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How bigtechs plan to implement AI-native development at home 2026 year (Category Management)

#Management #AI #Future #Software #Engineering #Productivity #Agents #Processes

In previous articles in this series, I wrote that the industry is moving from Classical PDLC for AI-native developmentand then to AI-native organizations. In this article, I planned to look at bigtech approaches to organizing this transition and setting goals within the organization, as well as measuring results.

According to public signals from Microsoft, GitHub, Google, Amazon, Meta, Uber, Stripe and Netflix, AI is becoming a full-fledged layer of the engineering operating model. He's built into PR. (pull requests)Code review, testing, migrations, triage of bugs, CI/CD Piplans and increasingly – in agency workflow, where the system not only prompts, but also performs bounded task under human control.

They are also beginning to measure this transition in a new way: not with one metric of use, which was popular in the past, but with a whole chain of adoption → throughput → quality / risk → economics. This bundle is gradually becoming a new language for tech executives and platform teams. This four looks something like this.

1) Adoption. How much AI has entered into the everyday circuit of work, how many active users, what are the adoption agents, how the use is distributed by command, IDE, CLI and workflow. 2) Throughput. Whether the cycle time on PR and review, as happened with time to merge, whether the path from the first commit to open PR has accelerated, whether the engineering velocity has grown. 3) Quality/risk. What Happens to Useful Comments, Feedback, Defect Prevention, Duplicate Error Processing, Test Efficiency, and Incident Frequency (These are counterbalancing metrics for speed and througput.) 4 )Economics. How many hours were saved on specific jobs, man-years saved during migrations, what is the value of CTS-SW? (Cost to serve software from AWS)What is the ROI and what is the cost of scaling this entire layer of AI?

This bundle is increasingly seen in official materials GitHub, Google, AWS, Uber and Amazon.

#AI #Management #Future #Software #Engineering #Productivity #Agents #Processes