Chinese enterprises expanding globally are shifting from selling products to building brands.

According to customs data, exports of high-tech products and independent brands grew by 39% and 25.4% respectively in the first half of 2026. However, growing export volume and improved product capabilities do not necessarily mean these brands have secured a stable place in overseas consumers' purchasing decisions.

How to be discovered, understood, and trusted remains a critical missing piece for Chinese companies transitioning from cross-border sales to global operations.

The rise of generative AI has introduced a new variable into this equation. Consumers are now using AI to compare products, research brands, and receive purchase recommendations, reshaping the traditional marketing funnel built around search, clicks, and conversions.

At its iPX 2026 China Overseas Marketing Summit held in Shenzhen on September 3, impact.com characterized this shift as moving from the "search era" into the "answer era." Chief Revenue Officer Justin Morrison and Greater China President Jennifer Zhang shared their perspectives on how these changes are affecting the industry.

In their view, corporate interest in AI is moving beyond features toward market opportunities and business outcomes. While AI can help brands find partners and analyze visibility in large language model responses, sustained budget allocation will ultimately depend on metrics like new customer acquisition, revenue, and retention.

AI has accelerated the speed of information generation and resource matching, but it has not shortened the time required for product refinement, localized operations, and brand trust-building.

AI is changing how brands get explained

Previously, consumers entered keywords and had to manually browse multiple pages and compare information themselves. Now, AI can pre-filter and summarize, placing a limited set of brands and reasons into a single answer.

Justin Morrison describes this as the transition from "active search" to "being recommended."

This does not mean traditional search will disappear anytime soon; rather, an information intermediary has been inserted between brands and consumers. Transactions may still occur through search engines, e-commerce platforms, or official websites, but whether a brand makes it into the candidate pool is increasingly influenced by AI-generated answers.

Public data illustrates this coexistence. Adobe's analysis of over 1 trillion visits to U.S. retail websites shows that in Q1 2026, traffic originating from generative AI increased 393% year-over-year, and in March 2026, the conversion rate for this traffic was 42% higher than non-AI traffic.

This indicates that AI-driven referral traffic is still growing rapidly, but the year-over-year growth rate alone does not prove its scale has surpassed mature channels.

An analysis by Pew Research Center of 68,900 searches found that when an AI summary appeared, only 8% of users clicked traditional search results, compared to 15% when no summary was shown. Meanwhile, Google's search-related revenue grew 17% year-over-year in Q2 2026.

A more accurate assessment at this stage is not that AI is replacing search, but that search interfaces and traffic distribution mechanisms are being reshaped by AI.

For marketers, the key change lies at the source level of answers.

Justin Morrison emphasizes that a substantial portion of content cited by AI is created by partners. Jennifer Zhang further notes that available tools can trace which content sources are being indexed by AI, enabling brands to identify influential partners.

Creators, content publishers, and user communities that previously served seeding and traffic-driving functions may now also influence whether a brand enters AI's recommendation scope.

impact.com's collaboration with generative search optimization firm Evertune follows this logic. The company attempts to identify creators and publishers frequently cited in AI answers and integrates them into its partner recruitment and management processes.

Evertune reports that among the 10,000 most AI-cited sources it analyzed, over 40% contained affiliate links or sponsored attributes.

This data comes from the partner's own research and cannot be used to conclude that paid content necessarily leads to AI recommendations. AI answers are also influenced by question phrasing, region, time, model version, and retrieval mechanisms, making brand mentions inherently unstable.

For businesses, generative search optimization is more like an emerging content and data capability than a new ad product where placement can be purchased outright.

Companies are re-evaluating AI investment returns

After the shift at the entry point, the practical question for enterprises is no longer how many more AI features they can add, but what business opportunities these capabilities correspond to, whether they serve the company's operational priorities, and what metrics should be used to judge effectiveness.

"The core of most conversations is no longer just solving pain points, but how to seize opportunities," Justin Morrison told the audience.

According to his observations, when executives evaluate marketing technology providers, they not only ask which AI capabilities the product currently uses, but also assess the provider's understanding of industry changes, its product roadmap, and whether these capabilities align with the company's core priorities and performance metrics.

AI has thus evolved from a point feature into one factor by which enterprises judge a vendor's long-term capabilities.

Justin Morrison believes AI can improve the efficiency of discovery and matching. Jennifer Zhang adds that brands also want to use AI to analyze their presence in large model answers and use cited content to identify partners who may be influencing recommendations. These applications address information overload and selection efficiency.

But finding potential opportunities is only the front end of the growth chain. In an environment where capital costs are high and marketing budgets demand measurable results, brands must still determine whether partnerships can balance brand awareness with revenue conversion.

AI mention rates, answer rankings, content reach, and partner counts can reflect process changes, but they cannot replace operating metrics like new customers, sales revenue, customer acquisition cost, and retention.

