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Software Development Company Rates in 2026: Market Trends

Techreviewer.co — Mon, 30 Mar 2026 14:06:43 +0000

In 2026, software development company rates reflect a global market in rapid transition, and Techreviewer's latest survey shows how AI adoption, specialization, and rising demand for value are reshaping the pricing landscape.

Techreviewer is a B2B research and development platform that helps businesses find technology partners to improve software projects through detailed rankings and market insights.

This survey was conducted between January and February 2026 among 127 software development service providers. It provides a full picture of hourly rates and market trends, along with comparisons to 2023 and 2025. It also talks about how AI and machine learning trends have affected the industry.

To read Techreviewer's past rate studies, click on the links below:

Overview of Participating Software Development Companies

Company size

Company size (Number of employees)

The largest percentage of survey respondents, 40.9%, are in companies with 10–49 employees.
22.8% have 100 to 249 people in their companies.
9.4% of respondents work with a group of under 10 employees.
Only 0.8% of respondents are enterprise-level and have over 1,000 employees.

Key Takeaways

70% of the 127 professionals who took the survey said they worked for small- to mid-sized software development companies with up to 100 employees. The high number of non-enterprise organizations here may indicate the presence of specialized teams delivering complex projects without typical corporate overhead.

Locations of Software Development Companies

26.8% of the software development professionals in this survey were from the United States and 25.2% were from India.
Ukraine made up 5.5% of responses, which was the third-highest percentage overall.
Poland and Vietnam were the fourth leading respondents. Each represented 3.9% of companies surveyed.
Portugal, Bangladesh, the UK, and UAE were right behind Poland and Vietnam at 3.1% each.
Canada and Pakistan each made up 2.4% of the survey, which shows that the software development market is becoming more diverse.

Key Takeaways

The US and India both have some of the biggest pools of developer talent globally. India alone is estimated to have more than five million software engineers and one of the biggest developer communities on GitHub.

The gap between the top two locations and all others was expansive. This shows how big the US and Indian technology sectors are in the global marketplace.

Despite ongoing wartime disruptions in Ukraine, the country's tech ecosystem keeps growing. A report by IT Ukraine found that Ukraine had over 2,600 active tech startups in 2024, and the ecosystem had tripled in value since 2020.

Main Target Regions of Software Development Companies

North America was the most targeted region in the software development market, coming in at 76.4%. Next was Europe at 68.5%.
The Middle East was third-most targeted at 40.2%, just ahead of Australia and New Zealand at 38.6%.
Southeast Asia attracted 19.7% of respondents, followed by 18.1% for Latin America and 14.2% for South Asia.

Key Takeaways

The traditional markets in North America and Europe are very important for software development. However, companies are moving into new areas, which shows that the global market is becoming more diverse.

List of US software development companies

The Middle East, Australia and New Zealand, and East Asia are all growing quickly. This aligns with trends, AI initiatives, and the world's growing digital infrastructure.

Survey respondents are showing mixed interest in other regions. This could be due to volatile economic conditions, regional stability considerations, and business access constraints in those areas.

Comparison of Main Target Regions

North America

In 2026, 76.4% of respondents said they're targeting North America, a moderate decline from 2025 and a more significant drop from 2023. While interest in other regions is growing, North America continues to represent the strongest market for software development service providers.
76.9% of respondents prioritized the North American market in 2025.
In 2023, 86.4% of respondents marketed their services to North America.

Europe

Europe was the second-most targeted region for software development services in 2026, with 68.5% of companies reporting high interest in this market.
Tech-forward and innovative, Europe continues to invest heavily in technology education and digital transformation initiatives, and the European Commission's Digital Europe Work Programme allocated €1.3 billion for 2025–2027 to deploy strategically important technologies.
73.2% of respondents prioritized Europe in 2025, and 72.8% did so in 2023.

Middle East

The Middle East continues to rank third among regions for companies seeking software development clients - reaching 40.2% in 2026 from 38.5% in 2025.
31.8% of companies targeted the Middle East for services in 2023.
From heavy investment in smart cities and AI initiatives to governments pushing large-scale digital transformation strategies, the Middle East is emerging rapidly in the technology space.

Australia and New Zealand

Over 38% of all software development companies surveyed are prioritizing marketing their offerings to Australia and New Zealand in 2026.
With startup ecosystems growing quickly in these countries, the demand for technology partners is expected to increase simultaneously.
In 2025, 30.8% of companies targeted Australia and New Zealand for software development, up from 28.8% in 2023.

East Asia

Interest in East Asia rose significantly in 2026, with 22.8% of companies reporting their intent to target the region. Growing investment in AI and digital transformation, along with advances in manufacturing, may be why this area has become so attractive recently.
The share of companies targeting East Asia was just 9.2% in 2025 and 9.1% in 2023.

Southeast Asia

Southeast Asia has seen slightly more interest from software development companies this year than in 2025, rising to 19.7% from 16.9%.
21.2% of companies targeted this region in 2023.

Latin America

Dropping moderately from the 20% mark, 18.1% of companies reported planning to target Latin America for services in 2026. This is still a notable change from 13.6% in 2023.

South Asia

South Asia saw a slight decline in interest from software development companies in 2026, with 14.2% reporting plans to target the region compared to 15.5% the previous year.
6.1% of companies targeted this region in 2023.

Africa

Interest in Africa has more than doubled since 2025, with 13.4% of companies reporting their plans to target the continent for new business.
In 2023, this number reached 12.1%. The drop to 6.2% in 2025 could be attributed to temporary market uncertainty and slower investment in digital infrastructure across some national economies in Africa.

Current Price Range

$20/hr (8.66%): This level of service is common in areas where operating costs are lower.
$20–29/hour (22.05%): This price range is between mid-tier and low-cost providers. It often attracts businesses looking for expertise and value in the market.
$30–$49/hour (37.8%): Statistically, the largest category reported in 2026, this mid-tier pricing level is often signified by affordability and specialization.
$50–99/hour (22.0%): Here is where high-quality work and experience come together and offer a unique service that's still reasonably priced.
$100–149/hr (6.3%): *Well-established, enterprise-level software development companies typically offer services in this price range.
*$150–199/hr (3.15%): The premium pricing in this category represents the highest level of service available, with developers working alongside niche industries and enterprise-level clients.

