A garment factory in Dhaka and a vineyard in South Australia may seem to have little in common.
One is trying to keep export orders coming in; the other is managing seasons, quality and overseas markets.
Yet both make daily decisions about production, stock, timing and customers. Much of the information needed to make those decisions already exists.
The question is whether businesses can put it to better use and whether AI can help them do more than save time or money.
Bangladesh’s export warning lights
Bangladesh’s ready-made garment sector remains the country’s biggest foreign-exchange earner.
In the fiscal year ending June 2026, apparel exports brought in US$38.70 billion, down 1.64 per cent on the previous year, according to Observer BD and the United News of Bangladesh (UNB).
The European Union accounted for 49.25 per cent of garment exports, compared with 20.01 per cent for the United States.
The United Kingdom accounted for 11.34 per cent and Canada 3.47 per cent, while other markets made up 15.93 per cent.
With almost half of exports going to the EU, a downturn in European demand can quickly affect manufacturers.
The pattern over the year was far from steady. Garment exports grew by 4.79 per cent in the first three months of FY2025–26.
By January, the cumulative figure had turned to a 2.43 per cent decline; by February, the fall had deepened to 3.73 per cent.
Through May, exports were down 3.41 per cent. The final full-year decline was smaller, at 1.64 per cent.
These shifts show why looking only at last month’s orders can leave a factory reacting too late.
Better forecasting may help businesses spot changing demand earlier, though data cannot create demand when buyers are pulling back.
Data is there. Is anyone using it?
In an interview with The Business Standard, the founder of Dhaka start-up Merchant Bay described an industry where decisions often rely on managers’ intuition.
Merchant Bay’s software turns factory records into reports and performance indicators.
The company raised US$260,000 in pre-seed funding in 2021 and also operates a marketplace where buyers post requirements and manufacturers bid for orders. It promotes reliability and traceability as part of its value proposition.
This is a useful example of digitisation, but it does not show that autonomous AI agents are already widely used in garment factories.
South Australia’s export opportunities
South Australia’s food, wine and agribusiness sector generated A$17.1 billion in revenue in 2023–24, supported 76,000 full-time-equivalent jobs and accounted for 49 per cent of the state’s merchandise exports, according to a report presented to the South Australian Parliament.
The state’s exports to the United States reached a record A$2.49 billion in the year to September 2025, up 16 per cent, while China remained the leading destination for South Australian wine, according to the state government.
Food and beverage products also made up 60 per cent of South Australia’s exports to Japan in the year to April 2025, worth A$327.5 million.
The state has promoted its food, wine and agribusiness sector through trade activity, including a delegation to World Expo 2025 in Osaka.
These figures point to opportunity, but exporters still need to understand what is selling, where demand is changing and how much stock to move.
The Bangladesh and South Australian figures cover different periods, currencies and measures, so they are not directly comparable.
Their shared lesson is simpler: exporters need to notice changes early enough to respond, rather than discovering a problem after orders slow or stock piles up.
Saving costs is only the beginning
Analytics and AI can help businesses reduce defects, cut waste, improve maintenance and plan production more accurately.
For firms working on tight margins, those gains matter.
But if competitors can buy similar tools, the savings may be copied. Efficiency is useful; on its own, it may not give customers a lasting reason to choose one supplier over another.
A bigger opportunity is to use reliable information to offer buyers something beyond a low price.
A garment factory that provides accurate production updates and dependable delivery records can help buyers manage their own commitments.
A South Australian producer might use clear provenance and quality records to show where a product came from and how it was handled.
Whether customers will pay more for that assurance, or whether it will win a new market, still needs to be tested.
That is the difference between doing the same work more efficiently and finding a new way to serve customers.
Data may support a new service, a closer relationship with buyers or a business model that was previously too difficult to manage.
Research from MIT Sloan’s Center for Information Systems Research has explored how AI can support new business models.
The idea is promising, but neither industry should assume the benefits will arrive simply by adopting the technology.
The hard part is not buying the technology
Buying a tool is the easy part. Businesses need accurate records, staff who can interpret them and managers willing to change plans when the information points in another direction.
Smaller firms may struggle with cost, skills shortages or the effort of connecting new tools to existing systems.
Without those basics, an AI project can become another expense rather than a source of value.
There is also a question about who benefits. If a factory becomes more productive, do workers gain better skills and more secure work, or do the gains go mainly to owners and buyers? If producers collect more detailed information, who controls it? These are practical questions that should be considered as businesses adopt new technology.
The real test starts after the software is installed
For Bangladesh’s garment manufacturers and South Australia’s food and agribusiness exporters, AI is not a strategy on its own.
Better data may help them see changes sooner, prove the quality of what they sell and give customers reasons to look beyond price.
The first benefits may be fewer delays, less waste and more reliable deliveries.
Turning those improvements into a new service or a stronger relationship with buyers is harder. It will take good decisions, not just new technology.
(Writers: Both are at Adelaide University, Australia)