2026 Engineering Productivity Benchmarks: What AI Is Really Changing in Software Delivery

The 2026 Engineering Productivity Benchmarks are now live.

This year’s report analyzes delivery data from more than 2,000 software engineering teams worldwide. The goal is simple: understand how engineering performance is evolving as AI tools become embedded in the development workflow.

AI adoption is now widespread. Most developers are using AI tools regularly, and coding speed is clearly increasing across many organizations.

But the bigger question isn’t whether AI helps developers write code faster. It’s whether it helps teams deliver software faster and more predictably. The early data tells a more nuanced story.

AI Is Helping Some Teams Much More Than Others

One of the clearest signals in the 2026 benchmarks is how uneven the impact of AI has been across engineering teams.

Lower-performing teams using AI improved delivery speed dramatically, reducing Lead Time to Value by nearly 50% compared to similar teams not using AI. By contrast, top-performing teams saw improvements of around 10–15%.

That’s roughly a 4x difference in impact. This suggests that AI acts as a multiplier.

Teams with slower systems often see the biggest immediate improvements, because AI removes friction in the coding stage. But teams that are already operating efficiently have fewer obvious bottlenecks to remove.

Faster Coding Is Exposing New Bottlenecks

Another pattern emerging from the data is where work begins to stall once coding speeds up.

As developers generate more code, the pressure shifts downstream into review, testing, and integration.

Code review is becoming a particularly visible constraint. Bottom-quartile AI teams now take more than 35 hours on average to merge pull requests, while top-performing teams complete merges in under 21 hours.

This gap highlights how strongly review and integration capacity now influence delivery speed.

When development accelerates but review workflows remain unchanged, code simply queues up waiting to be merged.

The result is that coding gets faster, but delivery timelines barely move. The constraint hasn’t disappeared- it has moved.

Performance Gaps Still Remain Large

Even in the age of AI-assisted development, the gap between high- and low-performing engineering teams remains significant.

Top-performing teams ship changes to production in under 22.5 days on average, while bottom-quartile teams take more than 62 days. That’s nearly a 3x difference in delivery speed.

Predictability also varies dramatically.

High-performing teams complete more than two-thirds of the work they plan in each sprint, while lower-performing teams complete less than half, regularly missing their delivery targets.

These differences aren’t explained by tooling alone. Instead, they reflect how effectively work flows through the delivery system- from planning and refinement through coding, review, and release.

The data shows this clearly in how teams spend their engineering capacity.

High-performing teams dedicate over 41% of their time to roadmap delivery, while lower-performing teams spend less than 21%, with the rest consumed by bugs, incidents, and unplanned work.

AI Doesn’t Fix Delivery Systems

One of the strongest conclusions from this year’s benchmarks is that AI accelerates development, but it does not automatically fix the underlying delivery system.

Teams that struggle with slow reviews, unstable planning, or frequent rework do not suddenly become high-performing just because developers write code faster.

Instead, AI tends to expose these issues more clearly.

When coding accelerates, weaknesses in planning, review capacity, and integration processes become harder to ignore.

The benchmarks show that teams which actively remove these constraints deliver more than twice the output per engineer compared to those that simply adopt AI tools without improving the system around them.

In other words, AI increases potential- but realizing that potential requires system-level change.

Why AI Adoption Needs a Framework

One lesson from this year's benchmarks is that AI adoption works best when it's guided, not improvised.

Many teams introduce AI tools informally. Developers begin using them individually, coding speeds increase, and leadership expects delivery performance to improve automatically. But without a structured approach, the benefits often stall.

That's why Plandek developed the RACER™ Framework, helping engineering leaders systematically manage the AI transition, from rollout to results.

RACER focuses on five critical questions:

• Rollout: Are teams meaningfully adopting AI?

• Approach: Are they using AI effectively?

• Constraints: What's holding AI back?

• Engineering Impact: Is AI improving engineering performance?

• Results: Is the investment paying off?

The goal isn't simply to measure AI adoption. It's to identify where gains are being lost, remove the constraints holding back performance and make sure increasing AI investment translates into better engineering outcomes.

Plandek's AI Radar puts this framework into practice. AIRadar™ brings together leading indicators from across AI tools and the engineering system, giving leaders one place to manage the transition, identify where to intervene and improve the return on AI investment.

What Engineering Leaders Should Take Away

AI is changing software development, but the fundamentals of engineering performance still matter.

The biggest gains don't come from faster coding alone. They come from improving how work flows through the entire delivery system.

For engineering leaders, that means going beyond AI usage metrics to understand whether AI-assisted work is improving Focus, Speed, Predictability and Quality.

It also means acting on the constraints that limit those gains. Faster code generation has little value if reviews, testing or deployment cannot keep pace.

The lesson from the 2026 benchmarks is clear: AI adoption creates potential. Improving the engineering system is what turns that potential into measurable results.

Explore the Full Benchmarks

The 2026 Engineering Productivity Benchmarks explore these findings in greater depth, including:

• How AI adoption is affecting delivery speed

• Where engineering bottlenecks are shifting

• The metrics that separate top-performing teams from the rest

• What engineering leaders can do to improve delivery outcomes

Discover how your engineering performance compares, where the biggest opportunities for improvement lie and what it takes to turn AI adoption into better results.

Get the 2026 Engineering Productivity Benchmarks Report