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Beyond “Prompt and Pray”

O'Reilly on Data

TL;DR: Enterprise AI teams are discovering that purely agentic approaches (dynamically chaining LLM calls) dont deliver the reliability needed for production systems. A shift toward structured automation, which separates conversational ability from business logic execution, is needed for enterprise-grade reliability.

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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] Reliability and security is paramount.

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Why Data Driven Decision Making is Your Path To Business Success

datapine

The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason. By implementing the right reporting tools and understanding how to analyze as well as to measure your data accurately, you will be able to make the kind of data driven decisions that will drive your business forward.

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Generative AI in the Enterprise

O'Reilly on Data

In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI. AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3%

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AR/VR Simulations for Sustainable, Regenerative, Circular Cities

Speaker: Nik Gowing, Brenda Laurel, Sheridan Tatsuno, Archie Kasnet, and Bruce Armstrong Taylor

In this session, participants will see how science data from such sources as NASA and NOAA, combined with local data inputs, can be used to both exponentially improve and accelerate net-zero carbon, climate positive and regenerative outcomes. This is a panel discussion you won't want to miss!

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.

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5 tips for transforming company data into new revenue streams

CIO Business Intelligence

Enterprises worldwide are harboring massive amounts of data. Although data has always accumulated naturally, the result of ever-growing consumer and business activity, data growth is expanding exponentially, opening opportunities for organizations to monetize unprecedented amounts of information.