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Beyond “Prompt and Pray”

O'Reilly on Data

This approach delivers substantial benefits: consistent execution, lower costs, better security, and systems that can be maintained like traditional software. Your companys AI assistant confidently tells a customer its processed their urgent withdrawal requestexcept it hasnt, because it misinterpreted the API documentation.

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

CIOs were given significant budgets to improve productivity, cost savings, and competitive advantages with gen AI. CIOs feeling the pressure will likely seek more pragmatic AI applications, platform simplifications, and risk management practices that have short-term benefits while becoming force multipliers to longer-term financial returns.

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5 top business use cases for AI agents

CIO Business Intelligence

Meanwhile, in December, OpenAIs new O3 model, an agentic model not yet available to the public, scored 72% on the same test. Were developing our own AI models customized to improve code understanding on rare platforms, he adds. That adds up to millions of documents a month that need to be processed.

Software 143
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CIOs to spend ambitiously on AI in 2025 — and beyond

CIO Business Intelligence

Nate Melby, CIO of Dairyland Power Cooperative, says the Midwestern utility has been churning out large language models (LLMs) that not only automate document summarization but also help manage power grids during storms, for example. Only 13% plan to build a model from scratch.

ROI 137
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Reimagine application modernisation with the power of generative AI

CIO Business Intelligence

As a consequence, these businesses experience increased operational costs and find it difficult to scale or integrate modern technologies. By leveraging large language models and platforms like Azure Open AI, for example, organisations can transform outdated code into modern, customised frameworks that support advanced features.

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AI agents: The next stage in the evolution of enterprise AI

CIO Business Intelligence

However, these applications only show a small glimpse of what is possible with large language models (LLMs). An example illustrates the possibilities: Imagine that an LLM receives the documentation for an API that can retrieve current stock prices. How many such AI agents might a large company need?

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Agentic AI design: An architectural case study

CIO Business Intelligence

From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. That will help us achieve short-term benefits as we continue to learn and build better solutions.

Testing 135