Rajko Ivanišević

Artificial intelligence is changing how organizations develop products and make decisions. This literature review shows that AI is now a key driver for innovation and efficiency in product management. Companies use AI tools to improve every stage of the product lifecycle, from idea creation to final delivery and ongoing updates. AI helps managers analyze large sets of data, predict market trends, and speed up decision processes. Instead of replacing people, AI often supports human work by allowing teams to solve complex problems and test new ideas faster. However, successful AI adoption depends on quality data, organizational readiness, and clear strategies. Main challenges include technical questions, data trust, ethical issues, and staff training. Best practices highlight the importance of combining human expertise with AI tools, using agile methods, and developing transparent, responsible systems. Research gaps remain in understanding AI's long-term effects, especially in non-digital sectors and on employee motivation. This review provides a structured overview of trends, challenges, and future research directions for AI in product management and decision-making.

Background: It is widely accepted that the digital transformation of business is increasingly attracting the attention of researchers from the academic circles as well as professionals from the business community. The main consequence of this lies in the daily development of new and improvement of existing digital technologies. The outcomes of such events on the market are reflected in all aspects of companies' operations. For this reason, they are constantly looking for various improvements to their business, which most often include the implementation of new technology. Mere implementation of a new technology without any other changes very often leads to failure. The core of this failure can be found and attributed to inadequately identified, analysed, documented and established business processes. Business process management (BPM) and redesign as its integral part are actually an indispensable segment of a successful process of digital business transformation. Therefore, the digital transformation of business should not be viewed exclusively from a technological perspective, but also from a process viewpoint. Purpose: With the aim of shedding additional light on the connection between business process management and digital business transformation, the paper aims to identify and explain the importance of business process redesign. Study design/methodology/approach: For the purposes of this paper, a systematic literature review was conducted. Findings/conclusions: The result of the conducted research indicates that a process approach to the digital transformation of business can contribute to significantly different, more successful results. Limitations/future research: Limitations refer to the number of databases searched during this systematic literature review. Subsequent research could include additional sources that would include additional works that can contribute to a better research result.

Background: Companies follow new trends to keep up with changes and find better ways of doing business. As one of the latest technologies, Robotic Process Automation (RPA) is in line with the automation trend that has become more prevalent in the 21st century. Since this technology is relatively new, many questions remain unanswered and have not been adequately addressed. Robotic process automation is under-researched in terms of risks that may arise during its development, implementation, or use. Purpose: The aim of this paper is to identify potential risks that may arise during the development, implementation, and use of the Robotic Process Automation. Research design/methodology/approach: Using Web of Science and SCOPUS index databases, a systematic review of the literature was conducted to answer the research question and achieve the defined goal. Results/conclusions: According to the results of the systematic literature review, Robotic Process Automation poses not only the typical risks associated with implementing any technology but also risks that are specific to this technology. Limitations/future research: Identified limitations refer to a small number of papers addressing the risks associated with robotic process automation. Furthermore, it is proposed that future research should include additional bases of studies.

As the use of artificial intelligence (AI) language models in business operations becomes an everyday reality, the management of company data resources, including the prevention of loss, raises critical questions about data privacy, security, and ethical considerations become increasingly import­ant. This study presents a comprehensive examination – literature review, as a foundation for further research on AI model usage and the effective man­agement of company data resources. The literature review surveys current re­search, revealing key themes and trends related to the topic, and offering in­sights into the reality of AI model use. This study aims to provide a holistic un­derstanding of the complex interaction between AI, data resource manage­ment, and corporate decision-making.

Owing to its high promises for improving learning support, teaching, and learning outcomes in higher education, learning analytics has captured much interest from both academics and practitioners over the last several years. Considering that it is rooted in several disciplines, researchers and practitioners have approached learning analytics from a range of perspectives. Although many studies concerning learning analytics have highlighted its great potential for improving learning practice, there is little evidence of successful transfer of the suggested potential into the practice of higher education happening. This clearly indicates a need for rethinking many aspects of learning analytics usage: first, the goals that can be achieved, but also the actions necessary to attain these goals. The aim of the descriptive research presented in this paper is to provide an updated and realistic view of the state of the art in learning analytics, its potential benefits, and tangible challenges that need to be overcome for a successful application of learning analytics as educational technology.