Remove Data-driven Remove Experimentation Remove Testing
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. Two big things: They bring the messiness of the real world into your system through unstructured data.

Testing 168
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88% of AI pilots fail to reach production — but that’s not all on IT

CIO Business Intelligence

The proof of concept (POC) has become a key facet of CIOs AI strategies, providing a low-stakes way to test AI use cases without full commitment. The high number of Al POCs but low conversion to production indicates the low level of organizational readiness in terms of data, processes and IT infrastructure, IDCs authors report.

ROI 127
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Digital transformation 2025: What’s in, what’s out

CIO Business Intelligence

Organizations will always be transforming , whether driven by growth opportunities, a pandemic forcing remote work, a recession prioritizing automation efficiencies, and now how agentic AI is transforming the future of work.

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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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10 AI strategy questions every CIO must answer

CIO Business Intelligence

The time for experimentation and seeing what it can do was in 2023 and early 2024. Do we have the data, talent, and governance in place to succeed beyond the sandbox? Its typical for organizations to test out an AI use case, launching a proof of concept and pilot to determine whether theyre placing a good bet.

Strategy 141
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 364
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. These changes may include requirements drift, data drift, model drift, or concept drift. encouraging and rewarding) a culture of experimentation across the organization.

Strategy 290