AI-Driven Business Process Automation in International Headhunting: Implementation of No-Code/Low-Code Solutions for Enhanced Operational Efficiency

This thesis investigates the transformative impact of AI-driven business process automation on the international headhunting industry, a sector characterized by significant operational complexities, legal hurdles, and scalability challenges. The central problem addressed is the pervasive inefficiency stemming from manual, time-intensive, and fragmented recruitment workflows that increase costs and limit strategic agility. Adopting a Design Science Research (DSR) methodology, this project centers on the creation, implementation, and rigorous evaluation of a portfolio of innovative IT artifacts. The research demonstrates the powerful synergy between Artificial Intelligence (AI), No-Code/Low-Code (NC/LC) platforms, and Business Process Management (BPM) principles to engineer a holistic solution. The core of this work is a bespoke, AI-powered automation ecosystem built on a secure, on-premises infrastructure, featuring a suite of targeted solutions that automate critical processes such as intelligent CV screening, automated content generation, and end-to-end lead management. The implementation of these solutions yielded significant and quantifiable im provements in operational performance. Key results include a greater than 98% reduction in CV processing time, a 90% decrease in the manual effort required for content creation, and the successful processing of datasets exceeding one million records with minimal error. These technical achievements translated into profound business impacts, including enhanced data-driven decision-making for leadership, improved experiences for candidates and clients, and the redirection of human capital toward high-value strategic activities. Ultimately, this thesis provides an empirical validation of AI-driven automa tion in a real-world context and contributes several novel frameworks, including a cost-quality optimization matrix for LLM deployment and a systematic model for knowledge flow management in recruitment. By critically addressing the ethical imperatives of data privacy and algorithmic bias, this work offers a replicable, human-in-the-loop model for achieving scalable, efficient, and responsible techno logical transformation in the global talent acquisition industry