Bangladesh’s apparel industry is entering a moment when artificial intelligence is moving from abstract strategy to operating practice. On August 15, the Bangladesh AI Business Summit in Dhaka included textile and RMG use cases such as quality, sourcing, production intelligence, and factory-floor automation. Fashion Business Journal has also been documenting how AI is moving into core apparel operations.
The next question should be more specific: how will factories teach people to challenge an AI system when its answer looks plausible but does not fit the situation in front of them?
Bangladesh already has the right pieces for a better approach. BGMEA has paired calls for AI adoption with calls for a skilled workforce and stronger leadership. That combination matters because durable adoption depends on more than buying software or running a demonstration. Workers and managers need repeated practice deciding when to trust an output, when to verify it, and when to override it.
I would build that practice around a simple “judgment apprenticeship.”
Each week, employees using AI in a recurring workflow should complete three small exercises. First, review one AI recommendation that turned out to be incomplete, misleading, or wrong. Second, compare one AI-assisted output with the underlying source information or a more experienced colleague’s judgment. Third, explain one decision they would make differently after that review.
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The exercise should take minutes, not hours. Its value comes from repetition.
Consider a merchandising team using AI to summarize buyer changes. A junior employee should learn to notice when a summary drops a commercial condition that matters. In quality control, an employee reviewing automated classifications should learn which unusual cases require a person to look again. In production planning, a manager should learn when a neat optimization conflicts with a constraint the model did not capture.
The apprenticeship should also be role-specific. A merchandiser, quality supervisor, production planner, and HR manager face different risks and different forms of ambiguity. One generic AI workshop cannot teach all four what to notice. The common method should stay the same, but the examples, source documents, and override decisions should come from each role’s real work.
Those examples share one principle: the organization should train judgment at the same time that it trains tool use.
This also gives middle managers a clearer role. Too often, managers receive an AI tool and an instruction to encourage adoption, then get judged on whether their teams use it. That creates an incentive to maximize activity. A judgment apprenticeship creates a better management question: are people becoming more capable of knowing when the tool helps and when human context matters more?
The same process can improve psychological safety. Employees are more likely to surface problems when leaders treat corrections as useful evidence rather than proof that someone failed. If workers believe every challenge to an AI output will be read as resistance, they will keep doubts to themselves. If managers regularly ask for exceptions, corrections, and lessons, the organization learns faster.
Measurement should follow the same logic. Track rework, corrections, cycle time, quality, and the kinds of exceptions that repeatedly require human review. Tool logins can show adoption activity, but they cannot show whether employees are developing better judgment.
Bangladesh’s apparel sector competes on speed, reliability, quality, and increasingly on its ability to move into higher-value work. AI can support all four. But the factories that benefit most will not simply have more automated decisions. They will have more employees who understand where those decisions come from, recognize when context has gone missing, and know when to intervene.
That is the capability Bangladesh should scale alongside AI itself.
About the Author
Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026).