Implement your target AI governance operating model by mapping governance pillars to key AI components and differentiating AI capabilities. This model should connect with other governance bodies and extend existing governance models to AI-specific considerations of trust, transparency and diversity.
talk-data.com
S
Speaker
Svetlana Sicular
5
talks
Svetlana Sicular is a thought leader in AI, making a transformational impact on AI governance, strategies and AI-data readiness of organizations of all scales, from multinational corporations and government bodies to innovative startups. As one of the first analysts ever covering AI, she has been shaping AI space for over a decade, influencing strategies of major players and adopters. Her current research interests focus on AI governance, AI trends, the ongoing shifts in AI and on the most exciting part of artificial intelligence - the human. At Gartner, Svetlana pioneered research on generative AI, AI governance, responsible AI, AI-ready data and earlier – on big data, multidisciplinary AI teams, data product management, the Chief Data Officer's role, data governance in today's AI-driven landscape, and augmented intelligence – where humans and machines excel together.
Bio from: gartner-data-analytics-uk-2025
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Responsible AI decisions are not black and white, they require trade-offs. Learn to make trade-offs and debate the alternatives to make AI governance and responsible AI decisions. Discuss controversial ideas in AI with your peers. Express your opinion and listen to what others are saying. Learn to ask the right questions and get the right answers to ensure responsible, trustworthy and ethical AI.
See through the hype to better understand the practical applications, risks, and aspirations of AI in the data governance industry. Find out how to position your data governance program as a leader for your organizations' AI initiatives.
Join this session to get oriented about where you are on the AI maturity curve and develop the next steps to achieve your goals. Explore AI maturity by addressing its building blocks such as use case selection, technology operationalization and AI adoption.
KIL Proposal:
Implement your target AI governance operating model by mapping governance pillars to key AI components and differentiating AI capabilities. This model should connect with other governance bodies and extend existing governance models to AI-specific considerations of trust, transparency and diversity.