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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. This fueled a belief that simply making models bigger would solve deeper issues like accuracy, understanding, and reasoning. Development velocity grinds to a halt.

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The key to operational AI: Modern data architecture

CIO Business Intelligence

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

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CIOs face mounting pressure as AI costs and complexities threaten enterprise value

CIO Business Intelligence

CIOs are under increasing pressure to deliver meaningful returns from generative AI initiatives, yet spiraling costs and complex governance challenges are undermining their efforts, according to Gartner. hours per week by integrating generative AI into their workflows, these benefits are not felt equally across the workforce.

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How will AI agents be priced? CIOs need to pay attention

CIO Business Intelligence

So far, no agreement exists on how pricing models will ultimately shake out, but CIOs need to be aware that certain pricing models will be better suited to their specific use cases. Lots of pricing models to consider The per-conversation model is just one of several pricing ideas.

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7 types of tech debt that could cripple your business

CIO Business Intelligence

CIOs perennially deal with technical debts risks, costs, and complexities. These areas are considerable issues, but what about data, security, culture, and addressing areas where past shortcuts are fast becoming todays liabilities? Using the companys data in LLMs, AI agents, or other generative AI models creates more risk.

Risk 123
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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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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.