Remove Experimentation Remove Optimization Remove Strategy
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Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

As gen AI heads to Gartners trough of disillusionment , CIOs should consider how to realign their 2025 strategies and roadmaps. AI innovation can not and should not exist without parallel investment in governance to ensure its responsible and effective integration, says Henry Umney, MD of GRC strategy at Mitratech.

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The key to operational AI: Modern data architecture

CIO Business Intelligence

People : To implement a successful Operational AI strategy, an organization needs a dedicated ML platform team to manage the tools and processes required to operationalize AI models. As a result, ​developers — regardless of their expertise in machine learning — will be able to develop and optimize business-ready large language models (LLMs).

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How AI orchestration has become more important than the models themselves

CIO Business Intelligence

To integrate AI into enterprise workflows, we must first do the foundation work to get our clients data estate optimized, structured, and migrated to the cloud. Once the data foundation is in place, it is important to then select and embed the best combination of AI models into the workflow to optimize for cost, latency, and accuracy.

Modeling 116
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Gartner projects major IT spending increases for 2025

CIO Business Intelligence

By 2026, hyperscalers will have spent more on AI-optimized servers than they will have spent on any other server until then, Lovelock predicts. Forrester also recently predicted that 2025 would see a shift in AI strategies , away from experimentation and toward near-term bottom-line gains. growth in device spending.

IT 133
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). The skillset and the background of people building the applications were realigned: People who were at home with data and experimentation got involved! Some seemed better than others.

Testing 168
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9 IT resolutions for 2025

CIO Business Intelligence

Its more about optimizing and maximizing the value were getting out of gen AI, she says. This approach not only demonstrates that we value our people wherever they are but allows me to engage effectively with my managers to develop strategies that foster a productive and inclusive culture where different strengths and skill sets can thrive.

IT 140