Transformative Technologies and Techniques in Innovation and Financial Management
Fernandes Marques da Fonte, Pedro (2023)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023092626382
https://urn.fi/URN:NBN:fi:amk-2023092626382
Tiivistelmä
Organizations in highly competitive industries must learn how to integrate Artificial Intelligence, automation techniques, and data analytics to enhance decision making processes. As result, these technologies have an extraordinary impact in all the operations in an organization. Specifically, in this thesis, the financial management practices are analyzed because both hinder and promote innovation at times.
Additionally, the empirical findings retrieved between the months of June/July in 2023, gathered the expertise of 5 professionals who had experience with computational tools. The research was conducted by taking a qualitative approach, using semi-structured interviews to delve into the impact of tools on decision making within SMEs companies, therefore exploring operational dynamics that smaller businesses can deploy. An important element that helped uncover patterns and important findings was the application of a thematic analysis to properly classify the found information. Also, ethical measures were taken to guarantee participant consent and confidentiality.
The findings from this study reveal that integrating the mentioned techniques can benefit organizations by improving efficiency and reducing costs. Data-driven financial tools offer advantages such as time-saving, informed decision-making, and increased productivity, although Big Data Analytics requires further research to understand its full impact. Skilled personnel and accurate data are crucial for successful tool implementation, but challenges include integrating the tools and overcoming resistance to change. Additionally, the study emphasizes future trends such as the importance of AI-based tools and the need for external data sources to thrive in challenging environments.
In conclusion, the thesis aims to study the integration of tools created with the help of AI, automation, and data analysis in SMEs, while discussing the manner in which they can affect the decision-making processes.
Additionally, the empirical findings retrieved between the months of June/July in 2023, gathered the expertise of 5 professionals who had experience with computational tools. The research was conducted by taking a qualitative approach, using semi-structured interviews to delve into the impact of tools on decision making within SMEs companies, therefore exploring operational dynamics that smaller businesses can deploy. An important element that helped uncover patterns and important findings was the application of a thematic analysis to properly classify the found information. Also, ethical measures were taken to guarantee participant consent and confidentiality.
The findings from this study reveal that integrating the mentioned techniques can benefit organizations by improving efficiency and reducing costs. Data-driven financial tools offer advantages such as time-saving, informed decision-making, and increased productivity, although Big Data Analytics requires further research to understand its full impact. Skilled personnel and accurate data are crucial for successful tool implementation, but challenges include integrating the tools and overcoming resistance to change. Additionally, the study emphasizes future trends such as the importance of AI-based tools and the need for external data sources to thrive in challenging environments.
In conclusion, the thesis aims to study the integration of tools created with the help of AI, automation, and data analysis in SMEs, while discussing the manner in which they can affect the decision-making processes.