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A framework for measuring effective AI adoption in engineering

These days, engineering leaders find themselves caught between a rock and a hard place. On paper, AI adoption looks like an unqualified success. Developers are shipping more code faster than ever, pull request volumes are up, and teams report feeling more productive. Their leaders rush to LinkedIn to share their plans to scale adoption because their teams are just so much more efficient. But then, the incidents and bug reports start piling up.

Intelligent Systems Powering the Next Generation of Online Retail

Online retail is no longer driven only by attractive storefronts and competitive pricing. Behind every smooth shopping experience sits a complex network of decisions that must happen instantly and at scale. From predicting demand to responding to customer behavior in real time, cognitive AI agents are becoming a foundational layer that helps ecommerce businesses operate with speed, accuracy and consistency.

Struggling With Customer Drop-Off? AI Insights Can Help You Fix It Fast

Are you noticing more customers slipping away than sticking around? It's frustrating, right? Customer drop-off can feel like a mystery, but the good news is-it doesn't have to stay that way. Thanks to smart AI insights, you can quickly spot where things are going wrong and fix them before it's too late. Imagine having a clear map showing exactly why customers leave and what you can do to keep them coming back.

No-Code AI Tools That Are Changing Digital Marketing Forever

Artificial intelligence is no longer limited to data scientists or enterprise teams with large development budgets. Over the past few years, a new wave of no-code AI tools has emerged, allowing marketers to automate tasks, generate insights, and optimize campaigns-without writing a single line of code. For digital marketers, this shift is transformational. No-code AI tools reduce execution time, lower costs, and empower teams to focus on strategy rather than manual work. More importantly, they level the playing field, allowing small and mid-sized businesses to compete with larger brands.

AI adoption is messy. Here's how engineering leaders are taming the chaos.

There's a moment every engineering leader hits when implementing AI where they realize that no one really knows what they're doing. Not your competitors. Not the consultants. Not even the executives pressuring you to show results yesterday. Everyone is figuring this out in real time, and beneath the confident vendor pitches and LinkedIn thought leadership, the truth is messier than anyone wants to admit.

AI & FinOps: The New Power Duo Driving Modern Profitability

FinOps teams have been expected to understand millions of dollars in cloud and AI spend using tools that a handful of (usually technical) specialists can operate. Dashboards, filters, exports, and SQL have been the norm. That era is over. CloudZero is now bringing AI directly into the FinOps workflow so anyone in the business can ask natural-language questions about cloud and AI spend, and get accurate answers back from the platform.

Discover how to build AI-augmented applications with enterprise-grade security

IT leaders want AI that moves the needle without blowing up risk, cost, or changing control. Your teams need a path to productize AI features on top of existing apps, connect safely to external models, and satisfy audit requirements without slowing delivery. Those are the core buying criteria we hear from IT middle management: buy over build, predictable outcomes, and a strong compliance posture.

Training Foundation Models on a Trillion Data Points with Apache Iceberg

Training an AI foundation model on over a trillion data points sounds impossible without hitting your production systems. Here's how Datadog did it with Apache Iceberg for their time series forecasting model TOTO. The key challenge: extracting massive historical observability data (metrics spanning years) and running incremental preprocessing pipelines without overwhelming production services. Iceberg solved this by providing schema governance, consistency guarantees, and seamless integration with ML tools like Ray and PyTorch.