skills they bring. While AI can support doctors by automating administrative tasks, it cannot replace the essential components of empathy, active listening, and decision-making critical to patient care. Patients' preferences for human physicians and the high demand for medical professionals further support this view.
Simone Roth and Medina Klicic of Ruhr West University of Applied Sciences highlight the media's crucial role in shaping public perceptions of AI. Positive articles often emphasize AI's benefits, while negative coverage gains more attention, contributing to public concerns and skepticism. Addressing consumer-related issues like fairness and protection in the media could help mitigate fears and foster greater acceptance of AI technologies.
Finally, Katharina I. Köstner, Bàrbara Llacay, and David Alaminos of the Universitat de Barcelona discuss the promise of algorithms like Deep Autoregressive Recurrent Networks (DeepAR) in forecasting market volatility. AI models using DeepAR outperform traditional methods in terms of accuracy and error reduction, particularly during periods of high volatility. Incorporating more control variables could enhance the model's accuracy and versatility in real-world applications.
Overall, the book provides a comprehensive overview of AI's impact on business and economics, highlighting both opportunities and challenges. It emphasizes the need for gradual integration, regulatory updates, and the importance of human elements in AI adoption. The insights from various experts offer valuable guidance for businesses and policymakers navigating the complex landscape of AI.
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