The textile and apparel sector in the world is about to complete a new phase in which buyers do not have to discover the products they want independently. It is being increasingly influenced by AI agents.

The international buyer, international brand, and even sourcing teams are changing how they find products and suppliers with agentic AI. Other than having to search, compare, and negotiate, decision-makers are beginning to depend on AI systems, which are able to analyze thousands of options in real time and provide suggestions on the most applicable ones. This is a structural change in sourcing behavior.

Recent statistics indicate the rate at which this change is occurring. Salesforce believes that approximately 39 percent of consumers worldwide already use AI to discover products. Mirakl states that over 70 percent of consumers use AI during the purchase process. This change of behavior is now being replicated in the B2B sourcing of the textile value chain.

Traders. In conventional sourcing of textiles, buyers used trade fairs, suppliers’ connections, and manual screening. Nowadays, AI systems can search databases of suppliers, compliance reports, price indicators, and sustainability indicators in real time. This implies that discovery is no longer grounded on the visibility of a person at an exhibition or the person with stronger ties. It relies on the choice of supplier that the algorithm makes. The transformation becomes clearer when comparing traditional sourcing with emerging agentic sourcing models:

Source: Gartner Strategic Technology Trends (2025–2026 Outlook)

This trend demonstrates the fact that the rules of competition are evolving. Visibility in the global textile market is no longer controlled only by branding or cost competitiveness. It is being dominated more by information. Provided that the product information, certifications, lead times, and sustainability credentials of a factory are not formatted in such a manner that AI systems can comprehend, then that factory might not be included in any of the AI-driven recommendations at all.

This poses an additional level of competition. Suppliers are currently competing based on price, quality, and data preparedness. Formatted product catalogues, certified ESG information, electronic compliance files, and real-time capacity information are emerging as important inputs of AI-based sourcing decisions.

The other significant change is the sourcing cycle compression. AI agents are now able to cut weeks of supplier discovery and analysis down to minutes. Buyers are provided with ready-to-use supplier lists, which have built-in comparison in cost, compliance, and delivery performance. This greatly alleviates friction and speeds up sourcing decisions.

This shift is both risky and opportune for Bangladesh and other leading textile exporters. Bangladesh has good cost and scale benefits. Nevertheless, the majority of its supplier ecosystem remains reliant on traditional visibility channels. Unless these suppliers are digitally organized and AI-enabled, they might become invisible in the future sourcing pipelines.

Meanwhile, early adopters will be able to obtain a significant edge. Such factories can be more discoverable by AI systems by investing in digital product passports, machine-readable sustainability data, and built-in supply chain platforms. This is capable of having a direct impact on the allocation of orders by international brands.

There is also a change in the evaluation of sustainability. The AI agents are able to compare the environmental and compliance data of suppliers in real-time. It implies that the sustainability claims should be supported by standardized and verifiable data. It is no longer enough to be sustainable. The suppliers should have the capability of demonstrating it in a machine-readable way.

Looking ahead, AI-driven discovery will play a central role in sourcing decisions. According to Morgan Stanley, AI could influence up to 25% of e-commerce transactions by 2030. In textile sourcing, its influence on supplier selection could be even more significant due to the complexity of global supply chains.