Process Standardization through well-defined work instructions:

• Provides a framework for establishing standardized processes and procedures for AI development. It helps define best practices for data management, model development, testing, deployment, and maintenance.
• Ensures consistency and repeatability in AI development, leading to improved quality and efficiency.

Risk Management:

• Identify and mitigate risks associated with AI development and incorporation within well-established systems. It involves conducting risk assessments to identify potential hazards or vulnerabilities in AI systems and implementing measures to mitigate those risks.
• Addressing issues related to data quality, model bias, security, privacy, and ethical considerations.

Documentation and Traceability:

• Emphasizes the importance of documentation and traceability in AI development.
• Maintaining detailed documentation of the development process, including data sources, model architectures, algorithms, and version control. Proper documentation enables traceability, reproducibility, and auditability of AI systems.
• Includes well-defined and updated quality forms, work instructions, policies etc.