FinTech Poland hosted a business breakfast to explore the practical aspects of AI Governance, with a specific focus on the actual costs of implementing and maintaining AI systems. The agenda covered key definitions, methodologies, and findings regarding real-world costs, their primary drivers, and potential for savings.
AI Governance is a broad term that encompasses areas such as compliance, quality assurance, in-production monitoring, retraining, data security, and decision auditability. The meeting presented a meta-analysis of AI Governance costs, averaging the data to an annual cost per model. The analysis identified six primary cost categories:
- Personnel and Supervision: The single largest cost driver.
- Documentation and Record-Keeping: Essential for transparency and compliance.
- Model Testing and Validation: Crucial for ensuring accuracy and reliability.
- Continuous Monitoring and Maintenance: Necessary for ongoing performance.
- Data Management and Infrastructure: Foundational costs for any AI system.
- Audit and Compliance Certification: Ensures regulatory adherence.
The presentation revealed that AI oversight costs are substantial, with the annual cost per model ranging from €60,000 to €120,000. It also explored different operational models for AI Governance, contrasting the Centralized model with the Federated model. A new hybrid model was also introduced, combining a central control plan with decentralized execution.
The event concluded with strategic recommendations for cost reduction, including standardizing artifacts, creating a central model registry, and automating control gateways throughout the production and maintenance lifecycle.