Key Takeaways

Pricing in the $30–49 range was the largest tier reported in the survey, and reliability, coupled with cost-effectiveness, may have pushed it to 37.8%.

Research from the KPMG's 2025 outsourcing survey reinforces these results, as it reported that companies increasingly prioritize outsourcing to partners that can combine technical capability and innovation with cost efficiency.

The high prices in this group mean that the service is the best available, with developers working with niche industries and big businesses.

Price Range Comparison: 2023 vs 2025 and 2026

In 2026, the three core price ranges of $20–29/hr, $30–49/hr, and $50–99/hr shifted back to an uneven distribution, similar to what happened in 2023. Those core ranges were more balanced in 2025.S
The $30–49/hr price range was the clear leader in 2026 at 37.8%, and it represents the highest concentration of any price tier across all three years.
Higher-priced service tiers have increased steadily year over year, revealing a growing demand for enterprise-level and specialized expertise.
Higher-priced services between $100–149/hr and $150–199/hr have steadily increased year over year, indicating growing demand for specialized or premium expertise.

*> R‍ead the full research here: https://techreviewer.co/blog/software-development-company-rates-in-2026
*

IT Labor Market in 2025

Techreviewer.co — Fri, 13 Feb 2026 11:53:10 +0000

71.6% companies rate their hiring pool as strong. At the same time, 53.7% struggle to find candidates with the required skills.

This contradiction defines the IT labor landscape in 2025: while talent is available, finding the right fit is increasingly challenging. Skills evolve faster than roles, salary expectations are rising, and hiring outcomes vary by specialization and timing. As a result, workforce decisions for IT service companies are becoming more complex each year.

This research was conducted by Techreviewer, an independent platform that analyzes and ranks IT service companies. We leverage a network of over 9,500 service providers to help IT services executives, owners, and HR leaders understand current hiring conditions and benchmark workforce strategies.

At the beginning of 2026, we analyze the results of 2025 to provide an up-to-date view of the IT labor market and its ongoing changes. This research is the second IT labor market report published by Techreviewer. The first study, IT Labor Market in 2024, explored hiring demand, retention approaches, labor market expectations, and more. This year's report continues that work, allowing us to track how expectations and practices evolve over time.

In this report, readers will find:

An assessment of talent availability, skill expectations, and hiring practices.
A year-over-year view of hiring demand and recruitment pressure.
An analysis of retention drivers, work models, and productivity.
A forward-looking outlook on AI impact and demand for IT professionals in 2026.

Key Research Takeaways

Talent availability is rated positively by 71.6% of companies, and technical interviews remain the dominant validation method, used by 94.0% of respondents.
Upskilling and reskilling are standard operating practicesthat companies implement to get expertise, with 83.6% of respondents running internal development programs.
Hiring demand shows high volatility, with 43.3% of companies reporting growth in 2025 and 26.8% reporting a decline.
Recruitment pressure focuses on skill fit, with 53.7% of companies reporting difficulty finding candidates with the required expertise.
Companies address hiring challenges primarily through internal capability building, led by training initiatives at 50.7% and remote work adoption at 49.3%.
Most respondents view their implemented retention strategies as highly or moderately effective, with 77.6% rating their retention approach positively.
Companies cite daily work conditions as the main drivers of worker retention, led by work-life balance at 61.2% and salary and benefits at 55.2%.
Hybrid and remote work models dominate preferences, and productivity remains stable or improves for 79.1% of companies.

Methodology

This research is based on an online survey conducted in December 2025 among IT services companies.

We collected responses through Techreviewer's internal network of service providers. The survey ran over the course of one month and targeted executives, owners, and senior decision-makers in IT services organizations.

In total, the study includes responses from more than one hundred IT services companies across multiple regions and company sizes. The questionnaire covered hiring demand, talent availability, recruitment practices, retention, work models, and expectations around AI and skills.

Respondent & Company Profile

Respondent's Role Within the Company

Overall, the data reflects a business-led view of the IT job market.

‍Most respondents are owners and senior decision-makers, including CEOs and founders, accounting for 55.2% of the sample.‍
Marketing roles account for 31.5% of respondents, covering managers, CMOs, and other marketing specialists.‍
HR roles contribute 6.0% of responses.‍
Sales and business development roles make up 5.9%, combining sales representatives and business development leads with a shared commercial focus.‍
CTOs account for only 1.4% of respondents, so technical leadership is minimally represented in this review.

Overall, the data reflects a business-led view of the IT job market.

Experience Level of Survey Respondents

The survey primarily captured responses from experienced professionals.

55.7% of respondents report having 4–12 years of IT experience. Another 38.3% have 13–20 years. Early-career respondents are rare: 6.0% have under 3 years of experience.

Overall, 64.3% of respondents have more than eight years of IT experience, making the results more representative of mature, informed perspectives than entry-level views.

Company Size of Respondent Companies

47.8% of respondents work in companies with 10–49 employees. Another 13.4% come from teams with fewer than 10 people.

Mid-sized companies are also well represented. 35.8% of companies fall into the 50–249 employee category, split between 50–99 employees at 20.9% and 100–249 employees at 14.9%.

Large companies are rare: 3.0% of respondents report 250–999 employees.

Geographic Distribution of Respondent Companies

The survey has a broad international reach.

India accounts for 21.2% of respondents, and the United States for 20.3%, making them the two largest hubs and together representing over 40% of responses.

Europe is represented primarily by Poland (9.6%), Lithuania (5.8%), and the United Kingdom (4.1%). Vietnam contributes 5.8% and is the largest Asian hub outside India.

The remaining 33.2% of responses come from other European countries and the Asia-Pacific region, with smaller contributions from Latin America and the Middle East, each representing only a minor share individually.

Talent Availability & Skill Expectations

Key Insights:

Respondents describe a hiring environment that functions but rarely feels easy. Candidates are generally available, as 71.6% of respondents support this view, while finding the right fit in seniority, skills, and salary often takes time.
Assessment focuses on hands-on experience and practical knowledge. Technical interviews are the lead, with 94.0% of respondents using them, supported by coding tests and hands-on tasks. Portfolios add value, while certifications and references usually play a supporting role.
Employers take a pragmatic approach to soft skills. They prioritize problem-solving at 37.3%, followed by communication and teamwork. Adaptability carries more weight than process-oriented traits.
Upskilling has become part of standard practice. 83.6% of companies run learning programs and treat continuous development as a basic requirement for retention and competitiveness.

