The DevEx guide to AI-driven software development
We polled 400+ engineering leaders on how they’re integrating AI into their workflows. Inside you’ll find:
5 common pitfalls of AI adoption – and how to avoid them
Survey results by role, tooling, company size, and use case
How leaders at Meta & Google are scaling AI across their engineering orgs
The DevEx guide to AI-driven software development
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Survey results
We gathered data reaching a broad cross-section of leaders across company sizes, industries, and technical roles.
You’ll find the research broken down by:
Org size insights
Role and AI tool insights
Survey results by AI score and SDLC stage
Real stories from the enterprise
Learn how DevEx leaders from the large SaaS enterprises are scaling their AI workflows to boost productivity, including:
How Meta is transforming software testing with AI-powered bug hunters
How Google uses AI to speed up code migrations by 50%
How both are shifting the conversation from innovation to bottom-line results
5 common pitfalls of AI adoption
Many teams are adopting AI in the most superficial way possible. We break down the most common pitfalls of AI adoption, including:
Using AI without context
Lack of DevEx enablement
Shallow experimentation without a strategy
1
Using AI without context
2
Lack of DevEx enablement
3
Failure to integrate AI into workflows
4
No feedback or iteration loop
5
Shallow experimentation without a strategy
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More resources
Guide
Hiring kit: Director of AI Enablement
Everything you need to define, hire, and empower the Director of AI enablement in your engineering organization.
Guide
Quickstart metrics guide: Rework
This guide will show you why Rework matters, how to measure it, and how to reduce it with smart practices and automation.
Guide
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.