AI Lifecycle

The AI lifecycle is a comprehensive, iterative framework describing an AI system's entire journey from conception to retirement. Adapted from the Software Development Lifecycle (SDLC), it specifically addresses AI's unique traits like data dependency and model evolution. As defined in the NIST AI Risk Management Framework (AI RMF), stages include design, data collection, model building, testing, deployment, and monitoring. Per ISO/IEC 42001, organizations must manage risks across this lifecycle, mapping factors like bias, privacy, and security to each stage, ensuring end-to-end governance unlike traditional SDLC.

Enterprises apply the AI lifecycle in three steps. First, map risks like bias or model drift to specific lifecycle stages using a framework like NIST AI RMF. Second, implement MLOps tools and standardized documentation (e.g., Model Cards) to ensure traceability, meeting EU AI Act requirements. Third, establish automated, continuous monitoring of model performance and fairness metrics, supported by regular audits. A fintech firm using this approach for an AI credit model reduced audit preparation time by 40% and achieved a 99% compliance rate.

Taiwanese enterprises face three key challenges. 1) Lack of regulatory expertise and integrated resources: Solved by forming a cross-functional AI governance committee and starting with a pilot project. 2) Fragmented MLOps toolchains: Mitigated by adopting open-source tools like MLflow for tracking and standardizing documentation. 3) Weak data governance culture, risking non-compliance with local data protection laws: Addressed by mandating Data Protection Impact Assessments (DPIAs) and involving legal teams early in projects.

Winners Consulting specializes in AI lifecycle for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact