How to evaluate HR tech with the impact of AI

Abstract

The purpose of the study was to uncover the various stakeholders, evaluation methods, and internal proce-dures for the strategic implementation of HR technology. This objective was accompanied by the increasing relevance of AI and its impact on organizational decision-making, and the goal was to find out the impact these new AI features have, requiring systems thinking and coordination for the dispersion of value creation across the organization. The study attempted to answer the questions on what is the background theory for evaluating HR tech solutions, and how AI impacts the evaluation of HR tech solutions. The idea for the study came from conducting data collection and discovering the complexity of organizing information for decision-making from a wide variety of HR tech solutions with different value-creation capabilities.

The research methods used were literature review and gathering qualitative data in data collection. Data analysis was not part of the research due to limitations associated with LLMs. However, the information that was gathered with the use of prompts, web scraping, and manual search was compiled and reviewed with comments on accuracy and recommendations for further use of LLMs in information retrieval. Data was collected on a range of HR tech with the assistance of an LLM to produce material for observation to compare the validity and the findings of the theory-based approach. The research revealed the extent of preparatory work that goes into adopting a new or replacement HR technology to fit the organization’s prerequisite for competitive advantage.

The results show that to benefit from the introduction of AI, new systems will produce the most value and require coordinating the change between each layer of the organization, to address the need for strategy, risk management, change management, and other composing elements of business administration. The inclusion of new HR technology is also a departmental process involving several stakeholders such as developers, managers, employees, and various internal and external experts. Coordinating the change requires leadership and access to resources such as ROI for decision-making. The evaluation criteria that were produced may be used in future studies and work-life processes. The theoretical framework can be expanded with further criteria and new focus on e.g., clientele needs. The tools that were introduced such as AI system discovery canvas, can help to identify value-creation opportunities within the organization. Subsequent research may benefit from imitating the use of LLMs for efficiencies in information retrieval.