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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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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. As of November 2023: Two-thirds (67%) of our survey respondents report that their companies are using generative AI. And only 33% report that their companies aren’t using AI at all.

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Generative AI in the Real World: Chip Huyen on Finding Business Use Cases for Generative AI

O'Reilly on Data

O’Reilly’s Generative AI in the Enterprise survey reported that people have trouble coming up with appropriate enterprise use cases for AI. Learn from their experience to help put AI to work in your enterprise. 3:02 : AI is easy to demo, but hard to productize. Why is it hard to come up with appropriate use cases?

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Post-Pandemic Enterprise Architecture Priorities

erwin

Before the COVID-19 pandemic, many enterprise architects were focused on standardization. By evaluating and deploying the right combination of cloud-based platforms and security tools, enterprise architects played a key role in keeping businesses up and running in a remote-work world. Responding to a Crisis.

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5 Ways Real-Time Financial Reporting Mitigates Enterprise Risk

Jet Global

Even before the coronavirus disrupted supply chains and shifted priorities, business leaders understood the need to identify and monitor the factors that could have an impact on their enterprises. When you put powerful self-service reporting tools into the hands of the finance team, that becomes possible. Compliance Risk.

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

Rocket-Powered Data Science

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. What could be faster and easier than on-prem enterprise data sources? using high-dimensional data feature space to disambiguate events that seem to be similar, but are not).

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Streamline data discovery with precise technical identifier search in Amazon SageMaker Unified Studio

AWS Big Data

Solving real-world discovery challenges In large, enterprise-scale environments, discovering the right dataset often hinges on pinpointing specific technical identifiers. Accelerate productivity across data roles Analysts, stewards, and engineers can find what they need fasterreducing delays in reporting, validation, and development cycles.

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