The 6 bottlenecks slowing AI-driven development

This report explores the hidden blockers stalling AI adoption across engineering orgs by analyzing:

Where AI is underutilized across planning, coding, and release

Why tooling, trust, and feedback loops are becoming breaking points

What 20k high-performing teams are doing to remove friction and scale AI effectively

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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

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

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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Workshop

GitHub Copilot here, Cursor there, Claude Code somewhere else. AI across tools is adopted, but is it actually working?

Guide

An operating model for engineering productivity with practical guidance for how to measure AI impact.

Report

Created from a study of 8.1+ M PRs from 4,800 engineering teams across 42 countries.