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How Meta & Google are scaling AI across the SDLC

This research report explores how AI is transforming software delivery at scale by delving into:
How Meta and Google are applying LLMs to accelerate testing and code migrations
What 400+ engineering leaders reveal about AI’s role across the SDLC
Key frameworks to evaluate, measure, and scale your own AI adoption strategy

How Meta & Google are scaling AI across the SDLC

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Cover graphic for How Meta and Google are scaling AI across the SDLC.

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
AI developer personas by vertical and horizontal scores=

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
Flowing chart showing how to scale AI workflows

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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Cover of How Meta and Google are scaling AI across the SDLC.

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