When AI Meets ERP: The First Mile of Enterprise Process Automation
ERP systems record a company's core operational logic. But these data are often vast, scattered, and hard to apply. The role of AI is not to replace ERP but to make this data "understood, analyzed, and used" in practice.
The first step in introducing AI involves "data entry," structuring and intelligently applying ERP data.
Why Not Integration?
ERP integration has high barriers and costs for maintenance. Each ERP system architecture is different, so rather than force integration, we opt for "adjacent data" analysis and application:
- Exporting data tables and reports to AI-readable formats (CSV, JSON, semantic summaries)
- Using ETL tools (Extract-Transform-Load) to regularly update the analysis database
- Inputting these data into a private language model as background knowledge base
Feasible Application Scenarios
- Automated operational reports: generating text summaries based on shipment, sales, and inventory data
- Abnormal detection: AI can automatically analyze anomalies in cost structure or sales trend patterns
- Query-based queries: managers can ask "what items have low stock this week?" using natural language
- Process decision suggestions: predicting the optimal purchase time based on historical data
NT Tech's Implementation Approach
NT Tech focuses on AI applications that are data-driven, without disrupting existing ERP structures:
- Customizing the data extraction process to convert data into formats understandable by language models
- Combining knowledge base construction and semantic embedding techniques to build intelligent query platforms
- No need for integration with ERP; simply provide reports or export forms for analysis
The data in ERP hold operational wisdom, and AI enables its value realization. Starting from the process of data entry and analysis, we gradually move towards genuine intelligent decision support.