Satisfaction With Talent Availability

Respondents generally describe the availability of experienced specialists as satisfactory. 71.6% express satisfaction with their current hiring pool, indicating that qualified candidates are usually available.

At the same time, 22.4% of respondents report reservations, indicating recurring friction over skill fit, seniority, and salary expectations. These responses reflect a form of dissatisfaction rather than confidence in availability.

Clear dissatisfaction remains limited. Only 6.0% describe talent availability as poor, suggesting that severe shortages affect a relatively small share of companies.

Overall, companies view the hiring pool as workable. A large satisfied group coexists with a smaller dissatisfied segment, reinforcing the view that candidates are available, while smooth hiring remains uneven.

How Companies Evaluate Technical Skills

Technical interviews dominate: 94.0% of companies use them as the primary filter.‍
Many teams add proof-based checks. 65.7% run coding tests, and 52.2% use hands-on project assessments.‍
Signals from past work are also standard: 43.3% of respondents review experience and portfolios to confirm real delivery.‍
Certifications and reference checks play a secondary role, with 19.4% and 22.4% mentioning them, respectively.‍
Third-party assessments remain uncommon, with 7.5% of respondents reporting use. Tooling familiarity and team fit as technical proxies appear rarely, each cited by 1.5%.

The pattern is clear: interview first, practice second, paperwork last.

Soft Skills Prioritized in Hiring

Soft-skill expectations focus on thinking and collaboration.

‍Problem-solving comes first. 37.3% of respondents say employers prioritize the ability to work through new tasks, even when requirements shift, or obstacles appear.‍
Communication and teamwork follow as common expectations, cited by 22.4% and 19.4% of respondents, respectively. These skills reflect the realities of distributed and cross-functional IT work.‍
Adaptability and critical thinking reinforce the same demand. 16.4% of respondents cite adaptability, and 11.9% highlight critical thinking, signaling the need for engineers who can adapt quickly to new technologies and challenge weak assumptions when proposing solutions.‍
Time management and similar process traits appear less often. Personality traits receive minimal attention across responses.

Overall, companies prioritize flexible thinking and team fit over individual personality traits.

Availability of Upskilling and Reskilling Programs

Upskilling and reskilling programs are widely adopted.

83.6% of companies invest in structured learning to keep employees up to date with evolving technologies. This indicates that continuous skill development is expected from the employee side.

Only 16.4% of respondents report having no such programs, suggesting limited tolerance for skill stagnation in the IT services market.

The data shows that internal skill development is now a key mechanism for retention and competitiveness.

Linux Foundation research reinforces this pattern. In its 2025 Linux tech talent study, 97% of organizations describe upskilling as strategically important, and 90% already run technical training programs.

Recent Changes in IT Hiring Demand

Key Insights:

Hiring demand over the past year shows net growth: 29.9% of companies report stable demand, 43.3% report growth, and 26.8% report decline. Compared to 2024, stability fell, and volatility increased. There is no clear trend - the situation depends on specific companies.
Central recruitment pressure shifted to finding the right skills. The share of respondents reporting difficulty finding candidates with the right skills increased from 38.1% до 53.7% compared to 2024. The reported shortage of qualified candidates jumped from 28.6% to 49.3%.
Companies respond primarily through capability-building measures. 50.7% of companies rely on training and development as a core response to recruitment challenges. 44.8% focus on upskilling current employees, up from 38.0% in the previous year.
Remote work rose the fastest to 49.3% from 29.0%. Regional expansion fell, and salary increases became less common.
Overall, demand remains net-positive, but hiring is increasingly uneven and skill-driven. Companies are shifting from paying more and expanding searches to focusing on training and smarter hiring.

Change in Hiring Demand Over the Past Year

Hiring demand over the past year has been split.

29.9% of companies report stable demand. An equal share reports a slight increase. Another 13.4% saw a substantial increase.

Declines are less common overall. 26.8% of companies report a decrease, either slight or significant.

Overall, 43.3% of companies report growth, outweighing the 26.8% reporting decline. This indicates selective hiring and cautious expansion.

Hiring Demand: Year-Over-Year Comparison

Compared to 2024, the share of companies with stable demand has declined, while slight growth has become more common. At the same time, significant declines have increased, indicating higher volatility.

Still, demand for IT professionals remains net-positive, but the landscape shows more volatility than last year, with fewer companies in a stable state and more reporting hiring pressure or contraction.

Research from World Economic Forum supports this pattern at the global level. Technology roles, including software developers, AI specialists, and data specialists, remain among the fastest-growing worldwide.

Growth continues, yet it concentrates in specific roles and projects. Companies that rely on static hiring plans face a higher risk, while those that align recruitment closely with delivery pipelines and skill demand can navigate volatility more effectively.

Key Recruitment Challenges in 2025

Recruitment challenges in 2025 cluster around skills and pay.

The biggest issue is fit. 53.7% of companies report difficulty finding candidates with the required skills, and 49.3% of companies point to a general shortage of qualified professionals. This reflects a specialization gap rather than a lack of available engineers.‍
Cost adds pressure. 37.3% of companies face high salary expectations, often reinforced by competition from other employers, cited by 26.9% of respondents.‍
Process and brand issues matter less. Around 13–14% of companies mention long hiring cycles or weak brand recognition. Location constraints and remote work adaptation affect a smaller share of companies.

Recruitment Challenges: Year-to-Year Comparison

Compared to 2024, in 2025, challenges moved from "where to hire" to "who fits."

Skill fit became harder. The share of companies reporting a lack of candidates with the right skills rose from 38.1% to 53.7%. The reported shortage of qualified candidates also jumped, from 28.6% to 49.3%. The gap is growing.‍
Salary pressure stays high and increased slightly year over year.‍
Location barriers weakened. Limited local talent and remote-work adaptation are mentioned much less in 2025, which suggests distributed hiring is now routine.‍
Process and retention issues rose modestly, adding friction as companies chase a narrower set of specialists.

