From Search Engines to AI Agents
Historically, customers discovered products through search engines or ecommerce websites. Brands invested heavily in SEO, content, and marketing to make their products visible.
But AI is changing the journey. Increasingly, consumers will ask:
- “Find me a sustainable linen summer dress under $300.”
- “Show me jackets similar to this one but cheaper.”
- “Recommend a cycling outfit for winter training.”
Instead of browsing websites, AI assistants will interpret these requests and recommend products directly. The discovery process becomes: Customer → AI agent → Product recommendation.
Why Product Data Is a Strategic Asset
AI systems don’t “see” a dress; they rely on structured product information to reason about materials, style categories, sustainability, and pricing.
In the Australian and New Zealand markets, where fashion brands often navigate complex global supply chains and seasonal shifts, this data is the difference between being found or being invisible. If your data is inconsistent, AI agents struggle to interpret it, and those products won’t be recommended.
The Role of ERP: Intelligence over Integration
Many brands assume their ecommerce platform is the centre of their data. However, ecommerce platforms are built for display, whereas the deepest intelligence sits inside ERP systems like Microsoft Dynamics 365 Business Central combined with extensions like TRIMIT.
These systems already manage the “DNA” of a product:
- Style, color, and size hierarchies
- Materials, compositions, and sustainability attributes
- Suppliers, manufacturing details, and inventory
- Pricing and margin structures
As AI commerce grows, the ERP becomes the single source of truth. Without this foundation, brands risk higher return rates and wasted ad spend because an AI agent might recommend a product based on incomplete or inaccurate data.
A New Layer: AI Commerce Distribution
New tools are emerging, such as Azoma (https://www.azoma.ai/), to standardise how brands provide data to AI agents. The goal is to ensure that when an AI assistant makes a recommendation, it uses accurate, brand-verified information. This highlights a permanent trend: product data will increasingly be consumed by machines, not just humans. In an agentic commerce world, brands are no longer optimising for search engines, they are optimising for AI agents that act on behalf of customers.
The Opportunity for Fashion Technology
At Styllar, we believe the next evolution of fashion technology focuses on product intelligence architecture. This means designing systems that:
- Structure attributes and taxonomies for machine readability.
- Maintain a single source of truth across all channels.
- Support AI-driven discovery to stay ahead of the curve.
For many fashion companies, the journey begins with strengthening their ERP foundation. Brands will still compete on creativity, but they will also compete on how intelligently their products are represented in data.
Those that invest in strong product intelligence today will own the next generation of commerce.
