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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.

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.

Get more value out of your Cortex catalog with our MCP prompt library

You've set up the Cortex MCP and connected it to your AI assistant and IDE. You ask about service ownership, check a Scorecard or two, and it works. You're impressed by how much faster this is than clicking through the web UI. Now you're wondering what else you can do with it. I'm willing to bet we've hit a nerve with that "hypothetical" scenario. The Cortex MCP works exactly as designed, but it's deceptively difficult to know which questions to ask and when to ask them.

Rethinking developer productivity in the age of AI

For decades, engineering leaders have struggled to measure the productivity of their developers. Metrics such as number of PRs merged, lines of code changed, hours worked, and tickets closed were always flawed. They incentivized the wrong behaviors and ignored code quality and best practices. Ultimately, they were the perfect formula to make Goodhart's Law a reality. Measures became targets, which meant they ceased being good measures.

Cortex Wrapped 2025: The Year of AI Excellence

Every December, Spotify launches its infamous Wrapped campaign, which sends millions of users into a frenzy about their listening habits. They become pseudo data scientists and analyze how frequently they listen to their guilty pleasures, their kids' terrible playlists, or the music they love that nobody else has heard of yet. We love this tradition, so we're bringing it to Cortex.

AI Maturity

Learn how Cortex helps engineering organizations unlock AI excellence by measuring, standardizing, and improving how teams adopt and use AI coding assistants like GitHub Copilot, Cursor, and Claude. Cortex enables organizations to mature their AI practices—not just adopt AI tools, but adopt them safely, consistently, and with measurable engineering impact. What you’ll learn in this video.

AI Readiness

Discover how Cortex helps engineering organizations unlock AI excellence by building the strong, reliable foundation needed for safe and scalable AI adoption. Cortex goes beyond just giving developers access to AI tools; it ensures your teams are ready to use AI safely, reliably, and at scale. What You’ll Learn in This Video: With Cortex, teams gain visibility into engineering practices, track compliance across services, and create a repeatable framework for safe AI innovation. By automating accountability and enforcing standards, Cortex helps organizations adopt AI with confidence, not risk.

AI Governance

Discover how Cortex helps organizations unlock AI excellence by bringing structure, visibility, and governance to teams that are building AI and machine learning models. As companies scale their AI initiatives, Cortex becomes the single source of truth for all ML and AI assets, ensuring reliable versioning, ownership, compliance, and responsible AI practices. What you'll learn in this video.