Embarking on the journey ahead: QA trend playbook for 2026

Welcome to the now that, not long ago, felt like a distant possibility. Still doubt we live in the future? Just watch. 3D bioprinting is revolutionizing the world of personalized healthcare, allowing the first woman ever to receive a 3D-printed windpipe generated from living cells of another person. Meanwhile, Gartner states that a new wave of innovation, physical AI, is pushing intelligence out of the cloud and directly into the machines – robots, drones, industrial equipment, and connected infrastructure – redefining how physical operations function. Deloitte highlights that next-gen satellite internet is reshaping connectivity by embedding direct-to-device satellite links into everyday gadgets.

What do all these novelties have in common? Advanced tech, for sure. This means the road to adopting these innovations won’t always be straightforward. But it may be simplified with thorough quality assurance.

As we are stepping into 2026, the mandate for QA is to become the architectural compass of the enterprise, helping it to adapt quickly, innovate confidently, and turn technology complexity into a competitive advantage.

1. Switch the focus by 180 degrees

Shift-left testing has long been an indispensable part of projects dedicated to developing software within multiple industries. But what about its opposite? It turns out production testing is gaining momentum right now as well. Although it still looks like uncharted territory, 38% of organizations that participated in the latest World Quality Report (WQR) 2025-26 have already started trailblazing it by conducting pilots to explore its possibilities.

By relying on AI for analyzing operational data and leveraging mobile analytics, A/B verifications, canary releases, and other valuable techniques of production testing, organizations of various scales get access to production telemetry and analyze it. Why is it so important? It gives teams insight into user behavior, helps spot issues that never appear in testing, continuously validate system performance, and better understand long-term trends.

Thus, shift-right testing is gradually becoming a strategic priority for 2026, reflecting a broader move toward production-centric quality practices that blend real-time insight with continuous improvement.

2. Make Gen AI the next chapter in ERP reliability

ERPs have become the central nervous system of modern organizations, integrating data, workflow, and decision logic across every function. Without them today, it’s hardly possible to maintain operational consistency or coordinate processes at the speed and scale that modern businesses demand.

And as these systems grow in complexity and criticality, the industry is turning to Gen AI to keep pace, not just in operations or analytics, but increasingly in how ERP solutions are tested and validated. For instance, 44% of the WQR respondents already execute pilots to delve into Gen AI’s QA potential. Meanwhile, the interviewees mention that with its help they have achieved a significant reduction in testing cycles (39%) and provided their software engineers with enhanced possibilities to conduct more QA tasks alongside coding.

AI-enabled ERP testing may play a vital role in creating adaptive enterprise systems. By 2026, organizations that strengthen their QA capabilities with AI may build the process where AI anticipates quality risks rather than relying on reactive firefighting that acts only once disruptions occur.

3. Reinforce automated efforts through next-gen approaches

Test automation is entering a new phase, evolving beyond scattered efforts toward unified, enterprise-level approaches where AI plays a central shaping role. To be precise, almost one-third of the WQR respondents stated they are actively switching to an enterprise-wide test automation strategy, while 10% of the survey participants have already leveraged Gen AI to generate up to 75% of all their scripts.

Source: World Quality Report 2025-26 

The increasing role of AI in test automation is quite understandable, as it helps organizations considerably cut QA cycles, decrease operational expenses, and attain more confidence in the overall quality of developed solutions. For instance, by relying on AI-driven test automation, an online social game developer reduced automated test development time by 28%, saving 788 hours over a nine-month period.

However, despite high efficiency, this approach still requires careful governance to ensure that AI-driven outcomes remain accurate and aligned with business priorities, preventing the introduction of hidden risks or falling out of sync with evolving operational realities.

Companies that follow this well-thought-out strategy in 2026 will be able to transform their test automation efforts into a more resilient, adaptive, and scalable process that keeps pace with growing product complexity.

4. Maintain sharp security strategies to lower potential liabilities

Cybersecurity still remains a huge challenge. Just take a look. Recently, a SharePoint on-premises vulnerability allowed intruders to pose as legitimate users, steal authentication data, and navigate through connected systems, which affected 100 businesses globally. Or another example. A cyberattack using ransomware hit M&S, halting their eCommerce activity and causing considerable economic impact and brand fallout.

