A vállalatok működése szempontjából a döntéstámogató funkció folyamatos fejlesztése, monitorozása kiemelt jelentőségű, hiszen az vezetést támogató eszközként segíti a menedzsmentfeladatok ellátását. Az üzleti intelligencia (business intelligence, BI) olyan infokommunikációs megoldás, mely a vállalati rendszerekből különböző adatforrásokat felhasználva képes az adatok összekapcsolására és elemzésére. A napi üzletmenet gördülékeny biztosítása céljából alkalmazott tranzakciós rendszerektől eltérően a BI-eszközök beszámolás orientáltak, a fókusz a döntéstámogatásra helyeződik. A kutatás a fogalmak tisztázását követően képet ad a legfrissebb üzleti intelligencia trendekről. A tanulmány szakmai mélyinterjúk elemzésén keresztül betekintést nyújt az üzleti intelligencia megoldások világába. A kutatás eredményeként az olvasó képet kaphat a BI bevezetésétől várt eredményekről, az implementáció és a hosszú távú működtetés sikerkritériumait illetően. --- Gergely GORCSI - Gergo BARTA - Zsuzsanna SZELES Success criteria for the application of business intelligence solutions In the running of any given company, continuous improvement and monitoring of decision support functions is crucial for such activities to serve as tools to support management tasks. Business Intelligence (BI) is an infocommunication tool that connects and analyses data from corporate systems using varied data sources. Unlike transactional systems that are used to ensure the sound operation of day-to-day business, BI tools are report-oriented, and focus on decision support. Reviewing related concepts, this research gives an overview of the latest business intelligence trends. Our study sets out to provide an insight into the world of business intelligence solutions by analysing professional, in-depth interviews. Through our research, one will become familiar with the results expected from the introduction of BI, in relation to the success criteria of its implementation and long-term operation.

The number of projects and the amount of investment into artificial intelligence (AI) based business process automation is increasing that is also due to research advancements in corresponding fields. To utilise its true power, business organisations shall identify and treat risks arising from AI, that must be reduced to an acceptable level to maintain fraud-free business operation in alignment with external legislative requirements. If risks are not assessed, then AI might cause greater headache resulting in expensive implementation without business benefit. The objective of the paper is to analyse the nature of risk elements that AI can bring to the life of corporations and the countermeasures that shall be implemented by analysing general IT risk assessment processes and the stages of intelligent system development. The article also examines frameworks for AI risk management approaching risks associated with intelligent decision making by providing guidelines of required business processes to be implemented.

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Purpose – the number of projects and amount of investment into Artificial Intelligence (AI) based business process au-tomation is increasing. To utilize the power of AI, business organizations shall achieve a certain level of digital maturity that enables of handling the risks arising from AI. AI brings new risk factors to their life that has to be reduced to an ac-ceptable level. If risk mitigation procedures are not in place, then AI might cause a greater headache than a market ad-vantage resulting in expensive implementation with no business benefit. Research methodology – the objective is to analyze what risk factors can AI bring with itself to the life of corporations by analyzing general IT risk assessment processes and the stages of AI development. Findings – We observed that current IT risk assessment methodologies don’t detail possible risk scenarios regarding in-telligent applications and don’t extend their threat catalogs to help organizations consider threats related to AI. Research limitations – the research work details possible risks for general AI development that might differ across in-dustries, business cases, specific algorithms etc. Practical implications – the research contributes to organizations to assess possible risks arising from the use of AI. Originality/Value – since AI based automation is the result of recent research work, analyzing risk management aspects of its use can be considered as a new field for further research.