In a globally positioned industrial manufacturing enterprise with annual revenues reaching tens of billions of yuan and a long-standing leadership position in its niche market, efficiency had long been a competitive advantage. Over the past decade, the company continuously reduced costs and improved delivery performance through lean manufacturing, ERP systems, and automation equipment.
Yet by 2024, the management team began to detect a worrying signal: the marginal returns generated by traditional efficiency tools were rapidly diminishing.
The external environment had not changed dramatically, but it had become markedly more complex. Customer demand was increasingly customized, delivery cycles continued to compress, and supply-chain uncertainty accumulated with greater frequency. Internally, data volumes surged, but decision-making speed did not. On the contrary, quotation cycles lengthened, cross-department communication costs rose, and critical judgments relied ever more heavily on individual experience. The once-reliable efficiency advantage began to erode.
The real crisis was not technological backwardness, but a structural misalignment between organizational cognition and intelligent capability.
The enterprise possessed abundant systems, tools, and data, yet lacked an intelligent decision-making capability that could run end to end across the entire process.
Problem Recognition and Internal Reflection: When Data Fails to Become Judgment
The turning point did not stem from a single failure, but from a series of issues that appeared normal in isolation yet accumulated over time.
During an internal review, management identified several persistent problems:
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The quote-to-order process involved an average of six systems and five departments.
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More than 60% of inquiries required repeated manual clarification.
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Decision rationales were scattered across emails, spreadsheets, ERP notes, and personal experience, with no reusable knowledge structure.
These observations closely echoed BCG’s conclusion in Scaling AI Requires New Processes, Not Just New Tools:
Traditional automation delivers only incremental improvements and cannot break through structural bottlenecks at the process level.
Independent assessments by external consultants reinforced this view. The company did not lack AI tools; rather, it lacked process and organizational designs that allow AI to truly participate in the decision-making chain.
The core constraint lay not in algorithms, but in workflows, knowledge structures, and collaboration mechanisms.
The Turning Point and the Introduction of an AI Strategy: From Tool Pilots to Process Redesign
The decisive inflection point emerged during an evaluation of customer attrition risk. Because quotation cycles were too long, a key customer redirected orders to a competitor—not because of lower prices, but due to faster and more reliable delivery commitments.
Management reached a clear conclusion:
If AI remains merely an analytical aid and cannot reshape decision pathways, the fundamental problem will persist.
Against this backdrop, the company launched an AI strategy explicitly aimed at end-to-end process intelligence and chose to work with HaxiTAG. Three principles were established:
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No partial automation pilots—the focus must be on complete business processes.
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AI must enter the decision chain, not remain confined to reporting or analysis.
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Process and organization must be redesigned in parallel, rather than technology advancing ahead of structure.
The first deployment scenario was precisely the one emphasized repeatedly in the BCG report—and the one the company felt most acutely: the quote-to-order process.
Organizational Intelligence Rebuilt: AI Agents at the Core of the Process
Within HaxiTAG’s Bot Factory solution, AI was no longer treated as a single model, but as a collaborative system of multiple intelligent agents embedded directly into the process.
Process-Level Redesign
Leveraging the YueLi Knowledge Computation Engine and the company’s existing systems, HaxiTAG Bot Factory helped establish four core AI agents:
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Assessment and Classification Agent: Automatically interprets customer inquiries and structures requirements.
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Recording Agent: Synchronizes order information across multiple systems.
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Status Agent: Tracks process milestones in real time and proactively pushes updates.
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Lead-Time Generation Agent: Produces explainable delivery forecasts based on historical data and capacity constraints.
While this structure closely resembles the BCG case framework, the critical distinction lies here:
these agents do not operate in isolation but collaborate within a unified orchestration and governance framework.
Organizational and Knowledge Transformation
Correspondingly, internal working patterns began to shift:
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Departmental coordination moved from manual alignment to shared knowledge and model-based consensus.
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Data ceased to be repeatedly extracted and instead accumulated systematically within the EiKM Knowledge Management System.
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Decisions no longer relied solely on individual experience but adopted a dual-validation mechanism combining human judgment and model inference.
As BCG observed, true AI scalability occurs at the level of processes and organization—not tools.
Performance and Quantified Outcomes: From Efficiency Gains to Cognitive Dividends
Six months after implementation, a comprehensive evaluation yielded clear, restrained results:
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Approximately 70% of inquiries were processed fully automatically.
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20% entered a human–AI collaboration mode, requiring only a single human confirmation.
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10% of highly complex orders remained human-led.
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The quote-to-order cycle was shortened by 30–40% on average.
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Redundant communication workloads across sales and operations teams declined significantly.
More importantly, management observed a subtle yet decisive shift:
the organization’s responsiveness to uncertainty increased markedly, and decision friction fell appreciably.
This represented the cognitive dividend delivered by AI—not merely higher efficiency, but enhanced organizational resilience in complex environments.
Governance and Reflection: When AI Enters the Decision Core
Throughout this journey, governance concerns were not sidestepped.
HaxiTAG embedded explicit governance mechanisms into system design:
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Full traceability and explainability of model outputs.
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Clear accountability boundaries—AI does not replace final human responsibility.
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Continuous audit and review enabled through process logs and knowledge version control.
This aligns closely with the BCG-proposed loop of technology evolution, organizational learning, and governance maturity.
AI was not deployed as a one-off initiative, but as a system continually constrained, calibrated, and refined.
Appendix: AI Application Impact in Industrial Quote-to-Order Scenarios
| Application Scenario | AI Capabilities | Practical Effect | Quantified Outcome | Strategic Significance |
|---|---|---|---|---|
| Inquiry Interpretation | NLP + Semantic Parsing | Structured requirements | 70% automation rate | Reduced front-end friction |
| Order Entry | Multi-system agents | Less manual work | Reduced labor hours | Greater process certainty |
| Status Tracking | Event-driven agents | Real-time visibility | Faster response times | Stronger customer trust |
| Lead-Time Forecasting | Rule–model fusion | Explainable predictions | 30%+ cycle reduction | Higher decision quality |
An Intelligent Leap Enabled by HaxiTAG Solutions
This is not a story about “adopting AI tools,” but about intelligent reconstruction from within the process itself.
In this transformation, HaxiTAG consistently focused on three principles:
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Embedding AI into real business processes, not leaving it at the analytical layer.
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Turning knowledge into computable assets, rather than fragmented experience.
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Enabling organizations to learn continuously through intelligent systems, rather than relying on one-off change.
From YueLi to EiKM, from a single scenario to full end-to-end processes, the true value of intelligence lies not in dazzling technology, but in whether an organization can regain its regenerative capacity through it.
When AI ceases to be merely a tool and becomes part of the process, genuine enterprise transformation begins.
