AI in Enterprise Context

Adding AI to existing systems requires careful planning. The goal isn't to add AI for its own sake, but to solve real business problems.

Use Cases That Add Value

• Document processing: Extracting data from invoices and forms
• Search enhancement: Semantic search for finding relevant information
• Automation: Predicting outcomes and suggesting actions
• Anomaly detection: Identifying unusual patterns

Architecture Approach

class PredictionService:
    def __init__(self, model_path: str):
        self.model = load_model(model_path)

    async def predict(self, features: dict) -> Prediction:
        return self.model.predict(features)

API Integration

AI services should be exposed through clean APIs:

@Injectable()
export class AIService {
  async analyzeDocument(file: File): Promise<Analysis> {
    const response = await this.http.post('/api/ai/analyze', file);
    return response.json();
  }
}

Considerations

1. 1Data privacy: Ensure sensitive data is protected
2. 2Performance: AI inference can be slow—consider async processing
3. 3Fallbacks: What happens when AI is unavailable?
4. 4Monitoring: Track AI accuracy over time

Starting Small

Begin with a single, high-impact use case. Measure results, learn, and expand.