The platform engineer’s guide to AI adoption
This guide explores how AI is redefining the role of platform teams in 2025 by delving into:
Strategies for architecting AI-native infrastructure and workflows
What 20K+ engineering leaders are doing to operationalize AI at scale
How platform engineering can enable safe, scalable AI adoption across the SDLC
The platform engineer’s guide to AI adoption
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Insights from 20K+ engineering leaders
Learn how today’s top engineering minds are preparing for an AI-first future, including:
Why unified documentation and high-quality data matter more than ever
How platform teams are enabling zero-touch deployment pipelines
The cultural and technical shifts needed for AI-human collaboration at scale
Your playbook for scaling AI with confidence
Inside, you’ll find practical checklists and expert-backed strategies to:
Establish AI governance frameworks without sacrificing velocity
Create invisible guardrails and human-in-the-loop validation
Standardize metrics and build trust in AI-generated output
Common AI bottlenecks (& how to avoid them)
This guide reveals the most critical bottlenecks slowing AI-driven development, including:
Isolated tools without orchestration layers
Legacy infrastructure unfit for autonomous agents
Disconnected knowledge and tribal documentation
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Everything you need to define, hire, and empower the Director of AI enablement in your engineering organization.
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This guide will show you why Rework matters, how to measure it, and how to reduce it with smart practices and automation.
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Quickstart metrics guide: Planning Accuracy
In this guide you’ll learn why Planning Accuracy matters, how to measure it, and how top-performing teams improve it.