Studiengruppe: Lifelong Learning

Artificial intelligence (AI), particularly generative AI (GenAI), can drive significant improvements and create new requirements in organizations and economies. It transforms work and demands a workforce equipped with the corresponding competencies and skills to utilize these modern technologies efficiently. Based on a review of existing research and case studies, this paper presents the AI-driven shift in the workforce, specifically highlighting the role of GenAI in enhancing work, facilitating connections, expanding access to AI use, and overcoming barriers.

Artificial intelligence (AI) in the workplace presents both challenges and opportunities for workers, organizations, and economies, as it supports the automation of tasks in companies and facilitates complex decision-making processes. Particularly, GenAI tools can be used to create new content, such as text, images, audio, or video, based on the inputs of machine learning models. Some steps for enabling GenAI among the workforce, for continuous development of new skills, and a checklist for engaging the workforce on GenAI are presented in this paper.

By using AI, new roles are being created in companies that require new skills and competencies. Necessary AI specialists, data scientists, machine learning engineers, robotics operators, professionals who can work with AI, and AI trainers are necessary. The role of reskilling in workforce development of upskilling in workforce adaptation increases. Some examples are given. Additionally, the importance of lifelong learning and how both employees and employers can contribute to the company's success in this context are discussed. Ways, which lifelong learning helps address the skills gaps, as well as suitable lifelong learning strategies for employees and employers, are given.

A multiphase exploration in this context has been done, consisting first of an existing literature review, then discussions with students at vocational schools who also work in companies, trainers, and educators were conducted to develop and test some theoretical and practical ideas.

Based on these results, a flexible framework will be proposed to support job augmentation and workforce productivity growth with GenAI. It is useful for organizations starting out their workforce deployment processes in using GenAI, as well as for those that would like to improve such processes. To prepare for the development of the frameworks, some steps for a systematic way to document and evaluate GenAI use cases, as well as conclusions, are given.