Remove Cost-Benefit Remove Risk Remove Unstructured Data
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Unlocking the full potential of enterprise AI

CIO Business Intelligence

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] AI in action The benefits of this approach are clear to see.

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5 tips for better business value from gen AI

CIO Business Intelligence

Still, CIOs have reason to drive AI capabilities and employee adoption, as only 16% of companies are reinvention ready with fully modernized data foundations and end-to-end platform integration to support automation across most business processes, according to Accenture. Paul Boynton, co-founder and COO of Company Search Inc.,

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Mastering Multi-Cloud with Cloudera: Strategic Data & AI Deployments Across Clouds

Cloudera

Adopting hybrid and multi-cloud models provides enterprises with flexibility, cost optimization, and a way to avoid vendor lock-in. Cost Savings: Hybrid and multi-cloud setups allow organizations to optimize workloads by selecting cost-effective platforms, reducing overall infrastructure costs while meeting performance needs.

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AI agents will transform business processes — and magnify risks

CIO Business Intelligence

The previous state-of-the-art sensors cost tens of thousands of dollars, adds Mattmann, who’s now the chief data and AI officer at UCLA. The systems are fed the data, and trained, and then improve over time on their own.” Adding smarter AI also adds risk, of course. “At They also had extreme measurement sensitivity.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. Data quality is no longer a back-office concern. The decisions you make, the strategies you implement and the growth of your organizations are all at risk if data quality is not addressed urgently.

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AI and generative AI are revolutionizing manufacturing…here’s how

CIO Business Intelligence

AI can help with all of these challenges via manufacturing-specific use cases that benefit manufacturers, their employees, and their customers. Process optimization In manufacturing, process optimization that maximizes quality, efficiency, and cost-savings is an ever-present goal. Here’s how. Artificial Intelligence

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Structural Evolutions in Data

O'Reilly on Data

.” Consider the structural evolutions of that theme: Stage 1: Hadoop and Big Data By 2008, many companies found themselves at the intersection of “a steep increase in online activity” and “a sharp decline in costs for storage and computing.” “Here’s our risk model. Until it wasn’t.