AI Helps Low-Performing Engineering Teams 4x More Than High-Performing Ones, New Benchmarks Show

London, UK – 5th March 2026 – The 2026 Engineering Productivity Benchmarks from Plandek, a Developer Productivity Intelligence (DPI) platform, has analyzed data from more than 2,000 software engineering teams worldwide.

The benchmarks show that low-performing teams using AI cut Lead Time to Value – the time it takes for work to move from ideation to being released – by nearly 50% compared to similar teams that are not using AI. This represents roughly 4x the improvement seen among high-performing teams.

Key findings from the 2026 benchmarks:

• AI helps lower-performing teams move much faster. Bottom-quartile teams using AI reduce Lead Time to Value by nearly 50% compared to similar teams without AI, while the top-quartile teams see improvements of only 10-15%.
• Code review is now a major bottleneck. Despite faster overall delivery with AI, bottom-quartile teams still take 35+ hours to merge pull requests, compared to under 21 hours for top performers, showing that review and integration remain key constraints on delivery speed.
• Results depend on removing bottlenecks, not just adding AI. Among AI teams, those that directly address planning, review, and quality constraints deliver more than 2x the output per engineer than those that don’t.
• Speed still varies dramatically across top and low performers. Top teams ship software in under 22.5 days on average, while bottom-quartile teams take more than 62 days, highlighting that large performance gaps persist even in the age of AI.
• Low-performing teams aren’t just slower – they’re less predictable. High-performing teams complete over two-thirds of their planned sprint work, nearly twice as much as low-performing teams, which complete less than half, regularly missing the targets they set themselves.
• Top teams spend their time building the roadmap, not fixing problems. Because high-performing teams ship predictably and avoid rework, they spend over 41% of their time on roadmap delivery, compared to less than 21% for low-performing teams whose capacity is consumed by bugs and unplanned work.

AI speeds up engineering – but it doesn’t fix broken delivery systems

The data shows that AI adoption improves delivery speed across the board, especially for lower-performing teams. But it also highlights a clear pattern: teams that already struggle with slow reviews, unstable planning, and rework don’t automatically turn faster code into faster delivery.

“AI is helping teams move faster, but it’s also exposing where the delivery bottlenecks are,” said CEO Charlie Ponsonby. “Our view is rooted in the Theory of Constraints: productivity doesn’t improve by optimizing everything at once, it improves by identifying what’s slowing the system down and fixing it. The teams seeing the biggest gains are the ones that understand where work gets stuck and remove those constraints, not the ones adding more tools.”

AI changes where work gets stuck

While AI reduces the time it takes to get code written, many teams are now slowed down later in the process. Bottom-quartile AI teams take more than 35 hours to merge pull requests, compared to under 21 hours for top performers. As development accelerates, review and integration increasingly determine how quickly value reaches production. Without faster reviews and clearer ownership, code simply queues up instead of shipping.

Performance gaps remain wide across engineering teams

Even beyond AI adoption, huge differences separate top and bottom performers. Top teams ship software in under 22.5 days on average, while bottom-quartile teams take more than 62 days – nearly a 3x gap in delivery speed. High-performing teams also complete over two-thirds of their planned work per sprint, compared to less than half for low performers.

For most engineering organizations, this suggests significant untapped improvement potential. The gap is not explained by tooling alone, but by how effectively work flows through planning, review, and quality control. Teams that improve these fundamentals can materially increase both speed and predictability without changing headcount.

About the Benchmarks

The 2026 Plandek Engineering Productivity Benchmarks are based on anonymized data from Q4 2025, spanning more than 2,000 engineering teams using tools including Jira, GitHub, GitLab, CI/CD platforms, and AI development tools.

About Plandek

Plandek is a leading Developer Productivity Insight (DPI) Platform headquartered in London, UK. It is used by software engineering teams globally to drive software engineering productivity and accelerate the transition to AI-augmented engineering.

The Plandek platform tracks the adoption and impact of AI tools (such as Cursor, Claude, and Devin), focuses engineering resources on value-creating activities and shared productivity metrics, enables teams to remove blockers and ship faster, and share clear KPIs with stakeholders.

To learn more, visit www.plandek.com.