Implementing AI in enterprise apps needs more than also modules of adding entities, it needs planning, architecture development, and tools. Some best practices that can be taken note of include the following:

1. Start Small with MVPs

Start with a Minimum Viable Product (MVP) before implementing AI in massive systems of enterprises. Select one aspect, including customized dashboards or recommendation engines and expand slowly. This makes it less risky and gives a possibility to test AI effectiveness in the real environment.

2. Use Modular Architecture

Using Blazor WebAssembly and backend running on ASP.NET Core it is better to keep AI services modular. Use AI features as microservices or standalone Web APIs, which means it is easier to update models, inject more performance, and scale without differing with the core application.

3. Continuous Model Training

The behavior of the users changes with time. Periodically refresh/train AI/ML models to ensure results are accurate and relevant. Perpetual retraining and deployment of artificial intelligence models in business applications is enabled by the integration of the services of the Azure ML.

4. Optimize for Performance

The AI models can be resource-intensive. Enhance the model performance with caching predictions, requests batching, and lightweight models on clients applications in Blazor. This makes sure that the applications are responsive with real time AI predictions.

5. Guarantee the Security and Compliance.

Security is important when handling each enterprise data. Introduce access administration, data crypting, and GDPR data encryption. The API and cloud integration with either the Azure or the AWS Workset assists in protecting sensitive information while exploiting the AI capabilities.