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Modern IT teams face growing pressure to release faster without sacrificing quality. QA has become a continuous, integrated part of delivery, but many testing tasks remain manual and brittle. GenAI marks a shift, enabling smarter, scalable testing that supports, rather than replaces, human insight. AI-powered tools now help teams navigate complexity, reduce repetitivework, and focus on higher-value activities, all while reserving essential human judgment.
AI in QA is most effective when applied to high-friction areas like repetitive tasks and complex analysis. Seamlessly integrated into existing workflows, it supports planning through execution without disrupting team processes. The next sections highlight how AI does it.
Test planning is often slowed by the need to align diverse, unstructured inputs, like user stories, business rules, and compliance requirements, into clear, risk-based testing objectives. These inputs typically lack consistent formatting, requiring manual interpretation. AI helps streamline this process by analyzing natural language to extract logic, intent, and risk-relevant details, enabling teams to quickly shape test strategies aligned with business goals. In regulated industries like finance and healthcare, it also enhances traceability and ensures critical requirements are not missed.
Traditionally, test automation demanded technical skills like scripting and tool expertise, limiting who could contribute and creating bottlenecks. AI-enabled authoring removes these barriers by allowing team members to describe test scenarios in natural language, which AI translates into executable logic. Visual AI tools can further enhance this by recognizing interface elements across web, mobile, and enterprise apps like SAP. This code-agnostic approach boosts test resilience and broadens participation in automation, regardless of technical background.
Even minor UI changes, like renamed buttons or repositioned fields, can break automated tests, leading to wasted effort and reduced trust in automation. AI-enhanced execution addresses this with visual recognition and self-healing capabilities that detect changes and adjust test paths automatically, minimizing false positives and preventing failures. This results in greater stability and lower maintenance, especially in fast-paced CI/CD environments.
Test execution produces large volumes of output that can be difficult to interpret, with root cause analysis requiring time, expertise, and context, often in short supply. AI streamlines this by providing intelligent summaries, fault detection, and next step recommendations, offering teams clear, prioritized insights that speed up defect resolution. It can also generate structured queries (e.g., in TQL) to evaluate test suite health, uncover redundancies, and identify coverage gaps, driving continuous improvement in both quality and collaboration.
Quality engineering encompasses a broad set of responsibilities, from analyzing requirements and authoring test cases, to managing test data, debugging issues, and reporting outcomes. These tasks can be grouped into three key domains where AI consistently delivers measurable impact.
Before testing begins, teams often face inconsistent requirements, complex compliance demands, and undocumented business rules, leading to slow alignment and missed scenarios. AI addresses this by analyzing unstructured content, such as user stories, system behavior, and legacy documentation—to uncover relevant test conditions and high-risk areas. This enables a proactive discovery process that replaces guesswork with informed prioritization.In SAP environments, for instance, Tricentis LiveCompare applies AI-driven impact analysis to examine system usage, transport contents, and custom code. It then identifies affected objects, transactions, and interfaces, mapping these to existing test cases and exposing untested areas. This targeted insight enables teams to significantly reduce testing scope while enhancing risk coverage and accelerating delivery.
Once test plans are defined, teams must create and maintain test assets, often across diverse platforms and tools. Historically, this required technical proficiency, limiting who could contribute to automation.AI simplifies and democratizes this phase by allowing test creation from natural language requirements, capturing user workflows, and transforming them into executable logic. AI also facilitates intelligent data generation and management, reducing the upkeep burden of traditional scripting approaches.
Maintaining automation in dynamic environments, marked by frequent releases and UI changes, is a constant challenge. AI enhances test resilience through mechanisms such as targeted execution, self-healing logic, failure diagnostics, and real-time analytics.
For example, Tricentis Tosca includes AI-powered self-healing capabilities that detect when an automated test breaks due to a change in the application’s UI such as a renamed button or a moved field. Rather than failing the test outright, Tosca uses machine learning models to compare the current state of the application with previous executions, analyzing visual and structural cues to identify the most likely matching element. It then adjusts the test execution path automatically, allowing the test to proceed and flagging the update for review.
Tricentis takes a pragmatic, layered approach to integrating AI into software testing, designed to support teams of all sizes, technologies, and AI maturity levels. At the core is a three-pillar framework, Tricentis Agentic AI, Remote MCP Server, and AI Workflows, that works together to embed AI across the quality engineering lifecycle without disrupting existing processes or requiring major changes.
Agentic Test Automation operates as a context-aware assistant, integrated directly into the test lifecycle. It enables teams not only to plan and execute test scenarios but also to adjust dynamically as systems change. Rather than functioning as a detached utility, it serves as an intelligent collaborator, interpreting requirements, navigating interfaces, adapting to application updates, and contributing to real-time analysis.For example, when working within SAP, a user may issue a prompt such as, “Create purchase order, fill in vendor, org, group, save.” Agentic AI understands the business intent, identifies relevant UI elements, completes the necessary input fields, and composes a fully executable test case. This is structured according to Tosca’s modular framework and adheres to team standards. The entire process is completed within minutes, eliminating the need for scripting or manual UI modeling.
The Tricentis Remote MCP Server offers a flexible way to adopt AI by providing a universal interface for integrating external models, such as those from OpenAI, Anthropic, or proprietary LLMs into tools like Tosca, qTest, and NeoLoad. This hybrid, open architecture lets teams choose the AI that fits their needs while benefiting from Tricentis’ governance and structure. More than just a connector, MCP represents a shift toward agentic AI—systems that understand context, take initiative, and execute testing tasks end to end. For example, teams can use Claude to generate test cases from Jira, organize them in qTest, and run them in Tosca without writing code. With seamless, model-aware interactions, MCP helps teams move from isolated pilots to fully integrated AI strategies that accelerate delivery and improve software quality.More practical example Claude (Anthropic’s AI) is interacting with Tricentis Tosca via the MCP. Rather than learning Tosca’s interface or APIs, Claude uses natural language to prompt test creation. Through MCP, Tosca understands these prompts and autonomously generates structured test cases, complete with folders, modules, and valid test steps.
AI Workflows provide a natural language interface that lets users generate test cases, analyze results, and identify coverage gaps simply by stating their intent, no scripting or deep tool knowledge required. Serving as a coordination hub, they delegate tasks to agentic AI agents, aligning with user goals while preserving human oversight. Tricentis continues to develop AI Workflows as part of its broader mission to embed AI across the quality engineering lifecycle, helping reduce routine friction, speed up decision-making, and empower testers, developers, and product owners to contribute more directly and confidently to quality efforts.
Software teams today must release faster without compromising quality, and while traditional automation helps, it often struggles to keep pace with the speed and complexity of modern development. AI fills this gap by handling tasks that are too time-consuming or fragile to scale manually, but human expertise remains vital, testers still define goals, interpret results, and provide context. The Tricentis platform embraces this balance by embedding AI throughout the delivery lifecycle, offering guided assistance, autonomous agents, and seamless integration with existing workflows to enhance, not replace, human insight.
Published: 7 May 2026Authors: Kateryna Gandzeichuk Product Marketing Specialist Markku Riihonen Senior Product Marketing Manager Both work at Tricentis
This blog is a partner contribution to the “Amplified Quality Engineering” publication.
Amplified Quality Engineering
