5 key takeaways from the 2026 State of Software Delivery
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Senior Technical Content Marketing Manager
AI has made it easier than ever to write code. Shipping it is a different story.
Today we released the 2026 State of Software Delivery report, sponsored by Thoughtworks. In it, we analyzed more than 28 million CI/CD workflows across thousands of engineering teams. The picture that emerged is clear: teams are producing more code than ever, but fewer of them are able to turn that activity into software that actually reaches customers.
Here are the five findings that matter most.
1. Average throughput jumped 59%, but almost all of it went to the top
Across all projects building on CircleCI, the average number of daily workflow runs increased 59% year over year, far and away the biggest throughput increase we’ve ever seen. Clearly, AI-powered code generation and agent-driven workflows are helping teams produce more changes, faster.
But that number is misleading on its own.
The top 5% of teams nearly doubled their throughput, increasing daily workflow runs by 97%. The median team? Up just 4%, while the bottom quartile saw no measurable increase at all. AI is amplifying existing delivery strengths, not distributing them evenly. Teams that already had strong pipelines and validation practices are pulling further ahead. Everyone else is running harder to stay in place.
2. Teams can write faster, but they can’t ship faster
Here’s the most telling split in the data: most teams saw a clear increase in activity on feature branches, where AI helps with prototyping and iteration. But throughput on the main branch, where code actually gets promoted to production, declined. For the median team, feature branch throughput increased 15%, but on the main branch, throughput fell by 7%.
Even teams in the top 10% struggled. Feature branch activity grew almost 50% for that group, while main branch throughput increased only 1%.
This is concrete evidence of the AI delivery bottleneck we’ve been calling out since last year. Writing code is no longer the constraint. Review, validation, integration, recovery. That’s where AI-generated code is piling up, and it’s quietly draining velocity, morale, and ROI from every AI investment.
3. AI-generated code breaks more often and takes longer to fix
Main branch success rates dropped to 70.8%, the lowest in over five years and well below CircleCI’s recommended benchmark of 90%. That means nearly 3 out of every 10 attempts to merge into production are failing.
Recovery times are climbing too: 72 minutes to get back to green for the typical team, up 13% from last year.
Those numbers compound fast. Imagine a team pushing 5 changes a day. At a 70% success rate, they’re experiencing 1.5 showstopping failures every day, compared to one every two days at 90%. Even if that team were to consistently hit our benchmark recovery time of 60 minutes (12 minutes faster than the median team in 2026), that gap adds up to roughly 250 additional hours lost to debugging and blocked deployments every year.
Scale that up to 500 changes a day, and you’re burning the equivalent of 12 full-time engineers just getting back to green.
4. Fewer than 1 in 20 teams have figured out how to ship at AI speed
The top 5% of teams are the exception to every trend above. Their throughput grew 97% year over year. Their main branch throughput increased 26% while feature branch activity grew 85%. They’re writing more code and shipping more code.
But they represent fewer than 1 in 20 teams. Their results show what’s possible when validation keeps pace with generation. They also show how far most teams still have to go.
5. Mid-sized companies are stuck in the “messy middle”
Performance by company size follows a U-shaped curve. The smallest companies (2–5 employees) and the largest enterprises (1,000+) perform best, with the highest main branch throughput and the fastest recovery times. Mid-sized companies (21–50 employees) struggle the most: they have the lowest throughput of any segment and recovery times approaching three hours. That’s nearly four times longer than the smallest and largest cohorts.
The pattern suggests a scaling problem. These companies have outgrown the speed and simplicity of small teams but haven’t yet built the systems and practices needed to operate at scale. AI is making this gap more visible and more costly.
How can your team close the gap?
The data points to a clear conclusion: success in the AI era isn’t determined by how fast code gets written. It’s determined by how fast it can be validated, integrated, and recovered.
The teams pulling ahead have invested in faster feedback loops, smarter test selection, and pipeline infrastructure that adapts to rising volume and complexity. The teams falling behind are running AI-generated code through the same static pipelines they built for human-speed development.
This is the problem that autonomous validation is designed to solve. Rather than relying on static scripts and manual upkeep, autonomous validation brings context and intelligence into the CI/CD pipeline itself, so that your validation layer can keep pace with the speed, scale, and complexity of AI-driven code generation.
You can learn more about these trends and what top-performing teams are doing differently in the 2026 State of Software Delivery.
Want to explore the dataset yourself? Visit the Software Delivery Data Explorer and compare your results against different team sizes, industries, and regions.
Based on 28,738,317 workflows run on CircleCI during September 2025. Projects with at least 2 contributors, workflows that ran at least 5 times. Full methodology in the complete report.