Especially when AI answers and review articles influence consumers, but the final transaction occurs on the official website or an e-commerce platform, a single last-click attribution cannot fully explain the contribution of front-end content.

Evaluating AI returns also requires clarifying the time frame for observing results.

Platform onboarding, partner recruitment, first orders, and establishing stable market influence each occur at different stages. If measured within the same cycle, the speed of technology deployment, short-term conversion effects, and long-term brand building risk being conflated. Justin Morrison notes that from project initiation to the first customer or first order typically takes weeks to several months.

Jennifer Zhang explains that established brands with existing awareness and credibility may see preliminary results within one to two months, while smaller brands with lower visibility may need six months, a year, or even longer to build partner responsiveness and market influence.

Therefore, AI can improve information analysis and partner matching efficiency, but platform launch, first conversions, and brand building cannot all be compressed into a single cycle.

Enterprises need to monitor short-term orders while also assessing whether partner networks and brand awareness can accumulate sustainably, in order to determine whether AI-driven opportunities are truly translating into business returns.

From clicks to recommendations: attribution changes are forcing organizational restructuring

When a single purchase decision is influenced by AI answers, creator content, affiliate links, and user referrals simultaneously, attribution challenges become organizational challenges. Which team manages a given partner, and should its contribution be counted toward brand exposure, content marketing, or sales conversion?

Jennifer Zhang points out that the boundaries between different partner types are blurring. Influencers may simultaneously participate in affiliate marketing, while media partners may also produce content and manage social media accounts.

If companies continue to organize teams strictly by channel, the same partner may be contacted by multiple departments, making it difficult to compare data under a unified standard.

This issue has already entered the realm of internal resource allocation.

Jennifer Zhang observes that many companies still maintain separate teams for email, influencer marketing, and search, and some have not even genuinely launched KOC (Key Opinion Consumer) or consumer referral programs. Fragmented teams can lead to overlapping partner resources and unreasonable KPI settings.

She characterizes this as companies "setting up a team for the sake of having that model, rather than structuring it around what actually delivers results." The adjustment is not just about merging departments but repositioning organization, data, and budgets under outcomes such as customer acquisition, retention, and revenue growth.

At the executive level, Justin Morrison believes any company's core mission comes down to acquiring customers, retaining them, and driving revenue growth. In traditional functional structures, customer acquisition typically falls under the Chief Marketing Officer (CMO), while retention and revenue growth belong more to the Chief Revenue Officer (CRO), though the exact configuration depends on the company's customer type and business focus.

Jennifer Zhang predicts that as marketing departments are increasingly required to adopt commercial and sales thinking, some senior roles may merge over the next three to five years. She further suggests that if she were a CEO, she would fold the CMO role into the CRO or have the CMO report to the CRO.

From product export to local operations: AI does not shorten the trust cycle

For Chinese companies, the shift in the AI entry point is occurring alongside another transformation: many firms already possess product, supply chain, and rapid iteration capabilities, but moving from completing exports to operating overseas brands still requires solving local communication, community relations, and management delegation issues.

Justin Morrison believes Chinese brands have advantages in product innovation and speed to market, but achieving scale in global markets requires building brand trust and deeper customer relationships.

Jennifer Zhang describes the current phase as early-stage exploration. New brands need to build awareness, while companies operating for years—even with supply chain and production advantages—must adapt their brand messaging to target markets. Products and marketing methods validated domestically cannot simply be replicated overseas.

She cites the example of a tattoo equipment company entering the U.S. market. The company discovered that its target users were concentrated in communities of tattoo artists, music events, bars, and street culture—not traditional industry trade shows.

This case reflects not merely channel selection tactics but the prerequisite for localization: understanding users' lifestyles and community structures before deciding whom to partner with and what content to use for communication. AI can assist with information sorting and target identification, but it cannot replace frontline market judgment.

Another barrier to local operations lies within the organization.

Jennifer Zhang observes that some Chinese companies hire overseas employees but rarely bring local talent into core management positions. Others have pulled operations back under domestic teams due to differences in work pace and management culture.

Whether overseas teams can participate in decision-making, access resources, and take responsibility for results directly affects a company's responsiveness to local market changes. Staffing local personnel only at the execution level may not achieve true organizational localization.

These observations point to a shift in the competitive foundation of Chinese companies going global.

With product efficiency and channel investment, companies can enter markets quickly. But as operations scale, competition extends to brand expression, third-party content, community relationships, and customer retention.

AI has changed how consumers access information and has made information gaps and expression misalignment in overseas markets more likely to directly affect whether a brand enters recommendation scopes.

Over a longer horizon, AI has not eliminated the gap between product globalization and brand globalization. It can compress the time needed for content production, information analysis, and resource matching, but it cannot compress the accumulation of cultural understanding, product validation, and user trust.

For Chinese enterprises, the next stage is not just about selling more products overseas, but whether they can convert supply chain and innovation advantages into stable brand recognition and local operating capabilities.