Core Strategic Responsibilities
- Defining AI Strategy & Vision: Building a long-term enterprise roadmap that aligns AI initiatives with overarching business goals, such as revenue growth, cost reduction, and operational efficiency.
- Portfolio Management: Identifying and prioritizing high-impact use cases (e.g., generative AI agents, predictive analytics) based on ROI, feasibility, and risk.
- Executive Evangelism: Acting as the "face" of AI for the organization, educating the board and external stakeholders on AI's potential and limitations.
Governance and Risk Management
- Ethical AI Frameworks: Establishing policies for FATE (Fairness, Accountability, Transparency, and Explainability) to mitigate algorithmic bias and ensure responsible use.
- Regulatory Compliance: Navigating complex legal landscapes, such as the EU AI Act and national executive orders, to ensure all deployments meet data privacy and security standards.
- Model Risk & Security: Partnering with CISOs to oversee the security of AI models and data pipelines against breaches or misuse.
Implementation and Operations
- Cross-Functional Leadership: Coordinating between IT, engineering, legal, HR, and marketing to integrate AI seamlessly into existing workflows rather than leaving it in silos.
- Talent & Culture: Recruiting top-tier AI talent and spearheading organization-wide upskilling programs to build an AI-literate workforce.
- Infrastructure Oversight: Evaluating and selecting AI tools, vendors, and cloud platforms to build scalable MLOps and data environments.
Performance Monitoring
- KPI Tracking: Establishing metrics to measure AI success, such as model accuracy, time-to-deployment, and demonstrable business value (ROI).
- Scaling Pilots: Managing the transition of AI projects from experimental prototypes to full-scale production environments.
Requirements
- Postgraduate degree holder in a relevant discipline
- At least 15 years of relevant experience gained in large organisations
- Fluent English and Cantonese
