Latest since ChatGPT attracted public interest in innovative technologies, existing and future AI and ML application examples have been one of the most discussed topics. The talent attraction sector is no exception. In a high-transaction business, one quickly arrives at the question how far technology might displace and replace the human factor from talent acquisition processes. Are the first contacts so crucial to success really the right moment for the use of impersonal technology?
There is no denying that AI and ML technologies are here to stay, and they are already part of the transformation efforts of many, primarily larger, companies. Examples of use cases already in common use include the following:
Improved screening of resumes
AI / ML are not only faster and at least as thorough as the human colleague in processing resumes and documents, they also do all this largely without bias. It remains to be proven whether the long-term success in talent attraction and selection using AI and ML is also significantly demonstrable.
At this point, however, I would critically question the extent to which ML-driven algorithms do not sooner or later “learn” hiring manager decision-making patterns which then become part of the evaluation again through the back door. Or, in the worst case, such algorithms were trained on data that was biased or incomplete.
Individualized approach to candidates
The chances of successfully attracting talent are undoubtedly greater the more comfortable the candidate feels when contacted. This, however, is neither economical nor an attractive task for anyone in talent acquisition at this early phase or while there are no openings available for a certain period.
AI and ML technologies with no doubt enable large enterprises to make more data-driven and strategic decisions when it comes to talent acquisition which in return helps them regarding more efficient and effective recruitment processes, and a more diverse and inclusive workforce. However, it is important to also understand that AI and ML algorithms can confuse the human user if, for example, it is no longer possible for the latter to identify potential biases or errors due to a lack of transparency and insight into the decision-making criteria. Furthermore, each algorithm can only be as accurate as the data source is, that was used to train it.
It is therefore much less a question of a displacement process than primarily of how these innovations can meaningfully support a company’s goals in the search for candidates and in personnel management. I therefore believe that the concern that AI and ML will rationalize away jobs in the different areas of talent attraction and personnel management is unfounded.
Over-reliance on technology, especially related to people-centric tasks, will sooner or later result in a loss of personal connection which will subsequently negatively impact the employer brand and work negatively against all the well-intended efforts to modernize and optimize the processes. If diverse data is used to train an algorithm, regular monitoring of results for bias is in place and human oversight is ensured throughout the entire process, the opportunities to find better pre-selected candidates in a shorter time and on better terms from the company’s point of view will always outweigh the risks.