Over the past year, enterprise large-model applications have progressed from the question of "can they be used" to "are they being used well." However, a common reality persists: while model capabilities are evolving rapidly, relatively few businesses have truly integrated AI into their operations and achieved tangible results. During collaborations with clients, data intelligence company Merit Interactive Co.,Ltd. (Stock Code: 300766) has observed that in many enterprise scenarios, the final hurdle blocking AI implementation is often not the model itself, but the data. This article aims to break down three key points: why increasing model power doesn't necessarily translate to improved business outcomes, where the root cause actually lies, and what a viable solution path looks like in practice.

The AI responds with a seemingly professional answer, but why can't the business actually use it? An operations director at a client company noticed a 12% decline in core conversion rates last month. She opened the AI assistant and asked, "Why did the conversion rate drop last month?" The AI quickly produced an answer: it listed data change points, cited industry attribution frameworks, and suggested "further investigation into channel traffic and user retention." It sounds professional, but upon closer inspection, the answer is completely detached from reality. This is because the AI doesn't know that this company's "conversion rate" is based on GMV excluding cancellations under a payment-basis calculation, not the industry-standard order-basis metric. It doesn't know that a new promotional campaign was launched last month, making short-term fluctuations an already explained occurrence. Furthermore, it doesn't know that a similar fluctuation happened three months prior, when the conclusion was that competitor subsidies had diverted traffic. These three "unknowns" render the AI's answer seemingly correct but practically unusable. It summarizes "what happened," but cannot answer "why it happened" or "what to do next." If the AI had access to this business data, its answer would be: "The 12% drop in conversion rate is primarily due to GMV fluctuations under the payment-basis calculation caused by traffic structure changes from the new promo campaign. This bears an 82% similarity to the competitor-subsidy event from three months ago. We recommend referencing the response strategy used back then." For the same question, the difference isn't just accuracy; it's whether AI can truly enter business decision-making. This scenario reappears frequently across the retail, finance, and internet sectors we serve. The question business leaders ultimately ask is simple: the model is smart, but its answers have nothing to do with my business.

The real bottleneck lies not in the model. Since 2025, reasoning capabilities, context length, and multimodal understanding in leading models have all improved rapidly. For the vast majority of enterprise application scenarios, the model is no longer the primary constraint. However, one reality is that the stronger the model, the higher the expectations for implementation outcomes, making the gap caused by insufficient data and business foundations even more pronounced. Research from multiple industry institutions indicates that over half of enterprise AI projects are delayed or fail to meet expectations, primarily not due to inadequate model capability, but rather poor data quality and governance. The limiting factor is shifting from model selection to enterprise data readiness. Merit Interactive Co.,Ltd. attributes this to the fact that large model training data originates from publicly available internet information. Yet, a vast amount of core, decision-valuable enterprise data does not exist publicly online—such as transaction records, user behavior logs, contract terms, approval workflows, and operational strategies. This private data resides only within the enterprise, and the model has never seen it. Besides being "unobtainable," a more common issue is that it's "unusable"—meaning AI cannot effectively leverage these valuable private datasets. Merit Interactive Co.,Ltd. identifies three underlying reasons. First, business semantics do not align. Every industry has its unique metrics and rules. While working with a chain retail client to organize their membership system, we encountered this: the model knows GMV means "gross merchandise value," but doesn't know if this enterprise's GMV includes tax, or whether it's an order-basis or payment-basis figure. This single difference could lead subsequent analysis conclusions to deviate significantly from reality. Second, data is scattered everywhere. One operations lead noted, "I asked the AI about today's DAU trend, and it analyzed the industry average for the same period last year. What I wanted to know was why today is different from yesterday." Model training data has a knowledge cutoff; without real-time connection to the enterprise's data sources, it cannot grasp the day's operational situation. Data spread across CDP, CRM, and BI systems cannot be uniformly supplied. Third, context is missing. Information like "why was this campaign run," "what does this metric mean," or "how was a similar issue resolved last time" is scattered across documents, emails, meetings, and approvals, rarely structured or governed. In the enterprises we've engaged with, contextual data is currently the most prominent shortfall in AI implementation. When we built the first version of our AIBI, we assumed simply integrating data would suffice. But when clients asked "how is this metric calculated," we couldn't answer because we hadn't even clarified the definitions ourselves. That lesson made us realize that connecting data is just the starting point; making AI understand the data is the real challenge.

