Risks in AI strategy: wrong initiatives, lack of support/clarity, overestimation. Align AI leadership/executive.
AI costs more than model development. Full lifecycle includes data governance, monitoring, and infrastructure.
Immature technology, lack of skills, model and data quality, and competence. Mitigate with skill-building and modern infrastructure.
People, culture, structure critical. Competent team catches problems.
Trust and explainability issues can hinder user adoption of AI. Poor performance, opaque models, are common reasons.
AI/ML: balancing innovation adherence to regulations. Mitigate with risk management and human monitoring.
Facial recognition, credit scoring, and insurance models are prone to bias, posing ethical challenges to AI/ML projects.