This research was conducted to identify and measure the significance of the factors or variables that influence technology acceptance for a smart lighting system built based on the internet of things (IoT) and artificial intelligence (AI) technology implemented in XYZ company. The smart lighting system implemented was a dedicated smart lighting system for office space (more than 20 m2 to 60 m2) to sense the conditions and make automatic adjustments to room conditions. Before mass production, the smart lighting system would be reviewed for its technology acceptance by users using the technology acceptance technology model (TAM). TAM is a method used to identify factors that affect the technology acceptance based on the functionality of the smart lighting system. Based on the smart lighting purposes and conditions from the XYZ company, six variables influencing the acceptance of smart lighting systems, namely reliability and accuracy (RA), perceived ease of use (PEOU), perceived usefulness (PU), attitude toward using (ATU), behavior intention (BI), and actual system use (AU) were proposed. These variables influenced each other and formed eight hypotheses, namely H1, H2, H3, H4, H5, H6, H7, and H8. Using the purposive sampling technique, validity test with product-moment correlation, and Cronbach’s alpha validity test, five hypotheses had a positive and significant effect, namely H1, H4, H5, H6, and H7. The RA variable influenced the PU variable, the PU variable influenced the ATU variable, the PEOU variable affected the ATU variable, the ATU variable influenced BI, and the PU variable affected BI. Meanwhile, the three hypotheses had negative and insignificant impacts, namely H2, H3, and H8. The RA variable did not affect the PEOU, the PEOU variable did not affect the PU, and the BI variable did not affect the AU variable.