Features of AI Solutions and their Use in AI Context Modeling
| dc.contributor.author | Rittelmeyer, Jack Daniel | |
| dc.contributor.author | Sandkuhl, Kurt | |
| dc.contributor.editor | Michael, Judith | |
| dc.contributor.editor | Pfeiffer | |
| dc.contributor.editor | Jérôme | |
| dc.contributor.editor | Wortmann, Andreas | |
| dc.date.accessioned | 2022-06-30T13:01:09Z | |
| dc.date.available | 2022-06-30T13:01:09Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Despite the implementation of many new artificial intelligence (AI)-based solutions in research and practice every year, companies still encounter problems while introducing AI solutions. One reason for that, from our own experience, are significant problems with understanding the concepts of AI. To cope with this problem, we aimed for developing a morphological box for AI solutions. The developed morphological box, its features and their values are based on four own industrial cases of AI solutions covering different application domains. We previously presented an enterprise architecture-based AI context model to help to better understand the context of an AI solution in a company and thereby minimize the risks of an implementation. We also analyzed that the morphological box supports the AI introduction process by improving and enhancing the three steps of the AI context model, which lead to more complete requirements for AI solutions. | en |
| dc.identifier.doi | 10.18420/modellierung2022ws-004 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/38800 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Modellierung 2022 Satellite Events | |
| dc.subject | Enterprise architecture | |
| dc.subject | AI context | |
| dc.subject | organizational AI solutions | |
| dc.subject | artificial intelligence | |
| dc.subject | morphological box | |
| dc.title | Features of AI Solutions and their Use in AI Context Modeling | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 29 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 18 | |
| gi.conference.date | 27.6. - 1.7.2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | MoKI - Modelle und KI |
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