What can organizations undertake to prevent operational disruption, reputational damage, and legal consequences? Consider the following suggestions:

• Create a culture of strong governance. Forrester suggests that as AI becomes a deeper part of software development, security and risk teams will need to match that pace of innovation with stronger and more consistent oversight. They also emphasize shifting from short-term reactions to a resilience-driven security strategy that prepares organizations to withstand and recover from emerging threats.
• Craft an enterprise-driven DevSecOps culture. When developers, QA engineers, and security specialists jointly own risk mitigation, vulnerabilities are addressed proactively rather than left to the end of the release cycle.
• Build specialized security testing CoE. This unified hub for best practices, tooling standards, and advanced expertise will help ensure that security is consistently applied across all company projects.
• Introduce the principle of security-first onboarding. When replenishing your team with new members, focus on secure coding, safe tool usage, credential handling, and privacy basics, establishing expectations from day one.
• Invest in continuous security upskilling for all staff. This ongoing development helps employees understand the impact of their daily decisions on overall safety and equips them to actively maintain a secure, risk-aware environment.

5. Uplift quality through a perfect duo of Agile and QA

The upward trend of Agile practices shows no signs of slowing, with 74% of respondents from the latest State of Agile Report confirming their current workflows are governed by hybrid, blended, or “homegrown” Agile models. This continued strong preference stems directly from Agile’s proven ability to boost cross-functional interaction, tightly link development efforts to strategic company objectives, optimize execution speed, and guarantee a consistently high standard of software quality.

However, a new tendency can be clearly seen: the WQR mentions that Gen AI is the number one competency most companies will demand in the future to ensure success of their Agile-driven projects, as it helps keep high development and testing pace.

Even though, over the next two years, 33% of respondents plan to embed up to half of their quality engineers into Agile teams; they still face significant process constraints. These mostly center on a lack of automation, gaps in skills, and a limited understanding of the importance of quality engineering.

To tackle them head-on and ensure QA remains a driving force, organizations may place stronger emphasis on domain knowledge, foster interdepartmental interaction, craft the culture of constant learning, prioritize AI utilization, and most importantly make sure that all project stakeholders recognize QA as a cornerstone of success.

Armed with this blueprint, companies in 2026 can elevate QA into an adaptive, intelligence-driven discipline that accelerates value across every Agile iteration, pushing teams toward faster and smarter outcomes.

6. Attain a new equilibrium of power by supplementing data with AI

Test data is like the foundation of a house: if it’s weak or incomplete, everything built on top becomes unstable. Solid test data keeps the entire QA effort standing. With 95% of the WQR respondents leveraging Gen AI to some extent to pour that foundation faster and more precisely, QA can stand on more reliable ground.

Additionally, more than one-third of all interviewees rely on synthetic techniques for over a quarter of their test data. These smart approaches allow organizations to make QA outcomes more precise, in line with regulations, limit dependence on live system data, and serve as a safe alternative to production information.

Thus, in the upcoming year, organizations that embrace Gen AI-driven and synthetic data as a core part of their test data management strategy will transform test data from a routine task into a strategic asset that drives both quality and compliance.

7. Integrate Gen AI into your testing strategy for maximized efficiency

Think of QA like astronomers scanning the night sky. Gen AI acts as a powerful telescope, bringing distant, barely visible stars into focus. It uncovers details and patterns that would otherwise remain hidden, offering teams new ways to explore and understand the landscape of their systems.

To be precise, the WQR respondents stated that they rely on its support mainly when creating new test cases, working with requirements, analyzing issues, generating test data, automating visual testing, or crafting effective QA strategies.

Although the process may be challenging because of privacy risks, integration hurdles for AI-powered tools, accuracy uncertainties, barriers to leveraging large LLMs, and other problems, still in the years to come companies may introduce AI agents to curtail QA cycles. Of course, their usage should be supported by people’s oversight to ensure decisions remain accurate, context-aware, and aligned with business objectives.

“The measure of intelligence is the ability to change”

As industries transform at unprecedented speed, adaptability has become essential for QA specialists. Teams that align with modern trends and adjust their strategies in real time will not only maintain high-quality standards but also leverage testing as a catalyst for business growth.

Contact a1qa to ensure impeccable operation of your software solutions.