1. Determine Critical Areas of Code.

Not everything in the code needs AI analysis. Pay attention to high-impact modules, which include authentication, payment processing, database access layers, and APIs. Focusing on these points will make AI tools identify possible risks where they are most important to enhance their reliability and security.

2. Connect with CI/CD Pipelines.

To ensure quality control, AI-based code review systems should be deployed in DevOps pipelines. Each commit or pull request can automatically run a scan, providing instant feedback and reducing manual inspection bottlenecks. This process becomes seamless with tools compatible with Azure DevOps and GitHub Actions when working on .NET and ASP.NET Core projects.

3. Integrate Artificial Intelligence and Human Control.

Although AI enhances the speed of the code review process, human reviewers are crucial in complex logic and architectural decision-making as well as workflows that are critical to the business. 

4. Establish Feedback Loops

AI tools are enhanced with time, based on past reviews of code. A well-structured feedback loop can enable AI models to constantly improve detection algorithms according to approvals, rejections, and corrections by developers. 

5. Measure Impact with KPIs

Key performance indicators (KPIs) that should be tracked by the enterprises to assess the performance of AI code review implementation are:

• Bugs reduction: Issues per pre- and post-AI integration.
• Improvement of Cycle Time: Saving time in the review and deployment process.
• Developer Productivity: Time saved in the manual code review.
• Code Quality Metrics: Standards compliance, maintainability, and reduction of security risk.
  
  

6. Training and Onboarding

Code review tools based on AI demand developers to know how to interpret AI feedback and implement suggestions to their advantage. Frequent training, documentation, and coding workshops are used to make teams as effective as possible in utilizing the tool.