Why Product Data Is Critical in the Age of Agent-Based Commerce

Summarize this article

E-commerce is entering a new phase of evolution in which consumers are no longer the only ones making purchases.

The rise of agent-based commerce is profoundly transforming the way products are discovered, evaluated, and purchased. Instead of manually browsing e-commerce sites, comparing products, or finalizing their orders themselves, consumers are gradually delegating these tasks to AI agents capable of acting on their behalf.

Consumer behavior is evolving rapidly. According to several recent studies, more than 70% of consumers now expect personalized and context-aware shopping experiences, while nearly one in two say they are willing to use AI tools to facilitate product discovery. Yet a major obstacle remains: trust. Today, only 17% of consumers would allow an AI agent to make a purchase on their behalf.

It is precisely in this gap between expectations and trust that the next wave of innovation and differentiation will play out.

For brands and retailers, one question then becomes central: what determines whether an AI agent selects their products rather than those of a competitor? The answer lies in the quality of product data.

What is agent-based commerce, and how did we get here?

To understand why product data is becoming so strategic, we need to look back at the evolution of the shopping journey.

Yesterday: Traditional E-commerce

Traditional e-commerce has always relied on the consumer to manage the entire shopping journey. Take, for example, someone looking to update the lighting in their living room without knowing exactly which products to choose. Their journey typically looks like this:

• Translating their need into search queries
• Browsing several e-commerce sites or marketplaces
• Compare dimensions, materials, technical specifications, and customer reviews
• Checking availability, delivery times, and prices
• Add products to the cart and complete the order

Each step takes time and effort. Every decision requires interpretation. And every friction point increases the risk of abandonment.

Today: AI-Enhanced E-commerce

Artificial intelligence is now beginning to transform the first part of this journey. Instead of browsing multiple sites, a consumer can simply explain their need to a conversational assistant like ChatGPT:
“I want to modernize the lighting in my living room with a warm, contemporary ambiance.”
The AI can then:

• Ask follow-up questions about the room or technical constraints
• Recommend suitable styles and finishes
• Suggest compatible products
• Build an initial relevant selection

This approach significantly simplifies product discovery. But the experience often remains incomplete.
Once the recommendations are received, the consumer must still:

• Visit e-commerce sites
• Search for products
• Check availability
• Add items to the cart
• Complete the payment

In other words, the second part of the journey remains largely manual. This creates a new form of friction: the experience becomes seamless during product discovery, but breaks down at the point of purchase. It also highlights a trust issue: consumers are increasingly accepting AI-generated recommendations, but remain much more cautious when it comes to entrusting the transaction to AI.

Even the most advanced players in the market are currently running into these limitations. Early experiments with “buy for me” features show that the transition from recommendation to purchase raises significant issues regarding trust, accountability, and data reliability.

The Future: Agent-Based Commerce

Agent-based commerce represents the next step: AI no longer simply assists the consumer; it acts directly on their behalf. Instead of recommending products, AI agents will be able to manage the entire transaction. In our example, a consumer could simply say:

“Buy everything needed to modernize the lighting in my living room with a warm, modern ambiance, for less than €300.”

The AI agent could then:

• Identify suitable products
• Check inventory across multiple retailers
• Compare prices and delivery times
• Build and optimize a shopping cart
• Finalize the transaction
• Confirm the order

Beyond one-time purchases, these agents will also be able to:

• Automatically trigger a purchase when a price reaches a defined threshold
• Reorder certain recurring products
• Reserve high-demand products as soon as they become available

This shift is already taking shape. Advances in real-time catalogs, contextual personalization, and AI-driven transactional systems are accelerating this transformation.

Why product data is becoming the deciding factor

In an environment where AI agents are gradually making purchasing decisions on behalf of consumers, product data is becoming a strategic asset. Unlike a salesperson or a consumer, an AI agent is not influenced by a particularly well-designed website or a polished user experience. It makes decisions based on the available data.

If product data is incomplete, inconsistent, or difficult to use, the products in question may simply not be selected. This is why the quality of product data is becoming critical.

1. Visibility in AI-driven environments

AI agents do not “navigate” like humans. They process structured data. If product attributes (dimensions, materials, compatibility, uses) are not clearly defined and standardized, the products may never appear in the results suggested by the AI.

2. Contextual relevance

Agent-based commerce relies on context: budget, intent, preferences, location, or specific constraints. Enriched product data—including detailed descriptions, use cases, or compatibility information—enables AI to offer much more relevant recommendations.

3. Trust and reliability

AI agents must be able to rely on reliable data to make decisions. Outdated or inaccurate information (incorrect inventory, missing specifications, erroneous delivery times) immediately increases the risk of transaction failure, a particularly sensitive issue for both consumers and platforms.

4. Interoperability Between Systems

Agent-based commerce is not limited to a single environment. Agents must interact with marketplaces, search engines, e-commerce sites, and third-party platforms. Structured product data ensures that catalogs are easily understood, shared, and utilized across these different systems in real time.

How to build a solid product data foundation?

Preparing for agent-driven commerce starts with better product data governance. Here are several key priorities:

1. Centralize product information

The first step is to eliminate data silos. Product information must be consolidated into a single source of truth to ensure consistency across teams, channels, and systems. This is precisely the role of Akeneo Product Cloud.

2. Standardize attributes and taxonomies

Consistent attributes make it easier for AI systems to interpret products. Standardizing categories, nomenclatures, and characteristics is essential for improving product comparability.

3. Enrich data with context

Technical specifications alone are no longer enough. Companies must also incorporate:

• Use cases
• Compatibility information
• Content focused on real-world use cases
• Vocabulary that consumers can understand

This allows AI agents to better understand the contexts in which a product should be recommended.

4. Ensuring real-time data updates

Prices, availability, inventory levels, and delivery times must be constantly synchronized. Agent-based commerce relies heavily on real-time decision-making, and outdated data quickly erodes trust.

5. Structuring data for AI systems

Data must be easily usable by automated systems. This involves, in particular:

• Consistent formats
• Standardized naming conventions
• Structures compatible with APIs and third-party systems

6. Implement a continuous improvement process

Customer behavior, reviews, product returns, and usage data should fuel a continuous process of improving product information. Over time, this helps build richer, more accurate, and more relevant product data for both consumers and AI agents.

Preparing for a more autonomous retail experience

The shift toward agent-driven commerce is already underway.

For now, experiences remain hybrid: AI handles certain stages of the journey, while consumers retain final approval. But as trust grows and technologies mature, an increasing share of purchasing decisions will be delegated to autonomous agents.

This shift is profoundly changing the rules of competition. In the future, brands will no longer differentiate themselves solely through their products or prices. They will also need to be:

• The most understandable to AI systems
• The most reliable in their data
• Most relevant in their ability to respond to a specific context

In other words, the companies that will succeed are those that invest in their product data today. Because in a world where AI makes purchases, product data becomes the experience itself.

Article co-written with our partner Akeneo.