Challenges have become less geographic and more structural, driven by deeper specialization, higher expectations, and increased competition for scarce skills.

Gartner highlights a structural source of this pressure. Its research shows that AI-assisted hiring has increased resume inflation, candidate misrepresentation, and noise in early screening. As a result, employers spend more time validating real expertise.

Measures Used to Address Recruitment Challenges

Companies address recruitment pressure through a combination of short-term access measures and longer-term capability investments.

1. Fast-response measures
To expand the candidate pool quickly, companies rely on remote work and geographic reach.

Remote work options are used by 49.3% of respondents, allowing access to talent beyond local markets.

35.8% of companies apply regional expansion to increase reach in locations with stronger talent supply.

2. Capability-building measures
At the same time, companies invest in strengthening internal capacity.

50.7% of respondents use training and development programs to build skills aligned with delivery needs.
44.8% of companies practice upskilling current employees to address emerging skill gaps.

Investments in culture and benefits appear in 37.3% of responses and support both attraction and retention.

3. Financial measures
Direct financial incentives play a supporting role.

28.4% of companies increase salaries, while 10.4% use sign-on bonuses to address specific hiring gaps.

Overall, the data shows that companies combine rapid hiring tools with structural investments in skills and engagement, reflecting a preference for sustainable recruitment.

Measures to Address Recruitment Challenges: Year-to-Year Comparison

From 2024 to 2025, companies shifted toward building talent in-house.

Training and development grew the most and became the top response. Upskilling current employees also gained weight, showing a move toward growing skills internally.

Remote work saw the fastest rise, nearly doubling. Flexibility is now a standard hiring tool.

Companies rely less on regional expansion and salary increases than they did a year earlier.

Compared to 2024, responses moved from quick hiring fixes to longer-term solutions.

This pattern also appears at a global scale. Linux Foundation research shows that organizations are 3.2× more likely to invest in upskilling existing employees than to rely on external hiring. Remote and flexible work practices are widely used to expand access to talent while keeping compensation growth under control.

Companies increasingly treat training, upskilling, and flexibility as core operating levers rather than HR add-ons. The ability to develop and redeploy skills internally now defines hiring resilience more than aggressive pay tactics or geographic expansion.

R‍ead the full research here: https://techreviewer.co/blog/it-labor-market-in-2025

Originally published at Techreviewer.co.

AI in Software Development 2025: From Exploration to Accountability — Survey-Based Analysis

Techreviewer.co — Fri, 12 Dec 2025 12:33:28 +0000

For the second year in a row, Techreviewer has run an online survey about AI’s role in software development. We use our global network of software development providers to track how AI adoption is changing across teams, regions, and company sizes. You can find last year’s survey, from 2024, here: Impact of AI in Software Development: A Survey-Based Analysis 2024.

In 2024, most companies were experimenting with AI in software development and cautiously trying it out. They tested tools, looked at different use cases, and measured the results, often using unique strategies and with limited in-house experience. Many software developers also asked, “Will programmers be replaced by AI?”

The 2025 survey takes a closer look at how AI adoption has changed over the past year. It examines what’s working, where companies and developers face challenges, and how attitudes and skills are developing. With input from professionals around the world, this research gives a clear picture of where the industry is now and where it’s going.

Executive Summary

By 2025, companies moved from experimenting with AI to making it part of their daily work. Now, 97.5% of companies of all sizes use AI as a key part of their internal processes.
Key use cases include code generation, documentation generaThe main ways companies use AI are for code generation, creating documentation, reviewing and improving code, and automating testing and debugging. The impact is clear: 82% of respondents saw at least a 20% boost in productivity, and 25% saw gains of over 50%
Companies are building in-house AI expertise instead of relying on third-party tools or providers. 63.3% of respondents nurture the skills of their employees through in-house training programs.
AI maturity presents challenges, as transparency, ethics, and data privacy have become significant concerns. The question is no longer if artificial intelligence should be used, but how to govern and sustain it responsibly.

2025 is a turning point. The industry is moving from simply adopting AI to taking responsibility for how it is used.

Respondent Profile

Demographics

The majority of survey respondents represent small to mid-sized software development companies. Specifically:

Over 80% of respondents work at companies with fewer than 250 employees.
The largest segment (27.2%) consists of small businesses with 10–49 employees.
Only 1.2% of responses came from large enterprises with more than 1000 employees.

The company size distribution reflects the presence of small- to medium-sized companies that embrace innovations and are inclined to implement new technologies quickly.

Respondent Roles

The survey reached a broad mix of roles in software development companies, including executives, leadership, strategies, and more:

Nearly 67% of respondents hold executive-level roles, including CEOs or Presidents (35.4%) and Marketing Managers (31.7%).
Technical leadership was represented through CTOs (8.5%) and Project Managers (4.9%).

Additional respondent roles are CMOs (7.3%), Sales (3.7%), HR (2.4%), Digital/SEO/Marketing executives (2.4%), founders (1.2%) and 2.4% for roles that didn’t fit into standard categories.

This breakdown shows that adopting AI is important for all roles in a company, not just for engineering teams.

Office Location

Respondents came from a wide geographic range, with the majority headquartered in several tech hubs:

India (27.2%) and the United States (23.5%) together account for over half of all responses.
Other notable contributors include Poland (7.4%), Vietnam (6.2%), and Canada (6.2%).
Bangladesh, Estonia, Pakistan, Ukraine, Armenia, Lithuania, and the United Kingdom each made up around 2.5% of the total.

An additional 12% of participants, with a few respondents from each region, include those from Zimbabwe, Hungary, China, Portugal, Croatia, North Macedonia, Spain, Brazil, Cyprus, Hong Kong, and the Netherlands.

This wide range of participants shows that AI in software development is being adopted around the world, not just in traditional tech hubs.

Artificial Intelligence Adoption: Where Are We Now?

Key Insights:

97.5% of companies have adopted artificial intelligence in software engineering, up from 90.9% in 2024.
Artificial Intelligence adoption is approaching industry-wide saturation.
2.5% of non-adopters cite cost, integration complexity, and unclear ROI as main barriers.

