Enterprise AI Agent: The Advanced Tool for Data Insights in Global New Brands

[Ebrun Original] In the increasingly competitive blue ocean of cross-border e-commerce, new brands are born and die every day. In 2025, China's cross-border e-commerce import and export volume reached 2.75 trillion yuan, an increase of 69.7% compared to 2020. However, over 70% of new brands face growth stagnation within three years. When we delve into the problems of these brands, we find a common crux: when facing the global market, the data insight capability within the competitiveness of enterprise data elements needs improvement.

I. The Challenge: The Contradiction Between Data Overload and Lack of Insight

A manager of a home furnishing cross-border e-commerce brand described his daily routine: 'We have data reports from over a dozen platforms like Amazon, Shopify, and Google Ads, reviewing nearly a hundred metrics daily. But when I genuinely want to know why Q3 sales dropped in the German market, it takes the team three days to give me a vague answer.' This highlights the core pain point for current global new brands – the structural contradiction between 'business personnel struggling to use data' and 'overwhelmed data engineers.' This contradiction is particularly acute in cross-border e-commerce:

On one hand, business personnel are drowning in a sea of data. Operations, marketing, and product teams receive vast amounts of data daily but lack the tools and capability to translate it into action. On the other hand, data teams are reduced to reporting machines. Small cross-border e-commerce enterprises often have only 1-2 data personnel, spending 80% of their time handling ad-hoc data requests. Finally, delayed decision-making becomes the norm. It takes weeks or even months on average to go from data anomaly to insight generation, while changes in the cross-border e-commerce market often occur weekly or even more frequently.

A beauty brand focusing on pure natural ingredients quickly gained popularity in European and American markets through TikTok marketing. In June 2025, its US sales suddenly dropped by 30%. The team spent a week analyzing and finally discovered it was a supply chain issue: international shipping delays for a key ingredient caused production delays, affecting promotional stock. However, this insight came too late. From identifying the problem to finding the cause and resolving it, the brand missed the current holiday sales window, losing significant market share. This case exposes the fatal flaws of traditional data analysis: data silos separate supply chain data from sales data; analysis speed cannot keep up with business pace; weak attribution capability makes it difficult to quickly pinpoint the root cause.

II. The Solution: The Convenience and Power of Enterprise AI Agents

Enterprise AI Agents are designed for business managers without a digital technology background, intended to act as an 'intelligent data assistant' for every business manager. They transcend the limitation of traditional data tools that merely 'present results,' aiming to proactively understand the business, conduct deep analysis, and drive decision execution. Their core value, or product advantage, is mainly reflected in three aspects:

First, Conversational Result Retrieval – 'Ask the AI like you would ask a colleague.'

Traditional data analysis requires technically skilled managers to 'translate' business data into instructions understandable by technical staff (e.g., submitting a request form), a process that is time-consuming and prone to information distortion. In contrast, an AI Agent allows you to ask questions using the most natural everyday language.

For example, previously, if you noticed poor performance of a new smart water bottle in the French market, you needed to convene a meeting with the data team, describe the problem, wait for them to break down the requirements, write code, and run queries, potentially receiving a basic report days later. Now, you simply type or ask by voice in a chat box: 'Why is the response to our newly launched smart water bottle lukewarm in the French market?'

The AI Agent will automatically understand the core of your question and immediately plan and execute a complete analysis, directly providing an insightful description integrating multi-dimensional reasons. For instance: 'Analysis reveals the main issues are slow video loading speeds on the product page, leading to a 40% lower conversion rate compared to the German market; simultaneously, a major local competitor recently implemented a 15% price reduction promotion.'

Second, Automated Root Cause Analysis – 'Finding the real cause of the problem.'

Current enterprise AI Agents can not only answer your actively posed questions but also proactively monitor key business metrics 24/7. Once an anomaly is detected (e.g., a sharp sales drop, surge in negative reviews), it automatically initiates a deep investigation process, simulating the analytical approach of a skilled analyst, connecting data scattered across different departments to get to the heart of the problem.

Previously, if the customer service department reported increased negative reviews for tent products, the supply chain department claimed raw materials were normal, and the operations department saw no sales anomalies, a manager would need to personally lead meetings involving multiple departments, taking days or even a week to determine that a specific batch of raw materials was defective.

Now, the AI Agent automatically detects the anomaly signal 'rising negative review rate for bestselling tents,' then silently performs the following tasks in the background: analyzing review content → identifying keywords like 'bent pole' → tracing the supply chain data for that product batch → pinpointing the issue to a specific supplier's raw material batch → assessing the scope of affected products and sales regions. Within 45 minutes, it directly reports to you: 'Issue confirmed originating from insufficient strength of aluminum in batch #5 from supplier XX. Recommend immediate recall procedure initiation. Estimated affected inventory: 500 units. Potential customer complaint risk flagged.'

