Adobe：亚马逊和谷歌纷纷加码AI购物决策层 卖家需适配机器读取规则

This article reveals that two major internet platforms, Amazon and Google, are ramping up their investment in AI-powered shopping, an industry shift that rewrites the traditional logic of product discovery and directly impacts everyday consumers. Key takeaways are as follows:

1. AI has now formally entered the core of the product discovery process. Data from Amazon’s latest Prime Day shows that conversion rates from AI assistant referrals already outperform those from all other channels, and consumers have widely adopted AI-powered shopping. While AI currently accounts for a small share of overall traffic, it has enormous room for growth in the future.

2. Two mainstream AI shopping models have emerged in the industry. Amazon has built a closed, single-platform AI assistant system that can deliver discount recommendations, interpret product details, track prices, and even complete purchases directly. Google is pursuing an open, cross-scenario AI shopping system that aggregates and compares products and deals across multiple merchants, helping consumers identify suitable options faster.

3. For everyday consumers, AI will pre-screen products that match their requirements in the future, drastically reducing the cost of filtering shopping options. However, it may also cause consumers to miss out on high-quality niche products with incomplete datasets, which shoppers will still need to find via active search.

This article outlines the emerging consumer trend of AI reshaping retail product discovery logic, and puts forward new requirements for brands’ marketing operations and information infrastructure. Key takeaways for brands are as follows:

1. On the consumer trend front: AI has evolved from a shopping assistance tool to the core of the product discovery process. Amazon’s latest data shows conversion rates from AI assistant referrals already outperform other channels, and consumers have fully embraced AI shopping. AI will become the core channel for brands to acquire traffic, and brands that build early布局 will gain a first-mover competitive advantage.

2. On operational requirements: Traditionally, brand operations have focused on optimizing product information for human consumers to fit search and browsing experiences. Now, brands must add a new layer of optimization: structuring product data for AI agents. The latest operational framework treats product data as core infrastructure, rather than a secondary marketing asset.

3. Risk warning: Currently, up to 46% of retailer product content is unreadable by machines. Instead of just lowering a product’s search ranking, this incompatibility leads to AI silently excluding products from results entirely, with no notification at all—effectively revoking the product’s market access. Brands need to audit and restructure their product data systems to comply with new rules as soon as possible.

Against the backdrop of Amazon and Google expanding AI-powered shopping, this article outlines new operational requirements, risks and opportunities for platform sellers. Key takeaways are as follows:

1. Industry change and growth opportunities: AI has completely rewritten the underlying logic of product discovery, and conversion rates from AI traffic already surpass those from other channels. Sellers that adapt to machine-readable rules early can capture first-mover access to new traffic dividends and build sustainable competitive advantages. A new operational framework tailored to this shift, called Agentic Commerce Optimization, has already emerged, centered on treating product data as core infrastructure.

2. Risk warning: If a seller’s product data is too sparse or unreadable by machines, the product will not just drop in search rankings—it will be silently excluded from consumer results before they ever see it, with zero notification. Up to 46% of current seller listings suffer from this issue.

3. Mitigation steps: Sellers need to adjust their traditional operational logic. On top of existing optimization for human browsing, they must add a parallel layer of adjustments to make product information accessible to AI agents, and complete this transition as early as possible.

The AI shopping shift outlined in this article brings new business opportunities and new insights for digital upgrading for factories that produce retail goods. Key takeaways are as follows:

1. Changing market demand: Downstream brands and sellers all need to restructure their product information systems to meet machine-readable requirements, and nearly half of all current merchants do not comply with these standards. Factories can adjust their product information output standards to meet this unmet demand.

2. New business opportunities: Factories can organize standardized, structured product parameters and attributes that meet machine-reading requirements before products leave the factory, offering this as a value-added service to downstream clients. This allows factories to build differentiated competitive advantages, win more partnership orders from brands and sellers, and unlock new growth avenues.

3. Insights for digital transformation: When advancing digital and e-commerce upgrades, factories should not only optimize production processes. They must also prioritize building structured digital product information to comply with new e-commerce rules, so they can better align with downstream clients’ new demands and keep pace with industry changes.

This article covers the latest development trends of AI-powered shopping in e-commerce retail, identifies core pain points for industry merchants, and points to new business directions for related service providers. Key takeaways are as follows:

1. Industry development trend: Two tech giants, Amazon and Google, are both ramping up investment in AI-powered shopping, and AI has become the core of the product discovery process. Optimizing product data for AI agents will soon become a standard requirement for retail operations across the industry, creating large untapped market demand that represents a new growth engine for service providers.

2. Core customer pain points: Currently, up to 46% of retailer product information is unreadable by machines. This is a market access issue, fundamentally different from traditional search ranking optimization. Most merchants have not yet recognized the severity of this problem, and lack the capacity to adjust and optimize their data independently.

3. Solution direction: Service providers can build complementary detection, organization and optimization services around the new Agentic Commerce Optimization framework, helping merchants restructure their product data systems to meet Amazon and Google’s machine-reading requirements. This solves the core pain point of silent product exclusion, and meets merchants’ emerging new demands.

This article outlines the latest AI-powered shopping布局 from leading platforms Amazon and Google, and clarifies industry winds and development directions for other marketplace operators. Key takeaways are as follows:

1. Latest approaches from leading platforms: Amazon placed its AI shopping assistant at the core of the deal discovery process for Prime Day, pursuing a closed, single-platform AI architecture, and conversion from AI traffic already outperforms all other channels. Google follows an open, cross-scenario approach, launching features that aggregate and compare deals across merchants, requiring merchants to submit richer product information, and adding a new module to track product visibility in its AI engine. Both approaches offer valuable reference for other platforms.

2. New platform requirements: Large numbers of current merchants have product information that does not meet machine-reading requirements. Platforms can launch AI rule-aligned merchant training and product data detection tools to help merchants adjust their listings. This will both improve the overall quality of product supply on the platform and boost user shopping experience.

3. Risk mitigation and opportunity capture: Platforms should publish new rules in advance to avoid widespread merchant discontent from silent exclusions that catch sellers off guard. They can also build complementary operational services around the new rules to boost platform revenue and merchant retention.

This article discloses the latest industry developments in global e-commerce shopping, summarizes new business model changes, and outlines new directions for industrial research. Key takeaways are as follows:

1. New industry developments: Two technology giants, Amazon and Google, have both independently chosen to ramp up investment in AI-powered shopping, placing AI at the core of the product discovery process. AI has evolved from a shopping assistance tool to the first reader of product information, completely rewriting the product discovery logic that has held for 20 years. With AI traffic conversion rates already outperforming all other channels, the industry has officially entered a new development stage of AI-powered shopping.

2. New industry problems: A new industry phenomenon called "silent exclusion" has emerged: products with machine-unreadable data are directly removed from consumers’ consideration sets with no notification whatsoever. Currently, more than 40% of merchant product listings have this problem, an issue that never existed in the traditional search engine optimization era, and one that merits in-depth research.

3. New models and new research directions: A new operational framework called Agentic Commerce Optimization has emerged in the industry, centered on the idea of treating product data as core infrastructure. The industry has also developed two distinct AI shopping business models—closed single-platform systems and open cross-scenario systems—that provide new research samples for industrial analysis.