RAG alone is far from sufficient. The industry's first instinct is RAG, turning enterprise documents into a retrievable knowledge base for the model. But RAG primarily addresses information retrieval. When an operations lead asks why the conversion rate dropped 12%, AI needs to complete five steps: first, understand the intent of the question; second, find the relevant data; third, accurately query and compute; fourth, attribute causes based on business rules; and fifth, provide actionable recommendations. RAG covers the first two steps, but the remaining three require deeper capabilities. Enabling AI to effectively use enterprise data goes beyond simply "writing SQL." Through the development of and client service practices with its AIBI intelligent operations product, Merit Interactive Co.,Ltd. has broken this down into three layers to help AI understand and utilize your data. This isn't just about "how to query," as NL2SQL technology already handles natural language to query conversion reasonably well. The harder part is "what to query" and "whether the results are usable." Data scattered across various systems leaves business users unaware of what's available or whether definitions align across systems. If AI tells operations that "30% are high-consumption potential users," the operations team needs to know: how is this label defined? Are these the same people as the "high-value users" defined in my app? What this layer aims to do is transform metric definitions, calculation logic, and historical attributions into structured knowledge that the model can access. It's not about creating a data dictionary; it's about making the model truly understand your data. The second layer involves enabling AI to analyze according to business logic—not through a single model call, but via a coordinated orchestration where a large model understands intent and reasons, a smaller model generates SQL for queries, and rule engines validate metrics for certainty. This gives AI access to real-time business signals rather than relying solely on static snapshots from training. The third layer focuses on helping AI accumulate and reuse experience. A validated analysis path can be stored for similar future tasks; a proven operational strategy becomes reusable knowledge recommended in analogous scenarios; common data processing flows are mastered by AI as skills, automating them next time. Experience no longer leaves with employees, and organizational capability no longer depends on individuals. Humans handle thinking and decision-making, delegating repetitive, experience-driven tasks to AI.

From capability to product, the motivation behind Merit Interactive Co.,Ltd.'s AIBI is to translate these capabilities into practical solutions. Its positioning for AIBI is "data-savvy, operations-focused, and knowledge-accumulating," targeting operational scenarios. It's not just another conversational bot; it front-loads multi-source data integration and semantic alignment, enabling business users to engage in a full loop through natural language—from conversational data retrieval, crowd insights, and strategy recommendations, to crowd selection and user outreach. Simultaneously, it supports enterprises in accumulating data definitions, rules, and past analysis experience, making answers more attuned to the specific business context. With each use, it continuously improves, becoming increasingly familiar with your business. These foundational capabilities rest on a complete AI system—knowledge base, retrieval, memory, workflow orchestration, and a skill marketplace—which isn't a standalone product but the underlying support for the entire framework. AIBI operates at the application layer, with this system at its core, jointly addressing the root issue hampering enterprise AI implementation: data. Returning to the opening scenario: if the enterprise's data is understood by AI, its experience is accumulated, and its metrics are defined, the answer to the same question would be what the business actually needs. What determines AI implementation success is data readiness. Models will continue to become more powerful, but that's beyond an enterprise's control. What is controllable is whether the data you hold can be utilized—and utilized well—by AI. At this point, data readiness becomes the differentiating moat for enterprise AI implementation outcomes. This disparity in data readiness dictates the varying effectiveness of the same model across different companies. This is the true variable and the last mile for data-driven growth. Merit Interactive Co.,Ltd. believes that the ceiling for unlocking data value isn't a limit of the data itself, but rather the limit of organizational governance capability. Data existing but remaining unused points not just to the presence of data, but to the effectiveness of its governance and utilization. The goal of governance has also shifted from "storage and management" to "understanding and usage." Crossing that final mile isn't about having more data; it's about ensuring data can be understood and invoked by AI.