Current Adoption Rate

Question: Are you currently integrating AI technologies in your software development processes?

According to the 2025 survey:

Ninety-seven point five percent of companies report that they are currently integrating AI technologies into their software development processes.
Only 2.5% of respondents have not yet adopted AI.

In comparison, the 2024 survey showed that 90.9% of companies had already adopted AI, while 9.1% had not. This year-over-year shift indicates a clear trend: AI is transitioning to near saturation in the industry.

Reasons for Not Implementing AI

Among the small minority (2.5%) of companies that have not yet adopted AI, several key barriers were identified:

High implementation costs or a lack of financial resources to initiate AI implementation for software development.
The complexity of integrating artificial intelligence with existing systems or workflows (mentioned twice, indicating a recurring concern).
No clear business need or uncertain benefits from Artificial Intelligence adoption.

These challenges show that the few companies not using AI are not skeptical about it, but are more concerned about whether it fits their needs, works with their systems, or will pay off.

Future Plans for Artificial Intelligence Adoption

Among those 2.5% who have not yet adopted AI:

50% of companies have no intention of integrating AI in software engineering in the foreseeable future.
50% indicated a timeline of 12+ months, suggesting a slower, more cautious approach.

This shows that while almost all companies are adopting AI, a small group is still unsure about its value for software engineering or simply doesn’t have the resources to use it.

AI in Action: Use Cases, Implementation Approaches, Goals, Results

Key Insights:

AI is now core to the SDLC: 72.2% use it for code generation, 67.1% for documentation and review.
Adoption is moving upstream: 53.2% use AI in requirements analysis and 48.1% in UI/UX, marking a shift from execution to planning.
New mainstream areas in 2025: DevOps automation (38.0%), code review, and predictive analytics. AI now supports full-cycle delivery.
13.9% report custom use cases: From architecture to marketing, the industry shows growing creativity beyond developer tooling.
Maturity is rising: 49.4% have used artificial intelligence for more than 1 year (vs. 32.5% in 2024); only 2.5% are new adopters.
Impact is real: 92.4% report positive SDLC effects; 82.3% gained ≥20% productivity, 24.1% exceeded 50%.

Areas of Utilization

AI is being applied across nearly every stage of the software development lifecycle, from planning to deployment. The 2025 survey reveals a clear hierarchy of use cases, with certain areas showing particularly high adoption:

Code generation: leads the list, with 72.2% of companies using artificial intelligence to assist or automate code writing.
Documentation generation (67.1%) and code review and optimization (67.1%) follow closely, highlighting the role of artificial intelligence in producing comprehensive documentation and improving code quality.
Automated testing and debugging are used by 55.7% of companies, demonstrating AI’s role in enhancing software reliability.
Requirements analysis and design (53.2%) and UI/UX optimization (48.1%): show that artificial intelligence is extending into earlier and more subjective stages of the development process.
Predictive analytics for project management (39.2%) and deployment/DevOps automation (38%) round out the top categories.

In addition to the major categories, 13.9% of respondents mentioned niche or company-specific use cases grouped under “Other.” These included:

Architectural design.
DevOps pipelines and CI/CD.
Bug detection and fixing.
Voice interfaces and AI-powered design tools.
Security enhancement.
Refactoring and optimization.
Project management and scoping.
Client communication.
Marketing.
ATS integration.
AI project development is a core service.

These answers show that companies are finding more creative ways to use AI, not just for developer tools but also in wider business processes and products.

Areas of Utilization 2025 vs 2024 Comparison

AI is now deeply embedded across multiple stages of the software development lifecycle. According to the 2025 survey, the top areas where companies apply artificial intelligence include:

Key Shifts Since 2024

Code generation remains the leading application, with usage rising from 67.5% to 72.2%
Automated testing and debugging declined slightly in relative share (from 62.5% to 55.7%), possibly due to the diversification of use cases.
Requirements analysis, UI/UX optimization, and predictive analytics showed notable growth.
Documentation generation and code review/optimization emerged as new high-ranking use cases in 2025, absent from last year’s top list.
The rise of deployment and DevOps automation in 2025 shows that AI is now having a bigger impact on operations, not just on development.

Maturity and Timeline of Implementation

Nearly half of the companies (49.4%) have been using AI tools in software development for over a year.
34.2% started integrating artificial intelligence within the last 6–12 months.
13.9% began 3–6 months ago.
Only 2.5% are new adopters, having started within the last 3 months.

Maturity and Timeline 2025 vs 2024

In 2024, only 32.5% of companies had used artificial intelligence for over a year. This figure has jumped to 49.4% in 2025. Meanwhile, the proportion of recent adopters has declined, especially in the < 3 Months category (from 7.5% in 2024 to 2.5% in 2025).

Today, AI and software engineering are closely connected. This change shows that the industry is maturing, with more companies moving from trying out AI to making it a permanent part of their work.

Perceived AI Impact on Software Development Life Cycle (SDLC)

In 2025, the vast majority of respondents view artificial intelligence as a net positive in the software development lifecycle:

55.7% describe the impact as significantly positive
36.7% as somewhat positive
Only 6.3% were neutral, and 1.3% saw a negative impact

Compared to 2024, the perception has become more optimistic. A year earlier, only 40% of companies reported a significant improvement, and 17.5% were unsure of the impact.

Measured Productivity Gains

When as aked about actual productivity improvements in 2025:

58.2% of companies reported a 20–50% increase in productivity.
24.1% reported an increase of more than 50%.
11.4% saw an improvement of less than 20%.
6.3% observed no measurable improvement.

This marks a clear upward shift in the benefits of AI in software development from 2024, where:

52.5% reported a 20–50% gain.
Only 7.5% had improvements above 50%.
27.5% had minor gains under 20%.

Overall, the 2025 data show that people see more value in AI, and the real benefits in software development are clearer. Productivity gains are now bigger and more common.