Third, Providing Actionable Decision Recommendations – Closing the loop from 'identifying the problem' to 'solving the problem.'

This fully embodies the value of AI Agents. They not only tell managers 'what's wrong' and 'why,' but also generate specific, actionable recommendations based on the analysis results and predefined business logic, even automating some execution, thus forming a complete 'insight-decision-action' loop.

A common past scenario: receiving a report showing 'Canadian site order cancellation rate suddenly increased by 15%, primarily due to logistics delays.' The manager still needed to hold meetings with logistics, customer service, and operations teams to discuss specific countermeasures and assign tasks. Now, with the AI Agent's assistance, after quickly analyzing the main reasons for the logistics delay, it directly appends a clear action list. The manager can approve these recommendations with one click, and the system will automatically or semi-automatically drive task distribution and execution tracking.

III. The Path: Building Data Insight Capability with AI Agents

In the traditional era, building enterprise data insight capability primarily relied on the accumulation of experienced senior talent with broad vision. Current AI Agents, however, potentially lower the barrier to data insight, enabling new cross-border e-commerce brands to build data intelligence capabilities competitive with traditional giants, even with limited resources.

For example, a startup brand's practice involved completing data infrastructure in 6 weeks: unifying data sources – connecting 8 core platforms like Shopify, Google Analytics, Meta Ads; defining key metrics – identifying 15 North Star metrics with clear calculation methods; deploying a basic Agent – enabling natural language queries for core data. The results were clear: management's daily data review time dropped from 2 hours to 15 minutes; the marketing team could self-serve basic reports, with data request response time shortening from days to minutes.

Another medium-sized seller upgraded its home furnishing brand, achieving traceable data validation covering product, marketing, and customer dimensions within 3 months. They established 10 common analysis templates, such as 'Promotional Campaign Review' and 'New Product Performance Tracking,' automating 70% of routine analysis needs. The average time to detect key business anomalies was reduced from 24 hours to 2 hours.

IV. Potential Difficulties, Challenges, and Countermeasures

Certainly, the ideal state is not achieved overnight. Building data insight support through enterprise AI requires foundational work like data acquisition and data governance, which, while basic, yield sustained benefits. A frequently faced challenge is data quality. A common saying regarding AI is 'garbage in, garbage out.' In other words, data quality is crucial for the effectiveness of enterprise AI Agents.

Many advanced enterprises have developed beneficial experiences for improving data quality. For instance, one brand adopted a 'data quality score' system, rating each data source; data below a threshold is flagged with a confidence level during Agent analysis. They also established a data quality monitoring Agent that automatically detects anomalies and triggers cleansing processes. Another enterprise implemented a triple-check system: first, a reasonableness check on results (e.g., sales should not be negative), then manual confirmation for key operations, followed by pre-execution in a sandbox environment. They also set up an 'AI decision traceability' mechanism, allowing the analysis process behind all important recommendations to be retraced.

While specific measures for improving data quality vary among enterprises, the overall methodology is quite clear. Looking ahead, future cross-border e-commerce AI Agents are expected to show three major trends:

1. Multimodal Integration: Capable of analyzing not just numbers but also understanding product images, marketing videos, and user voice feedback.

2. Enhanced Action Automation: Evolving from 'recommendation' to 'limited autonomous execution,' automatically adjusting ad bids, updating product descriptions, and sending personalized emails within defined rules.

3. Exploration of Ecosystem Collaboration: Enabling Agents from different brands to share market insights while protecting data privacy, allowing small brands to access industry-level intelligence. Beyond infrastructure like trusted execution environments, collaborative mechanisms across the industry and supply chain are also important factors.

Conclusion: The New Paradigm of Global Competition in the Era of Data Democratization

AI Agents are bringing a 'data democratization' revolution to new cross-border e-commerce brands. The data insight capability once affordable only to large enterprises is now becoming accessible and democratic through intelligent agent technology. The core value of this transformation lies not only in the technology itself but also in how it redefines corporate decision-making methods, enabling a shift in the overall operational paradigm.

For many early adopters, AI Agents are not just efficiency tools but builders of core competitive advantages. They allow startups to compete with larger players, enable regional brands to compete globally, and ensure product innovation accurately matches market demand. In cross-border e-commerce, one of the most digitally advanced and dynamically changing markets globally, data insight capability has become the new focal point. Enterprise AI Agents are precisely the intelligent systems strengthening this focus. Brands that master this technology first will not only win current market share but also define the intelligent standards for future global trade.

Ebrun Think Tank will continue to focus on the development of the data industry and the cultivation of data-driven enterprises. We welcome in-depth exchanges regarding good experiences and cases, and the Think Tank can conduct comprehensive and in-depth reporting on outstanding examples. Contact email: huangbin@ebrun.com