Objectives

Purpose

This document establishes the measurable objectives for Rygen Technologies’ AI Management System (AIMS) in accordance with ISO/IEC 42001:2023. These objectives support the AI Policy and demonstrate our commitment to responsible, effective, and trustworthy AI deployment.

Scope

These objectives apply to all AI systems within the AIMS scope, including the X1 Integration Platform, Corsair TMS, and internal AI tools.

AIMS Objectives

Trustworthy Delivery

Objective: AI systems shall be evaluated for trustworthiness requirements prior to deployment, with explainability and human oversight controls implemented based on risk and impact assessment outcomes for mission-critical systems or decision-support applications.

Key Performance Indicators:

• Percentage of AI systems with completed trustworthiness evaluations before deployment
• Percentage of high-risk AI systems with documented explainability mechanisms (where required by assessment)
• Percentage of mission-critical AI systems with implemented human oversight controls (where required by assessment)
• Compliance with assessment-driven requirements in design reviews

Implementation:

• Conduct risk and impact assessments for all AI systems to determine trustworthiness requirements
• Include assessment-based explainability and oversight requirements in AI System Design templates
• Implement explainability and human oversight controls only where justified by risk assessment
• Enforce compliance checks during design reviews based on system-specific requirements
• Conduct quarterly audits of trustworthiness evaluation completeness
• Document rationale for systems not requiring explainability or human oversight controls

Responsibility: Principal AI Engineer
Review Frequency: Quarterly

Performance Excellence

Objective: Achieve and maintain at least 90% accuracy/performance targets for all production AI systems as defined in their specifications.

Key Performance Indicators:

• Percentage of AI systems meeting defined performance targets
• Average performance score across all production AI systems
• Number of performance-related incidents

Implementation:

• Define specific performance targets in each AI System Design Document
• Configure automated monitoring through Vertex AI and custom metrics
• Set up alerts for performance degradation
• Conduct quarterly performance reviews
• Retrain or tune models when performance drops below targets

Responsibility: AI/ML Team Lead
Review Frequency: Quarterly

Responsible Governance

Objective: Complete 100% of required AI risk assessments and impact assessments prior to production deployment.

Key Performance Indicators:

• Percentage of AI systems with completed risk assessments before deployment
• Percentage of AI systems with completed impact assessments before deployment
• Number of deployments delayed due to incomplete assessments

Implementation:

• Integrate assessment requirements into deployment pipeline
• Maintain assessment templates and tracking in Jira
• Block deployments lacking required assessments
• Complete retroactive assessments for existing systems by Q3 2025
• Train all teams on assessment requirements

Responsibility: Principal AI Engineer
Review Frequency: Quarterly

Client Value Through Innovation

Objective: Deploy at least 2 new AI systems or features per year that demonstrably improve client operational efficiency and/or experience.

Key Performance Indicators:

• Number of new AI features deployed annually
• Client-reported efficiency improvements
• Feature adoption rates

Implementation:

• Maintain AI feature roadmap aligned with client needs
• Track adoption through product analytics
• Conduct semi-annual client feedback sessions
• Prioritize features with clear efficiency gains
• Measure and document value delivered

Responsibility: Product Manager with Principal AI Engineer
Review Frequency: Semi-annually

Operational Resilience

Objective: Maintain 99% availability for AI services with documented fallback mechanisms for all critical AI features.

Key Performance Indicators:

• AI service availability percentage
• Mean time to recovery (MTTR) for AI service incidents
• Percentage of critical features with tested fallback mechanisms

Implementation:

• Monitor availability through GCP monitoring
• Document and implement fallback mechanisms for all critical features
• Test fallbacks quarterly
• Maintain incident response runbooks
• Conduct post-incident reviews to improve resilience

Responsibility: DevOps Team Lead
Review Frequency: Quarterly

Compliance

Objective: Achieve 100% compliance with all relevant regulations and standards.

Key Performance Indicators:

• Number of compliance violations
• Percentage of compliance requirements met
• Audit findings and corrective actions completed

Implementation:

• Implement all controls per Statement of Applicability
• Track compliance status in Sprinto
• Conduct quarterly internal reviews
• Address audit findings within defined timelines
• Achieve ISO 42001 certification through Stage 1 and Stage 2 registration audits in 2026

Responsibility: Principal AI Engineer with Compliance
Review Frequency: Quarterly

Monitoring and Measurement

Each objective shall be:

• Measured using the defined KPIs
• Reviewed at the specified frequency
• Reported to top management at quarterly management reviews
• Reviewed at governance committee meetings as needed
• Updated annually or when strategic changes occur

Integration with AIMS

These objectives:

• Support the AI Policy commitments
• Align with Rygen’s strategic direction
• Provide framework for risk assessment priorities
• Guide resource allocation decisions
• Inform training and competence requirements

Communication

These objectives shall be:

• Communicated to all relevant personnel
• Posted on internal AIMS documentation portal
• Included in AIMS training materials
• Communicated during onboarding
• Reviewed at AI Governance Committee meetings

Revision History