The new frontier of AI in software development is nothing short of remarkable. From generating code and producing documentation to automating work and performing security analysis, this technology is now used by software development companies worldwide.
Along with this increased use, questions and concerns are appearing about security, over-reliance on AI tools, transparency, and accuracy, among other considerations.
For the third consecutive year, Techreviewer surveyed software development companies about their use of artificial intelligence in software development. A total of 127 companies responded to this year’s survey, sharing interesting insights on code generation, productivity, training, and more.
To see survey results from the past two years, follow these links:
- AI in Software Development 2025: From Exploration to Accountability
- The Transformative Impact of AI in Software Development
Some of this year’s most significant findings:
- A substantial portion of production code is now being written by AI. 89% of companies say that their AI writes or offers assistance with some of their code, the median response indicates a figure of 26 to 50%, and around one in four state that AI carries out more than half the coding. 62.2% of the companies surveyed report that most or all of their developers use AI tools on a daily basis.
- On average, companies are simultaneously using about four AI tools,with the highest rate of adoption being for Claude/Claude Code at 93.7%, then ChatGPT/OpenAI at 77.2%, followed by Gemini at 58.3%, GitHub Copilot at 57.5%, and finally Cursor at 52.0%. The current situation is that of a multi-tool environment rather than a winner-take-all one, and this has real consequences for governance, data access, and client confidentiality.
- The proportion of companies reporting productivity gains of more than 50% increased fourfold, rising from 7.5% in 2024 to 30.7% in 2026 — a rise of 23.2 percentage points. Almost 97% said they had experienced some improvement, and there was not a single company that reported a decline.
- About 90% of the companies also suffered at least one adverse effect. In 52.8% of cases, the suggestions were hallucinated or incorrect, 44.1% stated that the AI had increased the amount of code review work, and 33.1% came across security or vulnerability problems in the code generated by the AI.
- 37.0% said that there was an over-reliance or a decline in developer skills, especially among junior developers, so that skill erosion became a problem cited more frequently than technical debt (23.6%) or an increase in defects (22.0%).
- Expertise is developed internally rather than being obtained through hiring. The amount of in-house training increased to 72.4%, whereas the dependence on hiring external AI specialists decreased from 35.0% in 2024 to 15.0% in 2026.
- Data privacy and security were the main implementation difficulty, being named by 58.3% of the respondents. However, 15.0% of companies have no AI training strategy established, and 21.3% have neither in-house AI specialists nor arrangements with providers.
Survey Methodology
In July 2026, Techreviewer carried out an online survey among 127 software development companies in its network.
Respondents. Participants included CEOs and presidents (48.0%), marketing managers (23.6%), CTOs (8.7%), CMOs (3.9%), and smaller numbers of founders, developers, project managers, sales and SEO staff, and other professionals. Companies’ headquarters are in the United States (26.8%), India (18.1%), Europe (approximately 30% across Poland, the UK, Estonia, Ukraine, Cyprus, Bulgaria, Croatia, and Germany), and more than a dozen other markets.
Sample composition and its limits. Respondents were recruited from Techreviewer’s own network of software development companies.
About three-quarters of the respondents (74%) employed fewer than 100 people, while one company had more than 1,000 employees, meaning that these results relate to AI adoption among small and medium-sized firms rather than enterprise-level adoption. The number of marketing managers and CMOs put together (27.5%) was greater than the total number of CTOs and developers (11.1%). Hence, technical figures such as the proportion of code written by AI or the rate of hallucinated suggestions are based on the views of senior leaders rather than on independent measurements or direct reports from developers.
Comments from experts. The participating companies were asked for their expert comments using an optional section in the survey. The respondents who were quoted were chosen on the basis of the particularity of their responses, not because of their connection with Techreviewer.
Sample sizes and margin of error. This report compares the results from 2026 (with n=127) to those from Techreviewer’s 2024 (n=44) and 2025 (n=83) surveys. Not all of the respondents answered all of the questions; the number of respondents per question was about 40 in 2024 and 79 to 81 in 2025.
At the 95% confidence level, the margin of error for a single reported share is about ±8.7 percentage points in 2026, ±10.8 points in 2025, and ±14.8 points in 2024.
Reporting conventions. Some questions allowed multiple selections, so those percentages add up to more than 100%. Year-on-year changes are given in percentage points (pp), that is, the simple difference between the two shares. In cases where a relative change is more useful, it is indicated as such.
For some questions, the way the question was phrased and the range of answer options changed from year to year, and where possible, the comparisons use categories that can be directly compared.
Respondent Profile
Software Development Company Size
The largest group of software development companies responding to the survey, at 32.3%, had 10–49 employees.
74% of surveyed companies had fewer than 100 employees, making this primarily a view of AI adoption among small- and mid-sized software firms.
Companies with 100 or more employees accounted for 26% of the survey sample.
The smallest group of software development companies responding to the survey, just 0.8%, had over 1,000 employees each.
Similar to the two preceding years, small- and mid-sized companies were strongly represented in the survey, with 74% of respondents employing fewer than 100 people.
Consequently, the findings mainly show the level of AI adoption in smaller software companies rather than in large ones. Since available budgets, in-house expertise, workforce arrangements, development requirements, and the degree of governance maturity can differ according to company size, this point should be taken into account when interpreting the results.
