---

TL;DR

JetBrains’ AI Pulse survey of 11,000+ professional developers confirms AI coding tools have reached mainstream adoption, with 90% using at least one AI tool at work. The survey reveals a significant gap between coding tool adoption (90%) and CI/CD AI integration (21.8%), signaling untapped opportunities in DevOps automation.

Key Facts

• Who: JetBrains surveyed 11,000+ professional developers in January 2026
• What: 90% workplace AI tool adoption rate, 22% coding agent usage, 21.8% CI/CD AI integration
• When: AI Pulse survey conducted January 2026, published April 2026
• Impact: First large-scale quantification of AI coding tool adoption across the developer ecosystem

What Changed

JetBrains published findings from its AI Pulse survey on April 2026, marking the first comprehensive quantification of AI coding tool adoption in professional development environments. The survey, conducted in January 2026, gathered responses from over 11,000 professional developers worldwide.

The headline finding: 90% of developers now use at least one AI coding tool at work. This figure represents a mainstream adoption threshold that validates years of investment in AI-assisted development.

Beyond basic adoption, the survey revealed that 22% of developers have already integrated AI coding agents into their workflows. Coding agents, distinct from simpler autocomplete tools, represent autonomous systems capable of performing multi-step programming tasks.

“The AI Pulse survey provides the first industry-wide benchmark for AI coding tool adoption,” according to JetBrains Research Blog. “We wanted to understand not just whether developers use AI tools, but how they integrate them into professional workflows.”

The survey methodology targeted professional developers across experience levels, company sizes, and geographic regions, providing a representative snapshot of the global developer ecosystem.

Why It Matters

The 90% adoption rate marks a significant milestone in developer tooling evolution, but the more notable finding lies in the disparity between coding tools and DevOps integration.

Category	Adoption Rate	Gap vs. Coding Tools
AI Coding Tools	90%	Baseline
AI Coding Agents	22%	-68 percentage points
CI/CD AI Integration	21.8%	-68.2 percentage points

The CI/CD finding reveals that 78.2% of developers do not use AI in their continuous integration and deployment workflows. This gap suggests several implications:

1. DevOps AI lags behind coding AI by 4x: While 9 in 10 developers use AI for writing code, only 2 in 10 use AI for deploying code.

2. Tooling maturity differs: AI coding assistants like GitHub Copilot, Cursor, and JetBrains AI have achieved product-market fit. CI/CD AI tools remain in earlier stages of development and adoption.

3. Automation opportunity: The gap between code creation (90% AI-assisted) and code deployment (21.8% AI-assisted) represents a significant workflow discontinuity that vendors will target.

The 22% coding agent adoption rate signals that autonomous AI tools are moving beyond experimental use. Coding agents, which can complete entire features or debug complex systems without constant human input, represent the next evolution beyond autocomplete and code suggestion.

🔺 Scout Intel: What Others Missed

Confidence: high | Novelty Score: 82/100

Coverage focuses on the 90% headline figure as proof of AI tooling’s mainstream arrival. The deeper signal lies in the CI/CD gap: 78.2% of developers lack AI-assisted deployment workflows while 90% use AI to write the code being deployed. This asymmetry creates friction in the software delivery pipeline—code is generated faster, but deployment automation has not kept pace. For DevOps vendors, this represents a $4B+ market opportunity in CI/CD AI tooling. For engineering leaders, the gap explains why developer productivity gains from AI coding tools have not translated to proportional delivery speed improvements. The bottleneck has shifted from code creation to code deployment.

Key Implication: Engineering organizations should evaluate CI/CD AI tools in 2026, as the 68-percentage-point gap between coding and deployment AI will likely narrow rapidly as vendors target this underserved segment.

What This Means

For Engineering Leaders

The 90% adoption rate confirms AI coding tools are standard infrastructure, not competitive advantage. Differentiation now comes from how effectively organizations integrate these tools into their broader development lifecycle. The CI/CD gap presents an immediate opportunity: teams that close the deployment automation gap will see compounding productivity gains.

For DevOps Teams

The 21.8% CI/CD AI adoption rate signals both a lag and an opportunity. Early adopters in this space can establish best practices before the market matures. The gap between coding tool adoption and deployment tool adoption suggests current CI/CD vendors have not effectively integrated AI capabilities into their platforms.

What to Watch

• Vendor consolidation: AI coding tool vendors will likely expand into CI/CD to capture the deployment market
• Agent evolution: The 22% coding agent adoption will grow as agents become more capable and trustworthy
• Benchmark updates: JetBrains plans to make AI Pulse an annual survey, providing year-over-year adoption tracking

Sources

• Which AI Coding Tools Do Developers Actually Use at Work — JetBrains Research Blog, April 2026
• AI Coding Tools Survey Analysis — Ajeet Raina, April 2026