The AI Revolution in ERP
Enterprise Resource Planning systems have traditionally been data-heavy, process-driven applications that require significant training to use effectively. With the introduction of AI capabilities — particularly Microsoft’s Copilot — Business Central is undergoing a fundamental transformation in how users interact with their data.
What Is Copilot in Business Central?
Copilot in Business Central is an AI-powered assistant that helps users complete tasks faster and make better decisions. It leverages large language models to understand natural language queries and generate actionable insights from your business data.
Current Copilot capabilities include:
- Marketing text generation — automatically create product descriptions from item attributes
- Bank reconciliation assistance — AI matches transactions with ledger entries
- Sales line suggestions — recommend items based on historical orders
- Late payment predictions — forecast which customers are likely to pay late
- Inventory forecasting — predict stock levels and suggest reorder points
Building Custom AI Features
Beyond the built-in Copilot features, developers can create custom AI-powered extensions. The key components are:
Azure OpenAI Service Integration
Business Central can connect to Azure OpenAI Service to leverage GPT models for custom scenarios:
- Document summarization for posted invoices and orders
- Natural language search across master data
- Automated classification of incoming vendor documents
- Intelligent data entry suggestions based on patterns
AL and AI Toolkit
Microsoft provides the AI toolkit for AL developers, which includes:
System.AImodule for connecting to Azure OpenAI- Prompt management and template system
- Token usage tracking and governance controls
- Built-in safety filters and content moderation
Real-World Use Cases
Here are some practical scenarios where AI adds value in a Business Central environment:
Intelligent Document Processing
Instead of manually entering data from scanned invoices, AI can extract key fields (vendor name, amounts, line items) and pre-populate purchase invoices. This reduces data entry time and minimizes errors.
Predictive Analytics for Demand Planning
By analyzing historical sales data, seasonality patterns, and market trends, AI models can generate more accurate demand forecasts. This helps businesses optimize inventory levels and reduce carrying costs.
Natural Language Reporting
Users can ask questions like “What were our top 5 customers by revenue last quarter?” and receive formatted reports without needing to know the technical query language or report builder.
Challenges and Considerations
While AI brings powerful capabilities, there are important factors to consider:
- Data quality — AI models are only as good as the data they’re trained on
- Privacy and compliance — ensure your AI usage complies with GDPR and industry regulations
- Cost management — API calls to Azure OpenAI have associated costs that scale with usage
- User trust — employees need to understand AI limitations and verify critical outputs
Looking Ahead
The integration of AI into ERP systems is still in its early stages. We can expect to see more sophisticated features in the coming releases, including:
- Multi-modal AI that can process documents, images, and voice
- Autonomous agents that can execute multi-step business processes
- Cross-application AI that connects insights across the Microsoft 365 ecosystem
- Industry-specific AI models fine-tuned for manufacturing, retail, and services
Key Takeaways
- Copilot in Business Central is transforming how users interact with ERP data
- Developers can build custom AI features using Azure OpenAI and the AL AI toolkit
- Practical use cases include document processing, demand forecasting, and natural language reporting
- Data quality, compliance, and cost management are critical considerations