AI is easy.

Data + AI is hard.

Modern organizations are facing a simple but serious truth. According to a 2025 MIT report, 95% of generative AI pilots are failing. What has not yet materialized are the approaches for reining in hallucinations, bringing explainability to black-box models, and making GenAI outputs reliable at enterprise scale.

In fact, a recent survey by Wakefield Research found that while every technical team feels pressure to build GenAI, more than two-thirds of data and AI leaders don’t believe their first-party data is ready. The gap between ambition and reliability has become the single greatest risk to AI adoption.

This new O’Reilly report, authored by Monte Carlo’s Barr Moses and Michael Segner, provides a blueprint for closing that gap. You’ll learn why traditional testing cannot keep pace with today’s data + AI systems and how observability has emerged as the cornerstone of reliability. The report explores the toughest challenges in scaling Retrieval-Augmented Generation (RAG) models, highlights common failure modes in data pipelines and AI agents, and offers practical frameworks to make informed decisions about where to invest your team’s time and resources.

Built as a field guide for data and AI leaders, this report provides both the strategy and the tactics to accelerate adoption, deliver reliable outcomes, and drive value at scale.

What You'll Learn:

• How structured and unstructured data amplify both the value and the complexity of AI applications
• Why observability is replacing testing as the foundation for reliable data and AI systems
• The most common reliability challenges at scale, and proven approaches to overcome them
• How to prioritize investments and evaluate technologies to maximize impact