The digital talent trap of the SME sector: a make-or-buy solution approach
The digital talent trap of the SME sector: a make-or-buy solution approach

Summary/Abstract: The purpose of this study is to answer the question of what options companies have when competing for digital talent. SMEs in particular, with their limited resources and significant backlog in terms of the digitalisation of corporate processes, find themselves in a digital talent trap which threatens to grow ever larger as digitalisation progresses. In other words, the digital talent gap could become a digital talent trap for SMEs in terms of digitalisation. The aim of this work is to derive from this insight a pragmatic and cost-effective solution which, in addition to the highly competitive “buy” option – i.e., classic recruitment via the labour market – offers a “make” option for SMEs that focuses more on their own employee resources. The research approach was based on two steps. In the first step, various literature approaches for recording and measuring digital skills were analysed in order to summarise them and derive potential digital talent profiles. The second step was to search for suitable software packages that are able to detect these profiles or skills in company documents. With these data sets, an analysis was carried out in the third step using AI algorithms – created in Python – in order to identify potential digital talents. An anonymised personal data set was used to test the above decision-making process. The findings show that SMEs could already access powerful, user-friendly and low-cost digital AI tools when searching for digital talent – especially in the search for digital talents within the company. The assumption that only larger companies with the corresponding financial resources can afford this option cannot be confirmed. The originality of the work lies in the finding that suitable AI tools exist for SMEs to search for digital talent, but that these are not currently being used extensively. Barriers such as prohibitively high costs or the low user-friendliness of AI tools could not be confirmed within the scope of this study.

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