Artificial intelligence (AI) is rapidly changing the business landscape, and Switzerland, with its strong foundation in innovation and technology, is poised to be a key player in this transformation. However, to truly harness the power of AI, Swiss businesses and research institutions need to focus on key research areas that will shape the future of AI adoption.

This blog post delves into the critical research directions for AI in business, highlighting what this means for Switzerland specifically.

Key Research Areas for Switzerland to Focus On:

* Strategic Integration of AI:

* Swiss Context: Research should explore how Swiss businesses, particularly SMEs, can effectively integrate AI into their operations, considering the unique characteristics of the Swiss economic landscape. This includes examining how AI can be used to enhance productivity in sectors like watchmaking, pharmaceuticals, and finance, where Switzerland holds a strong global position.

* Focus: Develop frameworks and best practices for aligning AI tools with business strategies and existing IT infrastructure in Swiss organizations.

* AI and Innovation Management:

* Swiss Context: Investigate how AI can be used to foster innovation within Switzerland's renowned research institutions and companies. This includes exploring the use of AI in drug discovery, materials science, and developing new financial products.

* Focus: Analyze how AI impacts the innovation process, from ideation to commercialization, and identify the optimal balance between human and AI involvement in these processes.

* AI in Marketing:

* Swiss Context: Research the application of AI in understanding the multilingual and multicultural consumer base in Switzerland. Explore how AI can personalize marketing campaigns for different language regions and cultural preferences.

* Focus: Conduct systematic reviews to identify the most effective AI solutions for specific marketing functions within Swiss businesses, considering data privacy regulations.

* AI in Business-to-Business (B2B) Marketing:

* Swiss Context: Analyze how AI can be used to improve efficiency and effectiveness in B2B marketing for Swiss companies operating in global markets. This includes exploring the use of AI in supply chain optimization, customer relationship management, and international business development.

* Focus: Categorize AI applications in B2B marketing and identify specific trends and future needs for Swiss businesses.

* Societal and Organizational Impact of AI:

* Swiss Context: Given Switzerland's strong emphasis on ethical considerations and social responsibility, research should focus on the ethical implications of AI adoption, including bias detection, fairness, and transparency.

* Focus: Develop guidelines and frameworks for the responsible use of AI in Swiss organizations, considering the legal implications and the integration of AI with other technologies like IoT.

* AI Adoption and Value Creation:

* Swiss Context: Identify the key factors driving and hindering AI adoption in Swiss businesses, considering the country's unique economic and cultural context.

* Focus: Develop strategies to overcome barriers to AI adoption and maximize the value creation potential of AI for Swiss organizations.

* AI and Digital Transformation:

* Swiss Context: Analyze the role of AI in accelerating digital transformation across various sectors in Switzerland, including healthcare, finance, and public administration.

* Focus: Assess the impact of AI on business processes, decision-making, and competitive advantage in the Swiss digital economy.

Conclusion:

By actively pursuing research in these key areas, Switzerland can position itself at the forefront of AI innovation and ensure that AI technologies are used responsibly and effectively to benefit businesses and society as a whole. This will require collaboration between academia, industry, and government to create a thriving AI ecosystem that fosters innovation, addresses ethical concerns, and drives economic growth.

Borges, A., Laurindo, F., Spínola, M., Gonçalves, R., & Mattos, C. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. Int. J. Inf. Manag., 57, 102225. https://doi.org/10.1016/J.IJINFOMGT.2020.102225.

Loureiro, S., Guerreiro, J., & Tussyadiah, I. (2020). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.11.001.

Kitsios, F., & Kamariotou, M. (2021). Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda. Sustainability. https://doi.org/10.3390/SU13042025.

Han, R., Lam, H., Zhan, Y., Wang, Y., Dwivedi, Y., & Tan, K. (2021). Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. Ind. Manag. Data Syst., 121, 2467-2497. https://doi.org/10.1108/imds-05-2021-0300.

Dwivedi, Y., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P., Janssen, M., Jones, P., Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Meunier-FitzHugh, K., Meunier-FitzHugh, L., Misra, S., Mogaji, E., Sharma, S., Singh, J., Raghavan, V., Raman, R., Rana, N., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., & Williams, M. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. https://doi.org/10.1016/J.IJINFOMGT.2019.08.002.

Enholm, I., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24, 1709-1734. https://doi.org/10.1007/s10796-021-10186-w.

Ruiz-Real, J., Uribe-Toril, J., Torres, J., & Pablo, J. (2020). ARTIFICIAL INTELLIGENCE IN BUSINESS AND ECONOMICS RESEARCH: TRENDS AND FUTURE. Journal of Business Economics and Management. https://doi.org/10.3846/jbem.2020.13641.

Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2020). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.10.044.

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120392.

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. Int. J. Inf. Manag. Data Insights, 1, 100002. https://doi.org/10.1016/J.JJIMEI.2020.100002.

Need support for your AI strategy? www.day1tech.com