How Top-Down AI Introduction Leads to Incremental Business Improvement

dc.contributor.authorBrunnbauer, Matthias
dc.date.accessioned2022-12-27T19:21:00Z
dc.date.available2022-12-27T19:21:00Z
dc.date.issued2023-01-03
dc.description.abstractArtificial intelligence offers the opportunity for radical improvements such as completely new business solutions. It also enables the improvement of existing business. This paper reports on a case study that tests two strategies to identify AI use cases: top-down and bottom-up. The use cases are differentiated according to whether they promise incremental or radical business improvements and whether they are realizable in the short or long term. The top-down strategy identifies use cases that promise short-term but incremental improvements. They relate to existing business, but no disruptive ideas emerge. The bottom-up strategy allows for a broader understanding of AI’s potentials to improve business. Completely new and disruptive ideas emerge, but require huge upfront effort. Organizations best start with AI pilot projects that are feasible in the short term: Either by first applying a bottom-up strategy that is supplemented and evaluated with the top-down strategy, or top-down only.
dc.format.extent10
dc.identifier.doihttps://doi.org/10.24251/HICSS.2023.745
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other39e7c796-97e6-4653-9189-e9c945f8a9f6
dc.identifier.urihttps://hdl.handle.net/10125/103379
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPractice-based IS Research
dc.subjectartificial intelligence
dc.subjectbottom-up
dc.subjectbusiness improvement
dc.subjecttop-down
dc.subjectuse case identification
dc.titleHow Top-Down AI Introduction Leads to Incremental Business Improvement
dc.type.dcmitext
prism.startingpage6149

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