Agentic Commerce in Retail Is Moving Into Execution. Most Catalogues Are Not Ready.

Agentic commerce in retail is no longer a concept reserved for innovation labs. It is moving from pilots into operational reality.

According to GlobalData’s Q4 2025 Consumer Survey across Asia and Australasia, 45% of consumers say they are very or quite likely to purchase a product based on AI recommendations. In a recent media release, Jaya Dandey, Consumer Analyst at GlobalData, explained that agentic systems can now complete shopping-related tasks end-to-end.

This is a structural shift.

Because when AI systems move beyond recommending products and begin planning, selecting and executing purchases, product data becomes the interface.

Not the homepage.
Not the search bar.
The data itself.

And most retail catalogues are not built for that.

What Agentic Commerce in Retail Actually Changes

Agentic commerce in retail refers to AI systems that can:

• Understand intent
• Plan multiple steps toward a goal
• Apply constraints such as budget, allergens or preferences
• Compare options across categories
• Build shopping carts
• Execute transactions

Instead of browsing product by product, a customer might say:

“Plan five dinners for a family of four, mostly Asian recipes, no shellfish, under 45 minutes.”

An agent generates recipes, calculates quantities, substitutes intelligently if needed, checks availability and completes checkout.

This requires structured, enriched and reliable product data across the entire catalogue.

Without that infrastructure, execution breaks down.

This is not a UX problem.

It is a data architecture problem.

The Hidden Risk: Retailers Are Optimising the Interface, Not the Infrastructure

Retailers are investing heavily in:

• Conversational AI
• AI-powered search
• Personalisation engines
• Marketplace integrations
• Digital shelf analytics

Those investments are important.

But in agentic commerce in retail, visibility and performance depend on something deeper.

AI agents do not “browse” websites like humans. They interpret:

• Structured attributes
• Product relationships
• Pricing signals
• Availability feeds
• Taxonomy logic
• Metadata consistency

If attributes are incomplete, duplicated or inconsistent, the agent cannot make confident decisions.

If ingredient data is missing, allergen filtering fails.

If taxonomy is inconsistent across categories, substitution logic breaks.

If pricing feeds lag, budget constraints fail.

Agentic commerce does not expose small SEO weaknesses.

It exposes structural catalogue weaknesses.

Why Website Optimisation at Scale Now Determines AI Visibility

Traditional ecommerce optimisation focuses on:

• Keyword rankings
• Meta data
• On-page content
• Internal linking
• Technical SEO

Those remain relevant.

But agentic shopping channels require something different.

They require:

• Attribute completeness across thousands of SKUs
• Normalised data models
• Cross-category consistency
• Constraint-aware enrichment
• Real-time accuracy
• Structured schema alignment

This is optimisation at scale, not page-by-page editing.

In agentic commerce in retail, incomplete attributes are not minor ranking issues.

They are execution blockers.

An AI system cannot include a product in a basket if it cannot verify constraints. It cannot compare effectively if specifications are inconsistent. It cannot recommend confidently if product relationships are unclear.

Catalogue quality becomes performance infrastructure.

From Product Feed Management to Product Feed Performance

For the past decade, product feed management has focused on distribution.

The goal was to:

• Push structured feeds to Google Shopping
• Sync with marketplaces
• Meet formatting requirements
• Maintain pricing and availability accuracy

That layer is still necessary.

But agentic commerce in retail shifts the objective.

Feeds are no longer just distribution channels. They are decision inputs.

This demands:

• Data enrichment beyond supplier defaults
• Performance-based optimisation
• Ongoing auditing of attribute gaps
• Structured relationship mapping
• Continuous refinement loops

Product feed optimisation is no longer about compliance.

It is about performance within AI-driven systems.

Retailers who treat feeds as technical exports will struggle.

Retailers who treat feeds as strategic assets will perform.

Agentic Readiness Is Not a Content Rewrite Project

This is where many organisations misunderstand the shift.

Agentic readiness is not achieved by rewriting product descriptions.

It is not solved by adding more marketing copy.

It is not a one-time SEO refresh.

Agentic commerce in retail is an infrastructure challenge involving:

• Attribute modelling
• Data normalisation across systems
• Taxonomy alignment
• Cross-category consistency
• Schema structuring
• Feed logic optimisation
• Performance monitoring loops
• Operational workflows

It spans ecommerce, SEO, merchandising, IT and marketplace teams.

It requires automation, auditing and scale.

For retailers with 10,000, 50,000 or 150,000 products, this cannot be managed manually.

This is where operational efficiency and AI-driven catalogue optimisation become critical.

Why APAC Grocery Is a Signal, Not an Outlier

GlobalData’s research highlights how grocery retailers in APAC are already deploying AI across forecasting, replenishment and consumer-facing execution.

APAC markets are ideal test beds because of:

• Dense urban store networks
• High digital wallet penetration
• Integrated delivery ecosystems
• Frequent shopping behaviour

But this is not confined to grocery.

The same agentic logic applies to:

• Fashion size and fit filtering
• Electronics compatibility matching
• Home improvement specification comparisons
• Beauty ingredient constraints
• Marketplace cross-seller substitution

Wherever constraints exist, structured product data determines performance.

Agentic commerce in retail is sector-agnostic.

Infrastructure readiness will determine who benefits.

The Competitive Advantage Will Be Structural

As agentic shopping expands across:

• AI search engines
• Embedded assistants
• Messaging platforms
• Marketplace ecosystems
• Digital wallet integrations

Visibility will favour structured retailers.

Those with:

• Clean, enriched product data
• Complete attribute coverage
• Clear taxonomy logic
• Real-time feed accuracy
• Automated optimisation workflows

Will see higher inclusion in AI-generated recommendations and baskets.

Those relying on duplicated supplier feeds and thin category structures will struggle to participate.

This is not about replacing traditional search.

It is about adding a new discovery layer.

And that layer is machine-evaluated.

Preparing for Agentic Commerce in Retail

Retailers should assess:

• Are key attributes complete across all categories?
• Are ingredient and specification data structured for constraint filtering?
• Is taxonomy consistent across systems?
• Are feeds continuously audited for performance gaps?
• Is duplication eliminated at scale?
• Is optimisation automated or manual?

If the answer relies on spreadsheet updates and reactive fixes, the catalogue is not ready.

Agentic commerce in retail rewards proactive infrastructure.

Not reactive patchwork.

The Shift Is Already Underway

The GlobalData release signals growing consumer acceptance of AI recommendations. Operational deployment in grocery shows execution is already happening.

Retailers do not need to wait for full mainstream adoption to act.

By the time agentic shopping becomes dominant, the structural advantage will already belong to those who invested early.

Because in agentic commerce in retail, your products are not simply displayed.

They are interpreted.

They are evaluated.

They are filtered.

They are executed.

And that changes the competitive landscape.

The interface may evolve.

But the advantage lies deeper.

In the data.