Can AI Really Cut Costs in Bangladesh's RMG Sector?

Is artificial intelligence actually capable of cutting costs in ready-made garments (RMG) at scale? Or is technology being used as a convenient explanation for decisions driven mainly by economics?

The question has taken on new weight after Ha-Meem Group Managing Director AK Azad announced plans to cut around 10,000 jobs from his company's roughly 75,000-strong workforce. In a media interview, Azad said Bangladesh's near 9 percent annual inflation was forcing businesses to keep raising wages and benefits, while international buyers, particularly from the United States and Europe, remained unwilling to raise product prices in return.

He said global buyers were instead pushing manufacturers toward AI and technology-driven production to improve efficiency and cut reliance on manpower.That explanation deserves scrutiny. The answer is not simple. AI can reduce costs in specific parts of the production process. But two barriers stand in the way of doing this well, and quickly, in Bangladesh: skilled talent and the cost of adoption.

Yes, AI Can Cut Costs—But Only in Certain Places
AI is not a single tool. It works differently across different stages of garment production.
1. In quality control: AI-based defect detection is already improving output and reducing errors in Bangladesh's factories. Cameras can catch stitching flaws and fabric defects faster than manual inspection.
2. In maintenance: AI systems can watch machine performance in real time. They can predict when equipment is about to fail. This cuts costly downtime and reduces the staff needed to monitor machines constantly.
3. In planning: AI can automate demand forecasting, order tracking, and reporting. This reduces the back-office staff needed for administrative work.

Sewing remains the hardest stage to automate. It still depends on human hands and judgment. Cutting and design work show more promise, since AI can optimize fabric layout and generate patterns automatically.

So the honest answer is - yes. 'AI can cut costs.’ But unevenly, and only where factories are equipped to use it correctly. This is where the real barriers appear.

Barrier One: It's Not a Talent Shortage. It's a "Who Trains the Trainer" problem.
A common assumption is that Bangladesh lacks AI talent. This is not accurate. Bangladesh has a large pool of capable software engineers. General coding and AI development skills exist in the country already. The real gap sits somewhere more specific, at the intersection of two fields.

A software engineer can build an AI model. But that model needs to understand how a real sewing line, cutting floor, or quality control process actually works. That knowledge does not sit with coders. It sits with factory managers, technicians, and industrial engineers, people who understand garment production from the inside.

Very few people in Bangladesh currently combine both skill sets: deep RMG production knowledge and AI/data science expertise. This creates a narrow but critical bottleneck.

This raises the central question the industry has not yet answered: who trains the trainer?

Before AI can be deployed usefully on a factory floor, someone has to teach the AI engineers how garment production actually works. That person needs both technical and industrial knowledge. Right now, that role barely exists in Bangladesh, formally or informally.

Industry voices have acknowledged the broader skills challenge, if not this exact framing. Speaking at the BAYLA Future Summit 2026, former BGMEA president Faruque Hassan said manufacturers must invest more aggressively in "technologies that improve productivity and reduce costs" and move the country into higher-value market segments, warning that competitors were moving faster on AI and automation.

Speaking at a BGMEA-ASSET workshop reviewing the progress of the association's enterprise-based training program, current BGMEA president Mahmud Hasan Khan said the industry must become more skilled and technologically advanced to keep pace with global competition.

But existing training programs, including BGMEA's collaboration with the World Bank-funded ASSET project, remain focused on general workforce upskilling. They are not yet structured around the specific hybrid skill this moment requires.

Until that bridge exists, AI adoption in RMG risks being led by people who understand the technology but not the factory floor. Or by factory veterans without the tools to apply AI correctly.

Barrier Two: The Cost of Adoption
Even where the right skills exist, cost remains a serious obstacle. AI systems are not cheap. Equipment, software licenses, and staff training all carry upfront costs. For a large conglomerate like Ha-Meem Group, these costs may be manageable. For Bangladesh's many small and mid-sized factories, they can be prohibitive.

This creates a two-tier industry. Large groups can invest in AI and describe it as modernization. Smaller factories may be forced to cut jobs simply to survive, without ever adopting the technology being cited as the reason.

There is also a currency problem. Much of the AI software and hardware used in manufacturing comes from foreign vendors. This means adoption costs are paid in US dollars or euros, not taka.

Given that Azad himself points to inflation and rising costs as a core pressure, importing foreign AI systems may shift costs rather than reduce them, from wages paid in local currency to license fees paid in foreign currency.

Speaking at an industry forum on post-LDC competitiveness, BKMEA president Mohammad Hatem warned that factories failing to adopt AI and digital technologies would struggle to remain competitive in the coming years.

That warning may be accurate. But it does not answer who will bear the transition cost, or how smaller manufacturers are expected to fund it.

The Open Question
Azad's announcement links Ha-Meem's job cuts to buyer pressure toward AI-driven production. The evidence suggests that AI can genuinely reduce costs in specific functions - quality control, maintenance, and planning, in particular.

But two conditions have to be met first. Bangladesh needs people who understand both garment production and AI, not just AI alone. And factories need affordable, local access to the tools required to implement it.

Neither condition is fully in place today. Until they are, it remains fair to ask: is AI genuinely driving this shift, or is it providing cover for a workforce reduction that would have happened regardless?

Writer:  Tech Industry Professional and Former Journalist

Disclaimer: The opinions expressed in this article are solely those of the author and do not necessarily reflect the views of Digital Bangla Media. In keeping with the principles of pluralism, the article has been published without editorial alteration. Any offense or disagreement arising from its contents remains entirely the responsibility of the reader.