@article{AxmannPujar2025, author = {Axmann, Bernhard and Pujar, Sanket}, title = {Artificial Intelligence in Knowledge Management: Overview and Selection of Software for Automotive Reporting}, volume = {19}, journal = {Tehnički glasnik}, number = {si1}, publisher = {University North}, address = {Koprivnica}, issn = {1848-5588}, doi = {https://doi.org/10.31803/tg-20250313154901}, pages = {130 -- 135}, year = {2025}, abstract = {Knowledge Management is essential for modern organizations, enabling the systematic capture, organization, and sharing of knowledge to enhance decision-making and innovation. Traditional Knowledge Management tools, focused on document storage and retrieval, struggle with unstructured data and collaboration, necessitating advanced technological solutions, particularly those incorporating Artificial Intelligence. - Artificial Intelligence-driven Knowledge Management systems revolutionize data handling through automation, and real-time insights. This is particularly valuable in data-intensive industries like automotive, finance, and healthcare. In the automotive sector, annual reports provide critical insights but are complex and time-consuming to analyze and are a complex example and therefore a good test case. Annual reports of 5 major automotive companies BMW, Volkswagen group, Toyota Motors, General Motors and Tesla were selected as the testing dataset. Artificial Intelligence tools, using natural language processing and machine learning, streamline data extraction. - Despite their benefits, organizations face challenges in selecting the right Artificial Intelligence-driven Knowledge Management software due to a lack of standardized evaluation frameworks. This research applies a systematic methodology for assessing such software, considering usability, adaptability, cost-effectiveness, and data privacy compliance, specifically tailored to automotive reporting and gives recommendation for software tools.}, language = {en} } @inproceedings{HohenadlAxmann2025, author = {Hohenadl, Tom and Axmann, Bernhard}, title = {The Future of RPA in the Context of Automation Platforms}, booktitle = {Business Process Management: Responsible BPM Forum, Process Technology Forum, Educators Forum, BPM 2025 RBPM, PT, and Educators Forum, Seville, Spain, August 31 - September 5, 2025, Proceedings}, editor = {ER, Mahendrawathi and Gal, Avigdor and Grisold, Thomas and Santoro, Flavia and Weske, Mathias and Dijkman, Remco M. and Karastoyanova, Dimka and Aysolmaz, Banu and Bandara, Wasana and Revoredo, Kate}, publisher = {Springer}, address = {Cham}, isbn = {978-3-032-02936-2}, doi = {doi.org/10.1007/978-3-032-02936-2_18}, pages = {247 -- 262}, year = {2025}, language = {en} } @article{AgostinelliAsatianiAxmannetal.2025, author = {Agostinelli, Simone and Asatiani, Aleksandre and Axmann, Bernhard and Hannibal, Glenda and Modlinski, Artur}, title = {Trust Calibration for RPA}, volume = {14}, journal = {Dagstuhl Reports}, number = {7}, publisher = {Schloss Dagstuhl}, address = {Dagstuhl}, issn = {2192-5283}, doi = {https://doi.org/10.4230/DagRep.14.7.52}, pages = {61 -- 64}, year = {2025}, abstract = {This working group focused on foundational issues of trust in RPA: How we can conceptualize trust between users and software robots and specify it in a way that, eventually, we can measure that trust empirically.}, language = {en} } @inproceedings{AxmannCvetićMacowskiDurskiSilvaetal.2025, author = {Axmann, Bernhard and Cvetić, Biljana and Macowski Durski Silva, Vanina and Trstenjak, Maja}, title = {AI Office and Automation Software: Cross Country Survey on Utilization in Logistics}, booktitle = {2025 15th International Conference on Advanced Computer Information Technologies: ACIT'2025, Conference Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-9544-9}, doi = {https://doi.org/10.1109/ACIT65614.2025.11185844}, pages = {261 -- 266}, year = {2025}, language = {en} }