Enterprise AI Analysis
Trust Without Understanding: A Case Study of Industrial Computer Vision in Protein Processing
Trust in Al systems is often treated as contingent on user understanding and model explainability. This paper examines how organizational trust developed around a computer vision system deployed in legacy protein processing facilities where digital infrastructure was historically minimal. Drawing on nine semi-structured interviews with staff at the technology provider and the processor's internal project lead, we analyze how the system became accepted not as "AI," but as measurement infrastructure embedded within existing quality control regimes. Organizational trust developed through hardware reliability in a harsh environment, validation rituals grounded in existing quality frameworks (e.g., ANOVA, gauge R&R), low-stakes framing of error, and the system's fit with established organizational categories and routines. We show how ground truth and "accuracy" were pragmatically adjusted to preserve usability, and argue that trust in industrial AI is an organizational and infrastructural accomplishment, challenging assumptions that explainability is a universal prerequisite for trust.
Executive Impact & Key Findings
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Interviews Conducted
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System Pilot Year
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Allowance Adjustment
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Customer Organizations
Deep Analysis & Enterprise Applications
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This category explores how digital technologies are integrated into organizational structures, processes, and cultures. It focuses on the sociotechnical aspects of AI adoption, examining how human-computer interaction, collaboration, and social dynamics shape the success and impact of AI systems within enterprise settings.
Risk Acceptance & Unmet Operational Need
The system's adoption was driven by a context of prior failures and unmet operational needs, rather than initial enthusiasm for AI. The protein processor had attempted other solutions unsuccessfully, leading to a 'desperation' for improvement. This fostered a willingness to accept the risk of a new system, as existing manual processes were labor-intensive, inconsistent, and poorly suited to production scale. This illustrates how crucial operational gaps can drive AI adoption even in environments with initial skepticism.
Enterprise Process Flow
Initial skepticism about hardware durability
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On-site pressure washing demonstration
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Engineers' responsive troubleshooting
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Perception of physical resilience established
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Trust in material reliability secured, then focus shifted to data validity
| Concept | Traditional AI Trust Expectation | Observed Trust Drivers (This Study) |
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| Explainability & Model Internals |
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| Accuracy Definition |
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| Validation Approach |
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2X
Allowance Adjustment Factor: Accuracy pragmatically adjusted to align system outputs with human expectations. Stability and trend-sensitivity across shifts were prioritized over absolute individual precision.
Explainability Deemed Organizationally Irrelevant
Despite explicit probing, explainability was consistently characterized as irrelevant to adoption. It was not a prerequisite for trust and was sometimes viewed as potentially confusing. Trust was sustained through consistent outputs, successful validation, and alignment with managerial routines. Over time, the system became 'infrastructure,' accepted as 'the gospel' by new supervisors without questioning its internals, illustrating trust through routinization rather than scrutiny.
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