Deployed but Not Adopted: New Study Reveals the AI 'Trust Gap' Inside Industrial Engineering
Michimich.com/10334052

DETROIT & GÖTEBORG, Sweden - Michimich -- A newly completed master's thesis offers fresh evidence on why artificial intelligence (AI) initiatives in industrial organizations often fall short of expectations, concluding that the decisive factors are human and organizational rather than technical.

The research, completed as part of an MBA at Blekinge Institute of Technology (BTH) in Sweden by Abed Almalik Jberi and Abdul Salam Jeber, draws on a real-world AI change initiative within a global industrial manufacturing organization. The authors surveyed 40 engineering professionals — a predominantly senior sample, with 65% reporting more than ten years of professional experience and roughly 30% in leadership positions — using a quantitative descriptive survey design to analyze employees' perceptions of trust, organizational support, learning conditions, and acceptance.

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The findings point to a pronounced gap between expectation and present experience. Although 88% of respondents anticipated increased AI use over the next three years, trust in AI recommendations was negative (Net Promoter Score of -68) and confidence in daily use was also negative (-47). Only 8% of respondents reported receiving formal training, with most acquiring AI skills through trial and error.

To interpret these patterns, the study integrates two established frameworks — the Prosci ADKAR model of organizational change and Knapp's Relationship Model — positioning AI adoption as both a change process and a gradual process of trust-building. The analysis indicates that trust in AI was conditional, depending on the extent to which employees could understand, verify, and validate AI outputs against real-world results.

On the basis of these findings, the study identifies four conditions that support effective adoption: transparent communication, explainable AI systems, role-specific training, and sustained organizational support. Its central contribution is to demonstrate that the human dimension of AI adoption is not secondary to industrial performance, but a precondition through which AI becomes economically and organizationally valuable.

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"AI tools are only as valuable as the trust people place in them. Our findings show that without transparency, training, and support, even capable AI systems remain underused," said Abed Almalik Jber, co-author of the study.
The full thesis is available on request and through BTH's DiVA research repository.

About the Authors
Abed Almalik Jber works at the Schaeffler Group USA and Abdul Salam Jeber working at Toyota Material Handling EU