Lauren Kornutick

Delve into the critical aspects of risk management and regulatory change in data, analytics and AI. Bring your questions and learn how to identify potential risks, triage and prioritize them, and implement effective mitigation strategies to safeguard your organization’s digital assets to be able to deliver AI projects.

In a recent Gartner survey, 89% of CDAOs said effective D&A governance is crucial for business, technology and AI innovation, yet almost 70% find their current D&A and AI governance practices, skills and technology insufficient to drive innovation and business value. This multigroup discussion will focus on sharing experiences and addressing how CDAOs are evolving their D&A and AI governance approaches, building the business case for foundational investments, and designing effective operating models.

Data governance has traditionally encompassed analytics governance, managing most risks and value in traditional analytics. However, AI introduces new risks and considerations that D&A governance may not be equipped for. Should D&A governance evolve to govern AI or is it time for a separate discipline with a fresh mandate? This session explores conflicting accountabilities, leadership and operating models between these disciplines.

Organizations struggle to scale AI governance, mitigate risk and effect compliance while delivering value. Current governance processes are not effective to deliver value at scale. In this session you will learn how to address culture and behavior, prioritize what capabilities your organization needs to add to the tech stack from complimentary tools to effectively govern AI, and make an investment decision in an AI governance platform if appropriate.

Organizations struggle to scale AI governance, mitigate risk and effect compliance while delivering value. Current governance processes are not effective to deliver value at scale. In this session you will learn how to address culture and behavior, prioritize what capabilities your organization needs to add to the tech stack from complimentary tools to effectively govern AI, and make an investment decision in an AI governance platform if appropriate.

Delve into the critical aspects of risk management and regulatory change in data, analytics and AI. Bring your questions and learn how to identify potential risks, triage and prioritize them, and implement effective mitigation strategies to safeguard your organization’s digital assets to be able to deliver AI projects.

Delve into the critical aspects of risk management and regulatory change in data, analytics and AI. Bring your questions and learn how to identify potential risks, triage and prioritize them, and implement effective mitigation strategies to safeguard your organization’s digital assets to be able to deliver AI projects.

Data governance has traditionally encompassed analytics governance, managing most risks and value in traditional analytics. However, AI introduces new risks and considerations that D&A governance may not be equipped for. Should D&A governance evolve to govern AI or is it time for a separate discipline with a fresh mandate? This session explores conflicting accountabilities, leadership and operating models between these disciplines.

Organizations struggle to scale AI governance, mitigate risk, and effect compliance while delivering value in AI deployments. AI governance programs only check the box if they are not adaptive and embedded into the fabric of the AI lifecycle. In this session, you will learn how to define your role in AI governance, assess what technology capabilities are required to govern AI at scale, and make an investment decision in an AI Governance Platform or other technology tool.

Organizations struggle to scale AI governance, mitigate risk, and effect compliance while delivering value in AI deployments. AI governance programs only check the box if they are not adaptive and embedded into the fabric of the AI lifecycle. In this session, you will learn how to define your role in AI governance, assess what technology capabilities are required to govern AI at scale, and make an investment decision in an AI Governance Platform or other technology tool.