Anna K. Polzer, Johannes P. Zeiringer, Stefan Thalmann
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引用次数: 0
摘要
虽然近年来人工智能和 ML 的使用率不断上升,但中小企业在采用人工智能和 ML 时仍面临各种挑战。与大型企业相比,中小企业在采用人工智能方面举步维艰,因为确定合适的人工智能用例需要大量的专业技术知识。与此同时,像 AutoML 这样的生产力工具却能让非专业人员轻松获得人工智能能力。这项正在进行的研究旨在探讨如何利用 AutoML 工具来促进中小型企业采用人工智能。在一个有 11 位中小企业代表参加的焦点小组中,我们详细确定并讨论了 AutoML 的潜在用例。结果显示,潜在用例的确定很少关注现有可用数据,而是重复大型企业的已知用例和成功案例。我们认为,向以数据为中心的方法进行范式转变将有利于为中小型企业穷尽 AutoML 的功能。
AutoML as Facilitator of AI Adoption in SMEs: An Analysis of AutoML Use Cases
While the uptake of AI and ML has been rising in recent years, SMEs still face various adoption challenges. In contrast to large enterprises, SMEs struggle to adopt AI as already the identification of suitable AI use cases requires substantial technical expertise. At the same time, productivity tools like AutoML promise easy access to AI capabilities to non-experts. This research-in-progress aims to investigate how AutoML tools can be utilised to facilitate the adoption of AI in SMEs. In a focus group with 11 representatives from SMEs, we identified and discussed potential AutoML use cases in detail. Results show that the identification of potential use cases rarely focused on existing and available data but rather repeated known use cases and success stories from large enterprises. We argue that a paradigm shift towards a data-centric approach would be beneficial to exhaust the capabilities of AutoML for SMEs.