AI Talent and Skill Landscape

Key insights:

In 2025, 59.5% of companies rely on in-house AI specialists, indicating a strong trend toward building internal Artificial Intelligence development teams.
Dependence on pre-built AI tools dropped to 17.7%, signaling a reduced appetite for off-the-shelf solutions.
Despite progress, 12.7% of companies still lack both in-house expertise and external support, pointing to resource gaps.
The most common way companies train for AI is through in-house programs (63.3%), followed by online courses and hiring from outside. Still, almost 20% of companies don’t have a clear plan for upskilling.
Collaborations with universities have grown from 2.5% to 20.3%, reflecting the early stages of industry-academia alignment on AI.
The majority, 83.9% of companies, rate talent acquisition as very easy or neutral, and only 16.1% find it difficult, indicating that talent scarcity is no longer a universal bottleneck.
The main question has changed from “Will AI replace software engineers?” to “How can junior developers gain experience in the age of AI?”

Talent and Artificial Intelligence Expertise

In 2025, most companies prioritize internal capability building when it comes to AI:

59.5% have dedicated in-house Artificial Intelligence specialists, indicating a strong focus on nurturing internal expertise rather than relying on third-party tools and providers.
17.7% use pre-built AI tools and services: in second place, with a serious lag from the growing internal expertise.
12.7% report having no in-house expertise or third-party partnerships: these are likely early-stage adopters or companies with serious resource constraints.
7.6% partner with external Artificial Intelligence providers.
1.3% identify as Artificial Intelligence providers themselves or combine internal teams with external partners.

These numbers show that most companies prefer to build their own AI expertise, but a notable group still relies on outside tools or services.

R‍ead the full research here: https://techreviewer.co/blog/ai-in-software-development-2025-from-exploration-to-accountability-a-global-survey-analysis

Originally published at Techreviewer.co.

AI in Development Workflows: Balancing Speed, Security, and Responsibility

Techreviewer.co — Tue, 09 Dec 2025 09:55:37 +0000

AI is changing how software engineers build and deliver solutions at an incredible rate. With more than 84% of developers using AI, according to Techreviewer’s research, it’s obvious that it’s now commonplace. From helping developers write code to debugging, it streamlines the entire development process.

Although the use of AI for workflow automation improves productivity, it’s not without its issues. Jens Wessling, Chief Technology Officer at Veracode, explains that, “Despite the advances in AI-assisted development, it is clear security hasn’t kept pace.” Errors, security risks, and legal and ethical concerns are some drawbacks of its adoption.

To address these challenges, organizations must develop strategies to identify and prevent risks. Transitioning from the issues to practical solutions, the following sections examine the importance of human oversight in mitigating such problems while maximizing the efficiency of AI.

The Reality of AI Code Errors: What Developers Are Actually Experiencing

LLMs are trained on a wide amount of data to generate results for developers quickly. However, these models do not understand the context or the unique requirements of every project. They make code completions based on past examples or even hallucinate results.

Stack Overflow's Developer Survey found that 66% of developers identify nearly-correct solutions as their primary frustration. Developers often encounter different mistakes with AI-generated code, including:

Logic mistakes: The system can produce correct code that doesn’t apply to the intended business logic. This occurs when it fails to consider the project’s context or specific needs. Its output might look right, but it doesn’t actually solve the obvious problem or account for edge cases.
Insecure or vulnerable code: Techreviewer’s data reveal that 46.7% of developers experience security risks with AI-generated code. Studies also show that while these models are powerful for automating workflows, they often overlook security best practices.
Outdated or incompatible libraries: Models trained using outdated libraries may suggest solutions that do not adhere to current best practices. For developers, this training gap results in broken code and wasted hours spent implementing outdated approaches.

About 20% of respondents in Techreviewer’s survey have noticed outdated practices. Conversely, 33.3% experience incorrect API usage, including outdated SDK calls.

Incorrect assumptions: Another risk with adopting an AI development workflow is incorrect assumptions, particularly when developers enter ambiguous prompts. Without explicit details, the model may generate solutions based on statistical patterns rather than the developer’s intent.

Software developers working with AI workflows verify output before implementation. Only about 18% trust AI-generated code, treating it as a draft rather than the final product.

Why Human Oversight Remains Essential

Developers treat AI outputs as drafts and review them before use, highlighting the need for human oversight.

Teams integrating these models have built-in verification steps in their workflows to validate the generated code. 62% of the respondents from the Techreviewer research say they always verify AI-generated solutions. Up to 26% of them say they validate outputs often.

The method of verification differs for each company, but generally includes manual code reviews by engineers, automated testing suites, and static code analysis tools. 77.8% of respondents apply a human-first approach before using the given output in production. Teams may also layer in additional reviews or automated checks to increase accuracy.

Automated tests: Teams typically use established automated testing frameworks to run both unit and integration tests on the generated code. According to 48.9% of respondents, this method ensures that code behaves as expected and correctly handles edge cases.
Static analysis: Approximately 17.5% of teams utilize static analysis tools – software that examines code for potential errors, vulnerabilities, or deviations from coding standards – after running automated tests. This adds an extra layer of quality control before code deployment.
Peer reviews: A less common method, with only 18.8% of respondents participating, involves having fellow developers manually review the generated code for logical errors, adherence to standards, and security concerns. This emphasizes independent validation within teams.

There are other methods, such as checking generated code against documentation, runtime monitoring, and cross-referencing with multiple AI tools. The presence of human oversight, regardless of the approach, ensures that leveraging AI doesn’t introduce potential issues.

Ethical Dilemmas: IP, Data Privacy, Bias, and Disclosure

The integration of generative AI in the development workflow also raises serious ethical concerns. About 80% of respondents in the research encounter ethical dilemmas occasionally. Privacy risks and intellectual property concerns are the top concerns, with both at 45.5%.

These models are trained on publicly available code, some of which may have copyright licenses. Intellectual concerns arise when developers are unsure about who owns the code generated by the system. Also, are they infringing copyrights by copying this code?

There are also concerns about data privacy when exposing company code to external artificial intelligence tools. When developers feed these systems sensitive information, there’s a possibility of leakage.

Stanford mentioned in a recent article how some companies use user feedback to train their AI models and improve their capabilities. This is a cause for concern for development teams, as they must think twice when sharing information with their AI agent.

Another ethical dilemma arises from the potential bias that can occur when AI is trained on biased or non-inclusive datasets. This is a significant issue for approximately 43.2% of respondents.

Transparency and disclosure are primary concerns for developers as well. About 40% disclose the use of LLMs occasionally, 37.8% disclose rarely, while 20% do not reveal it at all.

