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State of DevOps Report: AI in Testing Edition 2026
- Chapter 1: The Current State of Testing
- Chapter 2: The Role of AI in Testing
- Chapter 3: The Trade-Offs of AI-Powered Testing
- Chapter 4: Evolving Roles and Responsibilities
- Chapter 5: Governance and Compliance in Testing
- Chapter 6: Measuring the Business Value of Testing
- Chapter 7: Regional and Industry Variations
- Chapter 8: Recommendations for the Future of Testing
- Bottom Line
Executive Summary
Key Findings:
- Hybrid testing infrastructures are increasingly common, driving the need for consistent orchestration and reporting across diverse environments.
- AI adoption is growing, spanning test creation, execution optimization, and analysis.
- While respondents report operational benefits, challenges persist in cost attribution and governance maturity.
- Quality ownership is evolving: developers are taking on more test authoring, while QA teams focus on analytics, orchestration, and governance.
- Progress is being made in compliance automation and secure-by-default practices, but skills gaps and time constraints remain significant barriers.
- Measurement is shifting from execution metrics to business outcomes like retention, feature delivery speed, and revenue impact.
Recommendations for Leaders:
- Develop robust measurement frameworks and improve cost attribution.
- Standardize governance practices for AI usage and hybrid workflows.
- Define clear ownership and operating models for quality across all testing layers.
"The future of testing is here, and it’s dynamic, AI-driven, and outcome-focused. By embracing clear ownership, smarter governance, and meaningful metrics, we can transform testing from a hurdle into a launchpad for innovation.”
Research Methodology and Respondent Profile
This Report is part of the 2026 State of DevOps Report. Originally pioneered by Puppet, this decade-spanning report has drawn insights from over 40,000 technology practitioners and leaders since its inception, making it an unparalleled resource for understanding the evolution and impact of DevOps.
Methodology
This study was conducted through a 20-minute online survey with 820 global IT decision-makers (ITDMs), purchase influencers, and DevOps practitioners.
Respondent Breakdown by Region
Participants were sourced globally, with representation across key regions:
Research Objectives
The study aimed to explore and benchmark modern software delivery practices in the context of AI and automation. Key objectives included:
1. Understanding Software Delivery Pipelines
- How enterprises structure and execute delivery pipelines in the age of AI.
- Identifying changes or consistencies in these processes.
2. Assessing Maturity Levels
- Evaluating the maturity of DevOps, platform engineering, and secure-by-default practices.
3. Adoption of Emerging Approaches
- Measuring the adoption and impact of AI-assisted delivery, automation, and compliance-by-default strategies.
4. Benchmarking and Insights
- Comparing organizational maturity to identify gaps and differentiators between leaders and laggards.
- Providing actionable insights for both C-level executives and practitioners.
Limitations
Results reflect self-reported practices and perceptions.
Back to topIntroduction
Application testing in DevOps stands at a critical juncture. As we move further into 2026, the initial hype surrounding Artificial Intelligence (AI) has settled into a pragmatic reality. AI is no longer just a theoretical advantage; it is a fundamental component of modern delivery pipelines. However, this integration has brought forth a new set of challenges regarding cost, governance, and complexity.
This report benchmarks how enterprises are structuring continuous testing with AI, where adoption is yielding the strongest outcomes, and offers practical recommendations to help you optimize value—regardless of your tooling strategy.
What’s in the Report?
This report offers a concise guide to navigating modern software quality assurance:
- Who: Insights from QA leaders, engineers, DevOps experts, and executives across industries and regions.
- What: Covers hybrid testing, AI integration, cost challenges, team roles, compliance, business outcomes, and industry trends.
- Why: Provides data-driven strategies to optimize investments, improve operations, and deliver business value.
- How: Includes actionable recommendations to boost efficiency, drive innovation, and future-proof testing practices.
Key Takeaways
- AI Is Mainstream, but Costs Are Rising: AI-augmented testing boosts speed and quality but increases costs and governance challenges as complexity grows.
- Hybrid Infrastructures Lead: Organizations favor hybrid cloud and onpremises models for flexibility, control, and scalability.
- Evolving Roles: Developers are more involved in test creation, while QA teams focus on analytics, governance, and aligning with business goals.
- Compliance and Security: Automation and secure-by-default practices are advancing, but skill gaps and time constraints remain.
- Performance-Driven Strategy: Teams prioritize cost tracking, customer metrics, and data insights to maximize ROI.
- Actionable Guidance: Practical steps for optimizing infrastructure, building skills, standardizing governance, and measuring outcomes are included.
Our findings reveal a landscape defined by hybrid infrastructures, shifting responsibilities, and a critical need for better measurement frameworks.
Get a Look into the Future of Testing
From industry trends to innovations shaping performance, functional, and API testing.
Message from Perforce
The velocity of digital transformation has elevated Quality Assurance (QA) from a supporting role to a strategic cornerstone of software delivery. In 2026, QA teams are not merely testing for reliability—they are the architects of speed, security, and digital resilience.
"The era of AI-augmented testing is no longer a prediction; it is our operational reality. This report captures the pivotal moment where organizations move beyond the novelty of AI to mastering its implementation. At Perforce, we are dedicated to equipping this community with the intelligent, scalable solutions required to navigate this new landscape with precision and confidence."
The State of DevOps Report: AI in Testing Edition illuminates the critical junctures facing today’s quality leaders:
- AI Integration at Scale: As AI adoption matures, the focus shifts from experimentation to governance, cost management, and measurable ROI.
- Hybrid Infrastructure Dominance: With the majority of organizations operating across both cloud and on-premises environments, the ability to orchestrate testing seamlessly across these boundaries is paramount.
- Security Shift-Left: The integration of secure-by-default practices into the earliest stages of development has become non-negotiable for risk mitigation.
At Perforce, we view these shifts not as obstacles, but as opportunities for competitive differentiation.
Our integrated quality solutions, led by Perfecto and BlazeMeter, are engineered to empower teams to embrace these changes head-on. By combining AI-driven efficiency with enterprise-grade governance, we enable organizations to accelerate innovation without compromising quality or compliance. This report serves as your strategic blueprint for turning these industry trends into tangible business value.