AI-enabled predictive analytics in SAP S/4HANA for real-time business intelligence : a systematic literature review
Oladejo, Adeola (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2026052818580
https://urn.fi/URN:NBN:fi:amk-2026052818580
Tiivistelmä
This study focused on the function of artificial intelligence-enabled predictive analytics in enterprise resource planning systems. The study is an academic inquiry into how contemporary digital technologies are changing corporate decision making. Hence, the thesis lays emphasis on enabling real-time business intelligence functions of ERP systems. The thesis’s primary goal was to investigate how artificial intelligence-driven predictive analytics may be incorporated into business systems to enhance operational performance, efficiency and decision-making. The study also sought to evaluate the advantages and difficulties of these technologies, especially in relation to financial and supply chain management.
The development of enterprise resource planning systems, the use of artificial intelligence in business settings, the function of machine learning in data analysis and the fundamentals of business intelligence and real-time analytics served as the foundation for the study's theoretical framework. The study also looked at how predictive analytics enhances risk management, forecasting, and overall business performance. To gather and examine relevant scholarly and industry sources, a methodical literature review approach was used. Reports, conference papers and reputable publications provided the data used for the analysis. An analysis of strengths, weaknesses, opportunities and threats was used to further examine the results from the chosen literature. In addition, comparative analysis was used to detect patterns, similarities, and differences amongst the research.
The study's findings demonstrate how artificial intelligence-enabled predictive analytics greatly improves enterprise systems' capabilities by facilitating quicker data processing, more precise forecasting, and better decision-making. Moreover, the study also noted problems like poor data quality, trouble integrating the system, high implementation costs, and the requirement for qualified staff. The thesis concludes that although predictive analytics offered by artificial intelligence has great potential to enhance business intelligence, its efficacy depends on appropriate implementation, trustworthy data, and organizational preparedness. The thesis emphasizes that it is crucial to match technology adoption with company strategy and that more research should be done to examine practical applications and new developments in this area.
The development of enterprise resource planning systems, the use of artificial intelligence in business settings, the function of machine learning in data analysis and the fundamentals of business intelligence and real-time analytics served as the foundation for the study's theoretical framework. The study also looked at how predictive analytics enhances risk management, forecasting, and overall business performance. To gather and examine relevant scholarly and industry sources, a methodical literature review approach was used. Reports, conference papers and reputable publications provided the data used for the analysis. An analysis of strengths, weaknesses, opportunities and threats was used to further examine the results from the chosen literature. In addition, comparative analysis was used to detect patterns, similarities, and differences amongst the research.
The study's findings demonstrate how artificial intelligence-enabled predictive analytics greatly improves enterprise systems' capabilities by facilitating quicker data processing, more precise forecasting, and better decision-making. Moreover, the study also noted problems like poor data quality, trouble integrating the system, high implementation costs, and the requirement for qualified staff. The thesis concludes that although predictive analytics offered by artificial intelligence has great potential to enhance business intelligence, its efficacy depends on appropriate implementation, trustworthy data, and organizational preparedness. The thesis emphasizes that it is crucial to match technology adoption with company strategy and that more research should be done to examine practical applications and new developments in this area.