Lingyang at Qwen Conference Singapore: AI Employees Need to Pass the Enterprise-Grade Test Before Going Live
TL;DR: Generic Agent platforms are getting stronger, but what makes AI "trustworthy" in enterprises is not the model itself—it's whether AI can digest years of accumulated data, metrics, permissions, and business semantics. Lingyang Quick BI solves this by embedding trusted indicators and governance into AI agents, compressing the entire workflow from "discovering anomalies" to "driving downstream system execution." Real cases: 90% reduction in manual daily reporting for e-commerce operations, cross-border sales attribution compressed from a week to minutes, and 50%+ less manual verification work during BI platform migration.
On May 26, 2026, the Qwen Conference Singapore, hosted by Alibaba Cloud, officially kicked off. As Alibaba Cloud's flagship event for global developers and enterprise customers, this year's conference focused on the real-world implementation of large models and Agent applications in enterprise scenarios. At the Agent Application Forum, Pan Ruochen, Solution Architect at Lingyang, delivered a keynote presentation—"Quick BI: Your AI Data Analyst, From Insights to Action"—introducing Quick BI's latest evolution in Agentic analytics.
The Consensus Forming at Qwen Conference
A consensus is emerging at the conference: generic Agent platforms are becoming increasingly powerful, but the differentiated capability that truly makes AI "safe to use" in enterprises has never been about the model itself. It's about whether enterprise data, metrics, permissions, and business semantics—accumulated over years—can be "digested" by AI.
This is exactly where Quick BI differs most from generic Agent platforms:
- Generic platforms solve "can talk"
- Quick BI solves "speaks correctly, speaks accurately, speaks in a way that can be executed"
While everyone is racing to chase stronger models, Lingyang chose to invest deeper into enterprise-grade data capabilities—a harder path, but one with a stronger moat.
"In the AI era, enterprise-grade analytics is no longer about competing on model capabilities. It's about who can feed the Agent with the enterprise's accumulated metric systems, permission governance, and business semantics—so the Agent's answers are both trustworthy and directly actionable." — Pan Ruochen
Where Quick BI's Capability Comes From
Unlike generic Agents, Quick BI's capabilities come from Lingyang's years of accumulated:
- Trusted metrics (可信指标)
- Permission governance (权限治理)
- Business semantics (业务语义)
This means the "AI Data Analyst" doesn't just give answers that "look about right"—it aligns completely with the enterprise's real metric systems, permission boundaries, and business context. The answers are correct, accurate, and actionable.
More importantly, it doesn't serve isolated analysis tasks. It's embedded into business workflows across operations, sales, supply chain, and finance:
- Discover anomalies
- Locate root causes
- Generate recommendations (allocation, alerts, replenishment)
- Drive downstream system execution
Every step in this chain is compressed.
Three Real Enterprise Workflow Efficiency Cases
Case 1: E-commerce Operations — Manual Daily Reporting Down ~90%
E-commerce operations teams traditionally spend hours compiling daily reports from multiple channels. Quick BI's AI agent automates this, reducing manual effort by approximately 90%.
Case 2: Cross-Border Sales — Attribution Compressed from a Week to Minutes
What used to take a week of manual sales attribution across regions and product lines can now be completed in minutes through AI-driven analysis with trusted metrics.
Case 3: BI Platform Migration — Manual Verification Work Down 50%+
During BI platform migrations, technical teams typically spend enormous effort manually verifying data consistency. Quick BI's semantic governance and trusted indicators cut this manual verification workload by more than 50%.
The Future: Not Just One AI Employee, But AI Teams
In the future, enterprises won't have just one AI employee. Customer management, procurement, finance, customer service—every role will have its own "AI employee." They all need to collaborate on the same trusted data foundation, otherwise you'll end up with "the same sales number, three Agents giving three different answers."
What Lingyang is doing is opening up its years of accumulated trusted metrics, permission systems, and business semantics—so every AI employee in the enterprise can answer questions and collaborate on the same data foundation. This is the fundamental prerequisite for overall workflow efficiency gains.
The Real Value: AI Makes Enterprise Data Assets Actionable
AI hasn't devalued data. Instead, it's made the data assets enterprises have accumulated over the past decade+ directly callable by business operations for the first time. This is the real value Quick BI wants to amplify in the AI era.
Quick BI, with 9 available regions overseas and 10,000+ enterprise customers, is continuing to export "enterprise-grade analytics hard skills"—trusted metrics, semantic governance, permission compliance—to the outside world. This ensures every AI employee in the enterprise answers questions and drives actions on the same trusted data foundation.
Next phase: Lingyang will continue to work with more ecosystem partners in key overseas expansion regions—Southeast Asia, Middle East, Japan—to make "from insights to action" a daily reality for enterprises.
This article is based on the keynote presentation at Qwen Conference 2026 Singapore (May 26, 2026).
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