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AI data readiness: C-suite fantasy, big IT problem

CIO

Confidence from business leaders is often focused on the AI models or algorithms, Erolin adds, not the messy groundwork like data quality, integration, or even legacy systems. Successful pilot projects or well-performing algorithms may give business leaders false hope, he says. Thats where the friction arises.

IT 504
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Your CEO has little idea who IT is or what IT does

CIO

IT may be central to modern existence, but the people and processes of IT remain a mystery to most business executives and colleagues. Its time to change this. I asked a group of business executives to take out a blank sheet of paper, draw a big circle, and label it IT People and Processes. For the vast majority, that circle was a tiny period.

IT 468
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Marsh McLennan IT reorg lays foundation for gen AI

CIO

The team opted to build out its platform on Databricks for analytics, machine learning (ML), and AI, running it on both AWS and Azure. Marsh McLennan has been using ML algorithms for several years for forecasting, anomaly detection, and image recognition in claims processing.

IT 458
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Marsh McLellan IT reorg lays foundation for gen AI

CIO

The team opted to build out its platform on Databricks for analytics, machine learning (ML), and AI, running it on both AWS and Azure. Marsh McLellan has been using ML algorithms for several years for forecasting, anomaly detection, and image recognition in claims processing.

IT 395
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Build a strong data foundation for AI-driven business growth

CIO

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.

Business 418
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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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Dulling the impact of AI-fueled cyber threats with AI

CIO

It can also create cyber threats that are harder to detect than before, such as AI-powered malware, which can learn from and circumvent an organization’s defenses at breakneck speed. Then there’s reinforcement learning, a type of machine learning model that trains algorithms to make effective cybersecurity decisions.

Security 482