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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →There is no single AI software used by every enterprise development team. In JetBrains’ January 2026 survey of developers worldwide, GitHub Copilot led reported workplace use at 29%, followed by Cursor and Claude Code at 18% each. Those figures describe developer-reported use—not enterprise procurement share, company-wide deployment, or proof that one tool produces better results.
For an enterprise, the practical choice is between tools that assist inside a developer’s existing workflow and agents that can take on multi-step coding tasks. Either way, teams need to assess workflow fit, repository and tool permissions, administration, auditability, and measurable outcomes alongside the product name.
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Which AI software do developers use at work?
JetBrains’ April 2026 analysis combines its September 2025 and January 2026 AI Pulse surveys with its 2024 and 2025 Developer Ecosystem Survey waves. In the January 2026 snapshot, 90% of developers regularly used at least one AI tool at work for coding and development, and 74% had adopted specialized AI developer tools. Reported workplace use among developers worldwide was:
| Tool | Reported workplace use | What the figure means |
|---|---|---|
| GitHub Copilot | 29% | Developer-reported use at work in JetBrains’ January 2026 survey. |
| Cursor | 18% | Developer-reported use at work in the same survey. |
| Claude Code | 18% | Developer-reported use at work in the same survey. |
Copilot was reported by 40% of developers at companies with more than 5,000 employees in that survey. This is a result for that company-size group, not a claim that 40% of large enterprises have standardized on it. See JetBrains’ tool-use analysis.
#1 Best Overall
Other named tools in JetBrains’ comparison include JetBrains AI Assistant, Junie, OpenAI Codex, and Google Antigravity. Their inclusion in the comparison does not establish enterprise availability, controls, or program status. Survey use also does not show that an organization has purchased, approved, or deployed a tool across its workforce.
What kinds of AI development tools are enterprises using?
In-workflow assistants
Many tools assist within an IDE or repository workflow, for example by offering code completion, chat, or editing help. Teams should check which tasks a particular product supports rather than treating “AI coding assistant” as a uniform capability.
Rank #2
Coding agents
Agents can work through multi-step tasks, such as planning, making code changes, or contributing to a review or pull request. Because an agent may interact with repositories and other tools, its permissions and the actions it can take matter as much as its coding interface.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAnthropic’s 2026 State of AI Agents report says nearly 90% of surveyed organizations used AI to assist with coding. The survey was conducted with Material among more than 500 U.S. technical leaders in late 2025. Respondents reported agents freeing time in planning and ideation (58%) and in code generation, documentation, testing, and review (59% for each activity). These are vendor-sponsored survey findings, not a neutral census of enterprise deployments or controlled evidence of productivity gains. Read the report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does enterprise adoption mean the tools improve results?
No. Adoption, reported benefits, and measured business outcomes are different kinds of evidence. GitHub’s 2024 survey, conducted by Wakefield Research, covered 2,000 non-manager enterprise respondents at companies with at least 1,000 employees in the U.S., Brazil, India, and Germany, with 500 respondents in each country. The survey ran from February 26 through March 18, 2024. More than 98% of respondents said their organizations had experimented with AI coding tools for test-case generation. That shows reported experimentation; it does not establish that the tools caused better tests, higher code quality, or faster delivery. See the survey.
Broader enterprise AI findings should not be read as coding-tool rankings. OpenAI’s 2025 enterprise report uses de-identified, aggregated usage from its own enterprise customer base and reports that enterprise users said they saved 40–60 minutes per day. McKinsey’s 2026 global survey says 44% of respondents reported AI scaling across their enterprise, 80% said AI improved their individual productivity, and 37% reported some EBIT impact. These findings concern broader AI use and different outcomes; they do not identify the best coding product. OpenAI’s report and McKinsey’s survey provide their respective context.
Quick Recap
How should an enterprise compare AI development tools?
- Match the tool to the work. List the tasks developers need: completion, chat, code editing, testing, review, issue-to-pull-request work, or asynchronous agent execution. Product labels alone do not establish which functions are available.
- Map repository and tool access. Determine what an assistant or agent can read, change, and invoke, including external tools. GitHub documents policies for MCP server use and approved registries; it also notes that local VS Code agents are configured separately from GitHub-managed agents. Review GitHub’s enterprise controls documentation.
- Check administration and auditability. Establish whether administrators can manage agent sessions, set policies for third-party agents, and retain relevant audit events. GitHub documents these controls for its platform; their existence should not be assumed for other products.
- Test operational fit. Evaluate integration with the organization’s actual IDEs, repositories, CI, identity systems, and policies. The cited surveys do not establish a universally superior tool.
- Set outcome measures before rollout. Track adoption alongside quality, delivery, and cost measures. GitHub documents measures including active users, agent adoption, completion acceptance, model usage, and agent contribution. These indicate activity or output; alone, they do not prove causal productivity or quality improvements. See GitHub’s metrics documentation.
- Account for workflow and operating costs. Assess total cost and delivery outcomes, not license usage alone. McKinsey reports that costs can constrain AI use and points to workflow redesign among high performers; broad AI findings are not a substitute for measuring results in the development workflow being changed.
What the adoption figures can—and cannot—tell you
- They can show reported use in a defined survey population. JetBrains’ January 2026 figures are a useful snapshot of tools developers said they used at work.
- They cannot establish enterprise market share. Developer responses are not procurement records or verified company-wide deployment counts.
- They cannot rank tools on security, code quality, or total cost. The cited evidence does not support a definitive ranking on those dimensions.
- They do not prove business impact. Reported adoption, perceived benefit, and activity metrics should not be treated as controlled evidence of causal gains.
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