Many are worried about the future of human jobs due to the dramatic changes brought about by the fast development of AI and automation in the industrial environment. To better comprehend the multifaceted effects of technological advancement on the labor market, this case study zeroes in on the industrial sector. To evaluate the impact of technology innovation on employment, Multi-Criteria Decision-Making (MCDM) is useful for methodically finding and choosing relevant factors. Important components outlined by MCDM, such as “Policy Efficacy and Alignment,” “Technological Integration Effectiveness,” “Employment Impact and Dynamics,” and “Adaptation Strategies by Businesses,” are among the established criteria. An invaluable idea for capturing fuzzy information in decision-making is the intuitionistic fuzzy \(\hat{Z}\) number ( \(IF\hat{Z}N\) ), which can handle higher levels of uncertainty than fuzzy and fuzzy \(\hat{Z}\) numbers ( \(F\hat{Z}N\) ). To handle higher levels of uncertainty, we offer and examine intuitionistic fuzzy \(\hat{Z}\) numbers ( \(IF\hat{Z}N\) ), which we think are a major improvement over current fuzzy set designs. The new operating guidelines and weighted aggregation operators that go along with them are the real prize here. We focus on updating the operational specifications for operators that aggregate Einstein data. We take a close look at these newly suggested rules and their features. Next, we develop new aggregation techniques for ( \(IF\hat{Z}N\) ) data by expanding upon these operational ideas, and we investigate the characteristics and connections between these operators. In order to make possible future applications easier, we also present a method for making decisions with numerous attributes in the \(IF\hat{Z}N\) setting. Lastly, a case study is provided to illustrate the approach’s practical applicability.