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CIOs weigh where to place AI bets — and how to de-risk them

CIO Business Intelligence

There are a lot of risks and a lot of land mines to navigate,” says the analyst. Coming to grips with risk The first step in making any bet — or investment — is to understand your ability to withstand risk. It’s a significant danger with significant costs. The opportunity is too big. billion in 2024 to $521.0

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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

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AI Helps Mitigate These 5 Major Supplier Risks

Smart Data Collective

AI is particularly helpful with managing risks. How AI Can Help Suppliers Manage Risks Better. The benefits of AI stem from the need to manage close relationships with business stakeholders, which is a difficult task. Failure or Delay Risk. Brand Reputation Risk. Competitive Advantage Risk.

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Essential skills and traits of chief AI officers

CIO Business Intelligence

Driving business benefits Companies seeking CAIOs are looking to reap myriad benefits from AI adoption, ranging from improved decision-making, to increased efficiency of business processes, higher-quality services, profitability, talent management, customer experience, and innovation.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It encompasses risk management and regulatory compliance and guides how AI is managed within an organization.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

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Digital Risk “Techquilibrium” Requires IRM

John Wheeler

Over the past month, I’ve been speaking to various groups to help them prepare for the onslaught of digital risks in their organizations. A common theme is the need for greater risk quantification beyond the realm of traditional, qualitative governance, risk and compliance (GRC) approaches.

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