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5 tips for better business value from gen AI

CIO Business Intelligence

According to AI at Wartons report on navigating gen AIs early years, 72% of enterprises predict gen AI budget growth over the next 12 months but slower increases over the next two to five years. CIOs should return to basics, zero in on metrics that will improve through gen AI investments, and estimate targets and timeframes.

Sales 143
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Data Scalability Raises Considerable Risk Management Concerns

Smart Data Collective

A report by China’s International Data Corporation showed that global data would rise to 175 Zettabyte by 2025. The Relationship between Big Data and Risk Management. While the sophisticated Internet of Things can positively impact your business, it also carries a significant risk of data misuse. Well, you aren’t alone!

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

AI at Wharton reports enterprises increased their gen AI investments in 2024 by 2.3 Deloittes State of Generative AI in the Enterprise reports nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts.

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PODCAST: Making AI Real – Episode 2: AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower

bridgei2i

Episode 2: AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. Today the Chief Risk Officers(CROs) struggle with the critical task of monitoring and assessing key risks in real time and firefight to mitigate any critical issues that arise.

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CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.

IT 59
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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8]. Currency amounts reported in Taiwan dollars.

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Managing risk in machine learning

O'Reilly on Data

There are also many important considerations that go beyond optimizing a statistical or quantitative metric. As we deploy more models, it’s becoming clear that we will need to think beyond optimizing statistical and business metrics. Real modeling begins once in production. Culture and organization.