HCLTech: How to Ensure AI Compliance and Responsibility
Heather Domin, Head of Office of Responsible AI and Governance at HCLTech, shares her perspective on agentic AI, compliance and ethical innovation
TL;DR
- Agentic AI shifts systems from passive response to proactive workflow execution and orchestration.
- Key challenges include governance, compliance, transparency, and responsible deployment at scale.
- Organisations need strong technical expertise and stakeholder coordination for responsible AI.
- Regulatory frameworks like the EU AI Act require structured documentation, traceability, and lifecycle monitoring.
- Transparency and trust are built through early design decisions, clear documentation (e.g., model cards), AI red teaming, and continuous monitoring.
- The choice between on-premises and cloud AI depends on use case, risk, compliance, and infrastructure, with hybrid models becoming common.
- Ethical risks with autonomous AI include goal misalignment and cascading effects, requiring clear accountability and human oversight.
- Future AI trends include enterprise-scale deployment of agentic systems, rapid growth in physical AI, and governance becoming a competitive differentiator.