Work Roles of Survey Respondents
Together, CEOs, presidents and marketing managers made up 71.6% of the survey sample.
CTOs accounted for 8.7% of respondents, and developers accounted for 2.4%, equaling just 11.1% of the survey sample.
The number of marketing managers and CMOs together (27.5%) is greater than that of CTOs and developers combined (11.1%), which indicates that technical measures such as hallucination rates and the proportion of AI-written code are mostly reported by people who are not technical.
The survey provides a predominantly leadership-level view of AI in software development. Nearly 72% of respondents were CEOs, presidents and marketing managers. This is a strength when examining how companies evaluate AI in relation to budgets, operating costs, staffing, strategic direction, and service positioning.
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However, the composition of the sample must also be taken into account when interpreting results. There are metrics like the percentage of code written by AI and the rate of hallucinated or incorrect suggestions, but those are mostly based on leadership-level reporting. They aren’t about independently measured performance or direct feedback from developers.
Company Office Locations
The United States stood out significantly as the leading office location, representing 26.8% of participating companies, while India ranked second at 18.1%.
All remaining software development companies were distributed across Europe, North America, South Asia, Southeast Asia, and countries grouped under “Other.”
Roughly one in five respondents were headquartered in the EU, which is a relevant context for the survey’s results on regulation and data handling.
As the chart shows, the software development industry is geographically diverse and spans a wide range of international software markets. Although the United States and India have strong representation, over half of all companies are located elsewhere.
Buyers researching vendors in the United States can explore Techreviewer’s list of the country’s top US software development companies.
Research here backs common knowledge about the prominence of developer communities in the United States and India. In fact, GitHub’s 2025 Octoverse report also found that the United States had the largest developer community, at approximately 28 million developers, followed by India at approximately 21.9 million.
How Companies Are Using AI
How Are Companies Using AI in Software Development?
Key Takeaways
In 2026, code generation was the most common AI application, at 86.6%, followed by documentation generation at 75.6%.
Roughly two-thirds to 70% of companies use AI for requirements analysis and design (70.9%), code review and optimization (70.1%), and automated testing or debugging (66.9%).
Predictive analytics for project management and DevOps automation remained the least commonly named applications, despite both increasing from 2025.
AI adoption ranges from 65% to 87% for high-volume tasks such as generating code and documentation, analyzing requirements, reviewing code, and testing software — applications that produce outputs that development teams can inspect, test, and refine before the wider development process is affected.
Looking more closely at predictive analytics and DevOps automation, adoption drops to approximately 42%–47%. In any use case, expertise, training, and review will continue to be critical to successful implementation.
“Using AI to catch security issues, logic gaps, and architectural drift before merge has a better risk-to-reward ratio than using it to write the code in the first place.”
— Rishi Ram, CTO at GMTA Software
Changes in AI Use for Software Development Since 2024
Key Takeaways
Every named AI application area increased in use between 2025 and 2026. The “Other” category fell from 13.9% to 4.7%, and bug detection/security analysis has no 2025 baseline because it was measured for the first time in 2026.
UI/UX optimization made the largest three-year increase of all application types this year, rising 32.1 percentage points — from 32.5% in 2024 to 64.6% in 2026.
Bug detection/security analysis reached 54.3% in 2026, its first year of measurement by Techreviewer.
Requirements analysis and design rose 25.9 percentage points between 2024 and 2026, from 45.0% to 70.9%.
These findings reinforce the trends documented in Techreviewer’s 2024 survey and 2025 survey, which show AI use spreading beyond code generation into requirements, testing, UI/UX, and other development work.
To that end, in 2026, at least two-thirds of all surveyed companies were using AI for documentation, code review, requirements analysis, and testing, while UI/UX optimization approached the same level.
For software development companies, speed and consistency are definite benefits of this technology, but the need for proper review and accountability also becomes paramount as a result.
The AI Toolsets Powering Software Development
Key Takeaways
Claude/Claude Code were leaders in AI tool use by software developers, at 93.7%, followed by ChatGPT/OpenAI at 77.2%.
More than half of the companies surveyed used Gemini, GitHub Copilot, and Cursor in 2026.
There’s a sizable interest in self-hosted and open-source AI models, with 24.4% of respondents using AI tools such as Llama, Mistral, and DeepSeek.
Although Claude and ChatGPT had the broadest adoption, most software development companies also used at least one of the next three leading options. Survey findings suggest that AI in software engineering is becoming a multi-tool environment, not just a market where a single product will replace everything else.
While using different tools to complete tasks can be helpful, it can also complicate governance and require companies to develop clear policies on tool use, access to proprietary information, subscription costs, and output metrics. A software development company’s clients may also need to know what tools are being used and how those tools are being managed, for the sake of confidentiality.
“Tying our entire industry’s productivity to a handful of giant LLM providers is incredibly risky. If their platforms go down or their pricing changes, our workflows halt. We need AI as a copilot, not the pilot.”
— Shraddha Dubey, Technical Architect at ACL Digital
Want to see the bigger picture? In the full research, discover how much companies spend on AI development tools, what productivity gains they’re seeing, how much code AI generates, and which challenges and risks come with adoption.
Read the full research here: https://techreviewer.co/research/ai-in-software-development-in-2026
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