Description
The revolutionary potential of machine learning in enhancing collaboration within business and industry is investigated with an emphasis on both the benefits and challenges in this study. Maintaining competitiveness and promoting innovation in firms has become more dependent on successful collaboration in contexts that are becoming more complicated and dynamic. Hence, this study explores the strategic applications of machine learning to enhance collaboration and help businesses better adjust to shifting market dynamics and operational requirements. The methodology includes a thorough analysis of the most recent developments in machine learning technologies and how they might be used in real-world corporate settings for supply chain, product, and customer relationship management, among other areas. Increased prediction accuracy, improved decision-making, efficient resource allocation, and support for more flexible and responsive business strategies are just a few of the significant opportunities that machine learning brings. Additionally, the research highlights the dynamic nature of machine learning algorithms—that is, their ability to continuously learn and adapt—which makes them extremely helpful in unstable and unpredictable environments. Nevertheless, significant challenges in using machine learning for cooperative goals are also addressed. Among these difficulties are worries about privacy and data security. The findings suggest that machine learning has great potential to transform corporate and industrial cooperation. However, companies need to deal with these challenges to reap the benefits of machine learning. By properly negotiating these obstacles, businesses may better utilize machine learning-driven collaboration, which is in line with the overarching objective of utilizing cooperation as a major force behind societal modernization and growth.
Keywords: Machine learning, collaboration, decision-making, resource allocation, business strategies
| Full Name (In Capital Letters) | Rza Bediyev |
|---|---|
| rzabediyev@gmail.com | |
| Kurum / University / Affiliated Institution | Çukurova University |
| Akademik Ünvan/ Academic Title | Other |
| Country | Türkiye |
| Telefon / Phone Number | +905373864931 |
| Katılım Tipi /Participation Types | Çevrimiçi /Online |
| Sunum Dili /What Will Be The Presentation Language? | English |
| Where Do You Want to Publish the Full Text? | I will only send summary text |
