Proactive and agile data governance is essential for public sector organizations to effectively scale agentic AI, according to Capgemini’s Dr. Kirti Jain. He emphasizes the need for data governance to be integrated from the onset of AI design to ensure accuracy, consistency, and quality in data-driven decision-making.
Artificial Intelligence (AI) is a transformative development with the potential to solve real-world problems, but it also raises significant ethical challenges regarding data privacy and algorithmic bias. To responsibly harness AI’s potential, we must prioritize ethical reflection, transparency, and accountability in its design and implementation.
Regional policymakers and industry leaders convened at the 2025 PECC Conference in Singapore to discuss the importance of responsible AI governance and the need for collective action in harnessing AI’s potential. Denise Wong from IMDA emphasized that while AI can generate significant value, it also poses risks that must be managed to build public trust and ensure equitable outcomes.
In this post, Joseph Jude discusses the complexities of implementing Responsible AI in enterprise applications, emphasizing the conflict between ideal principles and real-world business pressures. He introduces the NIST AI Risk Management Framework as a structured approach to identify and mitigate AI risks while fostering a culture of responsible AI use.