Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Automation of tasks as a result of advances in Artificial Intelligence (AI) is currently one of the major economical drivers. However, the varying effectiveness of AI usage across occupations and industries suggests that the impact of AI diffusion is uneven. Thus, it is imperative to understand which types of tasks are more or less prevalent in AI-enabled businesses. Using a cross-sectional dataset of 27,700 start-ups and occupation data, we utilize word embedding to link start-ups to their respective underlying tasks. We compare the task types of AI-enabled with non-AI start-ups in the services and platforms domain using a suitability for machine learning metric. The results show that analytical, logistical, and statistical tasks predominate among AI-enabled start-ups while services with customer proximity have a smaller share and the overall task diversity is lower. The implications of our findings are discussed in the light of labor theory and the economies of scale of AI start-ups.
Recommended Citation
Schulte-Althoff, Matthias, "What's to Automate? A Task Analysis of AI-enabled Start-ups" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 4.
https://aisel.aisnet.org/hicss-56/da/emerging_markets/4
What's to Automate? A Task Analysis of AI-enabled Start-ups
Online
Automation of tasks as a result of advances in Artificial Intelligence (AI) is currently one of the major economical drivers. However, the varying effectiveness of AI usage across occupations and industries suggests that the impact of AI diffusion is uneven. Thus, it is imperative to understand which types of tasks are more or less prevalent in AI-enabled businesses. Using a cross-sectional dataset of 27,700 start-ups and occupation data, we utilize word embedding to link start-ups to their respective underlying tasks. We compare the task types of AI-enabled with non-AI start-ups in the services and platforms domain using a suitability for machine learning metric. The results show that analytical, logistical, and statistical tasks predominate among AI-enabled start-ups while services with customer proximity have a smaller share and the overall task diversity is lower. The implications of our findings are discussed in the light of labor theory and the economies of scale of AI start-ups.
https://aisel.aisnet.org/hicss-56/da/emerging_markets/4