The development of AI is crucial due to its potential to enhance efficiency in various industries. However, security concerns and social and regulatory consequences are impeding its progress. What governance functions can be implemented in the lifecycle of AI applications to overcome these challenges?
With structured AI lifecycle management, companies can effectively address the growing risks, social and ethical issues and regulatory requirements along the entire lifecycle of an AI application. This ensures compliance with regulations and the responsible use of AI. The AI lifecycle management approach enables the practical implementation and operationalisation of regulatory principles throughout the entire AI lifecycle. At the same time, it provides an overarching framework for complying with functional and regulatory requirements. This enables companies to build trustworthy AI systems, protect their reputation and maximise the benefits of AI technologies.
“By aligning governance processes throughout the lifecycle of AI systems, organizations can ensure seamless compliance with legislation like the EU AI Act, while driving innovation in the development and operation of AI solutions.”
The strategy and planning phase sets the foundation for successful AI governance implementation.
The data and model phase focuses on establishing a data infrastructure, selecting algorithms, and training AI models.
The test and validation phase is crucial for assessing the performance and reliability of AI systems.
The deployment and operation phase focuses on implementing the tested and validated AI systems while considering governance measures.
Ensuring compliance with regulatory conformity, risk governance and Trustworthy AI dimensions are critical for successful implementation of AI governance measures along the AI lifecycle.
The AI lifecycle consists of interconnected phases that guide through the ideation, development, deployment, and operation of AI systems. During each phase, measures for AI governance objectives are defined, implemented and assessed. In each phase, different roles are involved with different responsibilities, related to AI governance. Each role and each phase are crucial in ensuring the success of AI initiatives. Alinging roles and responsibilities along the AI lifecycle ensures the integraion of the right people.
The EU AI Act introduces a clear differentiation between the roles of “provider” and “deployer” for AI development and deployment, as outlined in their respective obligations. Providers develop AI systems or models and place it on the market or use it under its own name while deployers, according to the AI Act, only use an AI system under their own authority. The different obligations these roles bring should be considered accordingly during the process of integration along the AI lifecycle.
Creating responsible and high-quality outcomes with PwC’s sophisticated expertise in strategical AI lifecycle management and AI governance
Adhering to AI governance principles in AI lifecycle is not only a responsible choice but also a strategic advantage for businesses and organizations. It enables them to mitigate risks and comply with regulatory requirements such as the EU AI Act. PwC offers expertise in building up the AI governance within the AI lifecycle management, assisting organizations in developing and implementing strategies and governance frameworks to ensure successful outcomes. By leveraging tools to operationalize and automate the AI lifecycle management, organizations can manage the entire lifecycle of AI models more efficiently and effectively.