Additionally, whether teams openly disclose the use of AI depends on factors such as contract terms and the type of project. All these dilemmas listed above create real legal and organizational risks if left unaddressed.

Balancing Efficiency With Responsibility: Best Practices for Safe AI Adoption

The solution to code errors and ethical dilemmas is to adopt the use of LLMs responsibly. Proven steps and best practices for development teams include:

Always verify generated code: As Techreviewer shows, teams should use manual reviews and automated testing to verify their work, treating AI-generated code as a starting point rather than the final product.
Prioritize data privacy: Teams should always limit the amount of sensitive details they share and anonymize them when possible. This includes everything from proprietary code to credentials and user data.
Establish internal guidelines: Companies will benefit from establishing clear procedures on how members can adopt these tools. Having guardrails will help keep the team in check and reduce potential risks.
Adopt AI in high-ROI areas: Avoid using these tools for complex logic and architectural decisions that require human judgment and discretion. Instead, teams can focus on areas where they add measurable return, such as debugging, code generation, and documentation.
Developer Upskilling: Companies must encourage continuous developer upskilling to maintain critical thinking and problem-solving skills. Moreover, without proper training, staff may unintentionally overshare data or use unvetted outputs.

Conclusion

AI workflows are now an important part of development processes for organizations. They offer speed and increased efficiency, which help teams improve their productivity. At the same time, AI introduces new technical errors and ethical challenges.

To use these models responsibly, human supervision, continuous testing, and clear ethical guidelines are vital. That way, teams can leverage their strengths while utilizing human judgment at every step for quality control.

Read this Techreviewer survey, which covers this topic even more extensively: https://techreviewer.co/blog/how-ai-reshaping-development-workflows-in-2025. It reveals deeper insights into how dev teams use AI, the issues they face, and how ethics is redefining AI for programmers.

The Most Important KPIs for Software Development

Techreviewer.co — Thu, 19 May 2022 08:50:08 +0000

Setting clear and concise goals is critical for a development team to reach project objectives on schedule. In fact, a Harvard study found that 3% of its graduates from the MBA program earned more than 10 times more than the remaining 97% of the class when they set clearly defined goals for themselves.

When considering this application within the software development process, software metrics can be utilized to assess the development team’s efficiency and how they can improve. These software development metrics are beneficial when working with a remote group of software developers or outsourcing.

Software metrics are a critical way to allow planning for which areas resources should be allocated to. The acronym KPI stands for key performance indicator.

Appropriate key performance indicators help a software development team stay on top of their goals and decrease the amount of needed interference.

Software development is a complicated process. Using specific KPIs in software development makes the procedure more manageable and allows the development team to stay on task and within budget.

The Importance of Software Development Metrics

Key performance indicators are an essential part of work-life in general, especially when it pertains to software development teams. Those managing the development process can use KPIs for software development in order to prioritize tasks accordingly.

Managers can do this by easily identifying issues, keeping track of all progress, and communicating with their team as needed during software development with the help of key performance indicators. These software metrics are also a good way to stay on top of areas that could use improvement. It also allows the software developers to constantly refine their results and productivity.

Clients and stakeholders of different software development companies can use software development KPIs to also monitor the progress of the project. They are able to assist with workflow management while also planning for any future projects that may occur.

Suitable KPIs for software can also aid in decreasing costs during the development process. By staying on top of the set goals, software development teams are able to minimize the amount of extra edits that are needed with little to no extra management.

Software development KPIs allow for an increased investment return because they improve performance throughout the development process. When KPIs are used properly, they allow a team to finish in a timely and budget-friendly manner.

Understanding Software Development KPIs

Key performance indicators used in software development quantify overall business performance during all stages of the development process. Specifically, KPIs for software development allow for the work done during development to align with the objectives of software development companies and other businesses using the software as a whole.

To get the best results, proper software metrics need to be chosen according to the team’s preferred model. Using suitable metrics will allow for the effective measurement of the project's success.

It is rather typical to see metrics such as lines of code or the number of deploys being used, but these are not great choices. They are not clear and do not set tangible goals for the team. A software development KPI needs to be more distinct.

Since software development is a complex process, the business manager can often fall short when creating a plan to meet deadlines and stay on track. However, key performance indicators can help avoid this.

Modern development teams tend to prefer Agile software development because it is quick and established. Agile focuses on rapid application development to provide steady delivery, results, and deployment.

When considering performance measures, business growth is a key measure that any software development KPI should lean on. There are four major groups of KPIs for software teams focused on what the performance indicator is measuring. These include defects metrics, software performance metrics, usability and user experience metrics, and developer productivity.

The Best Defects Metrics

These are the KPIs for software that will help find and get rid of as many issues and bugs within the software as possible before release. Managing software malfunctions can be an extremely frustrating and stressful part of the software development process. Utilizing appropriate metrics can help.

Actual Security Incidents

This KPI for software development provides a look at any possible security threats. This can include attempts at unauthorized access, disclosure, use modifications, destruction of information, or other security problems.

If left unchecked, these issues can allow for theft, denial of service, or compromised user accounts. This metric is essential to ensure that the software being developed is secure.

Code Defect Detection Percentage

Code defect detection percentage is another great way to test the efficiency of the development team.

To calculate this percentage, the relationship between the number of faults found before the software’s release and the total number of defects, including those discovered after the product's release, needs to be examined. Ideally, a majority of the bugs would be found and fixed before the software is released.

Mean Time to Detect Defects and Mean Time to Repair

The mean time to detect defects metric can be used to figure out how long it takes developers to discover a malfunction within the software on average. It is calculated from the time the issue occurs to the time the developer notices the problem. Reducing the MTDD value will produce the best outcomes.

The time between the discovery of the problem and the working fix for it is the average time to repair. The fix must be functional and employed for the stop time to be considered. Again, the lower the MTR value is, the higher efficiency will be.

Vulnerability

This software metric will help discover any potential weakness in the product during software development. The software might be susceptible to users gaining access past the boundaries established through their privileges, unauthorized actions, or more.

As vulnerability increases, so do security concerns for the software being developed. This metric is very important when considering how secure the finished product will be.

