Enhancing onboarding with AI : designing a modern conversational agent for onboarding
Tariq, Muhammad Zubair (2024)
Diplomityö
2024
School of Engineering Science, Tietotekniikka
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2024052737703
https://urn.fi/URN:NBN:fi-fe2024052737703
Tiivistelmä
The onboarding process in organizations presents significant challenges for new employees, particularly in accessing specific information quickly and adapting to the new role and work environment. This often requires continuous mentoring by senior employees or experts, who may not always be available. To address this issue, this thesis investigates the potential of Artificial Intelligence (AI) to enhance the onboarding process by proposing the design of a Modern Onboarding Agent (MONA).
The study focuses on the use of AI, especially conversational agents, to make onboarding more efficient, personalized, and engaging. A quantitative methodology is employed, utilizing a structured survey to gather data on onboarding practices, interaction preferences with MONA, and comfort with data privacy. The survey is designed based on an extensive review of conversational agents used in onboarding and is conducted among employees from various organizations with higher education levels. Statistical analysis of the survey responses informs the design of MONA.
Design Science Research (DSR) is applied to create a framework that identifies key findings (KFs) from the data to derive design principles (DPs). The study reveals that while most companies have formal onboarding procedures, few customize them by role, resulting in inadequately prepared new hires and lower job satisfaction. Survey respondents expressed a preference for interactive and personalized onboarding experiences, highlighting the importance of customization for user engagement and satisfaction. Another critical finding is the necessity for strict data protection measures due to the importance of privacy and data security.
Based on these results, the thesis proposes several design principles for MONA, including personalized onboarding programs, interactive learning elements, prompt support, strict data security measures, and continuous user engagement. The research highlights the importance of customization, interaction, and secure data management in creating an effective onboarding agent. Further insights in this direction include incorporating continuous user input, performing scalability testing in various organizational contexts, and integrating advanced AI features such as predictive analytics and enhanced emotional intelligence. In summary, this thesis establishes a strong foundation for developing AI-driven onboarding solutions, with MONA exemplifying a tailored and engaging onboarding experience for employees.
The study focuses on the use of AI, especially conversational agents, to make onboarding more efficient, personalized, and engaging. A quantitative methodology is employed, utilizing a structured survey to gather data on onboarding practices, interaction preferences with MONA, and comfort with data privacy. The survey is designed based on an extensive review of conversational agents used in onboarding and is conducted among employees from various organizations with higher education levels. Statistical analysis of the survey responses informs the design of MONA.
Design Science Research (DSR) is applied to create a framework that identifies key findings (KFs) from the data to derive design principles (DPs). The study reveals that while most companies have formal onboarding procedures, few customize them by role, resulting in inadequately prepared new hires and lower job satisfaction. Survey respondents expressed a preference for interactive and personalized onboarding experiences, highlighting the importance of customization for user engagement and satisfaction. Another critical finding is the necessity for strict data protection measures due to the importance of privacy and data security.
Based on these results, the thesis proposes several design principles for MONA, including personalized onboarding programs, interactive learning elements, prompt support, strict data security measures, and continuous user engagement. The research highlights the importance of customization, interaction, and secure data management in creating an effective onboarding agent. Further insights in this direction include incorporating continuous user input, performing scalability testing in various organizational contexts, and integrating advanced AI features such as predictive analytics and enhanced emotional intelligence. In summary, this thesis establishes a strong foundation for developing AI-driven onboarding solutions, with MONA exemplifying a tailored and engaging onboarding experience for employees.