The Top Software Performance Metrics

During the quality assurance process, software performance metrics can evaluate and rank any problems within the software.

These KPIs for software development are helpful tools for managing the project, debugging the software, measuring performance levels, and estimating cost.

Availability

This specific KPI for software development focuses on the likelihood that the software will function when it needs to. The availability is a percentage of the total time that the software is operating.

A high-end availability means that the software can function even through malfunctions and defects. This is not to say they will not happen, but rather that if defects do occur, the software can separate the issue and continue working at a decreased capacity.

Reliability

This metric calculates the ability of the software to produce the expected results at any set time. The software can accomplish this through factors that avoid, find, and fix defects within it.

Developers can ensure that the product will not constantly deliver errors by appropriately utilizing this KPI. At the very least, the software should be able to find and fix any problems or isolate the issue and relay the error to the development team.

Serviceability

Serviceability is a KPI in software development that allows for the calculation of how easy it is to fix the software system when any given problem arises. There is an inverse relationship between serviceability and availability. If one decreases, the other will inevitably increase.

The software metric focuses on providing accurate repair options for each faulty situation while causing as little disruption as possible to the normal services.

Throughput

Throughput measures the amount of features, tasks, issues, and other possible activities that are finished within a certain period. With this metric, project managers can easily oversee how the development team allocates their time and what their workload is like.

It is similar to the software metric of velocity, which will be covered in the developer productivity metrics, but throughput is more technical.

The Ideal Usability and User Experience Metrics

KPIs for software do not stop once the software development is finished. They continue into the release of the product to record user interactions with the software.

These software metrics are heavily subjective since they depend on customer input and feedback.

Customer Effort Score

The customer effort score, or CES, is a scale ranging from 1 to 7. It calculates how easy the software makes it to interact with the business it was created for.

The calculation includes customer transactions, reporting an issue to the support team, and other methods of connecting with the business through the software. It is a powerful tool to know exactly how consumers feel about the product’s usability.

Customer Satisfaction Score

The customer satisfaction score, or CSAT, uses a scale from 1 to 5. With this software development KPI, one can measure how satisfied a consumer is with the product. The rating is based on the customer’s experience with the software.

The CSAT must be sent out to customers that purchased or are using the software. This is another invaluable tool when looking at how the software ranks amongst consumers.

Net Promoter Score

The net promoter score, or NPS, value ranges between -100 and 100. This metric finds out how likely a customer is to recommend the software to friends, family, and other people they may interact with.

The NPS can be used to consider consumer loyalty and long-term customer satisfaction in regard to the business’s software.

The Best Developer Productivity Metrics

During the process of software development, teams can use these KPIs to focus on their efficiency and work rate. Developer productivity metrics allow project managers to assess how much time and work is needed for a specific software project.

Code Churn

This software development KPI calculates the frequency of code changes. While code changes will happen, a frequent need for changes in code can cause problems.

If software needs to be revised each time a new feature is added to it, this will lead to a product that requires a high level of maintenance and is generally high-risk.

Code Coverage

Using the code coverage metric promotes constant delivery and development that is centered around proper testing. It calculates how much the source code achieves during testing procedures.

The higher the value of this metric is, the better the outcome. However, it will rarely ever be 100 percent.

Code Simplicity

There are several different ways to calculate code simplicity. A project manager could use cyclomatic complexity to view the number of separate paths the code must go through. The fewer paths needed, the better.

A more straightforward code is less complicated to support and test.

Code Stability

Code stability is a KPI for software development that looks at the possible impact of small adjustments on the product and if those adjustments could harm the objectives of the project or the software overall.

Generally speaking, minuscule changes to the code should not influence the entire software program.

Cumulative Flow

Visual diagrams are immensely helpful for providing an easily understandable view of the project’s overall productivity. The cumulative flow metric provides a visual representation of where tasks are at during software development.

There are many different task stages that can be represented in the diagram, including backlogged tasks, tasks that are currently being worked on, and tasks that have been finished and approved. Each stage is typically represented by a different color.

This flow chart provides an overview of the workflow throughout the software development process. It greatly helps balance the workload and prevent negative occurrences, such as task congestion.

Cycle Time

The cycle time is a KPI for software development that allows management to view the time spent on specific tasks.

By doing this, they can accurately predict how much time the team will require for future tasks and even future projects.

Flow Efficiency

This metric shows the relationship between active work time and the total time spent on a task. Although an assignment may be labeled as a work in progress, things may be at a standstill.

Typically this is due to periods where the software developers cannot proceed from one task to the next and must therefore wait until a separate task is completed. You can compute the flow efficiency by dividing active work time by the total time of the cycle.

Low efficiency in specific time frames can allow project managers to deduce inconsistencies within the workflow. Oftentimes an adjustment in the way the project or workload is being managed can provide a better workflow.

Release Burnout

Release burnout is a very important metric used during software development, as it allows management and the development team to accurately oversee the software’s release.

The development team can keep track of the project’s schedule, viewing whether it is ahead of schedule, on schedule, or falling behind. Using the release burnout metric, a business can provide accurate software release updates for consumers.

Velocity

Velocity is a KPI in software development that calculates the amount of work the development team can finish in a single execution. A higher velocity value during software development means that the team is functioning well.

It can be used to predict the level of productivity. Velocity can be calculated by things such as the number of tasks finished and ideal days. A common way to calculate velocity is by using story points.

Three or more iterations during software development will be needed before the average velocity for the team can be accurately measured. Velocity is a team metric and does not calculate individual work rates.

When calculating the amount of work that is finished in a single execution to use for measuring velocity, a software metric known as sprint burnout is used. Sprint burnout allows project managers to see the team’s work time in a single iteration. These are then used to properly compute velocity.

Conclusion

Using the proper software development KPIs is indispensable during the development process and an irreplaceable tool for the business the software is for. Appropriate software metrics allow team productivity, project timeframe, and budget to be measured correctly.

Project managers and software development companies can successfully use key performance indicators to evaluate and prioritize team goals and project objectives.

Each group of KPIs allows development teams, project managers, and clients to understand the overall status of the software development progression. Using them will help developers reach steady and maintainable goals while keeping on schedule and hopefully under budget.

This article was originally published on Techreviewer.co