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As organizations accelerate their digital transformation, testing faces growing pressure: systems are more complex, release cycles are shorter, and quality expectations remain high. Test automation has become essential, not just to run more tests faster, but to provide smart, sustainable quality at scale.Sogeti’s vision for Sustainable Test Automation responds to this challenge by going beyond traditional automation. Please note that the word sustainable in this context is used in the broad sense of being durable and having a long-term perspective, not in the narrow sense of Green IT. Sustainable Test Automation emphasizes building a practice that delivers lasting value, operates with a small footprint, and connects people and teams.
It’s not only about whether a test passes, but whether it supports smarter decisions, accelerates feedback, and empowers collaboration.At its core, this approach links three strategic goals: building confidence through meaningful automation, enhancing control by making automation sustainable, and ensuring long-term value through clear metrics and thoughtful execution.
With the advent of Generative AI, we now have the opportunity to amplify this vision. GenAI offers new capabilities to support each step of the automation lifecycle, generating tests, guiding decisions, and interpreting results, enabling a shift from manual orchestration to intelligent augmentation. This module explores how GenAI enhances sustainable test automation and how teams can leverage it to create feedback loops that are not just faster but also more resilient and future proof.
Sustainable test automation is rooted in asking the right questions. Not just “how do we automate?” but “what value does it create?” and “how can we make it last?” To guide this, Sogeti’s framework centers around six foundational questions, covering the what, how, when, and why of automation. With the advent of GenAI, we can now enhance how we answer each of these questions, making automation not just faster, but fundamentally more intelligent and more adaptive.
One of the first and most critical questions in any automation initiative is: What should we automate? GenAI now enables teams to analyze requirements, user stories, production logs, and historical anomalies to generate meaningful test scenarios. Instead of relying solely on human judgment or coverage maps, GenAI can surface test ideas that might otherwise be overlooked, especially edge cases or real-world usage patterns.
Scenario: A product team working on a mobile banking app uses GenAI to analyze user feedback and app store ratings. By identifying recurring themes, such as complaints about failed transactions or confusion during authentication, GenAI suggests relevant test scenarios targeting those pain points. It also identifies product improvements and usability enhancements, helping the team align testing efforts with actual user concerns.
Once you know what to automate, the next question is how. Traditionally, this has involved time-consuming scripting, framework setup, and maintenance. GenAI is transforming this process. Code assistants now generate boilerplate code, configure frameworks, and even implement page object models or step definitions based on natural language input.Even more transformative is GenAI’s contribution to self-healing automation. When a UI element changes or an API response format shifts, AI-enhanced tools can adjust the test logic or locators dynamically, without human intervention, significantly reducing test flakiness and maintenance cost.
Scenario: A retail website undergoes a UI redesign. While dozens of automated tests fail initially, the GenAI-enabled automation platform maps the new DOM structure and restores test execution autonomously within minutes.
Automation scripts must interact with the system under test, often through dynamic user interfaces or intricate API calls. GenAI improves this by bridging human and machine understanding. It can interpret human-readable test cases and map them to UI elements or API endpoints using semantic matching instead of complex locators, and pattern recognition instead of fixed elements in a response message.
Scenario: A test analyst writes, “Log in as an admin and verify the dashboard loads.” GenAI translates this into a full Selenium or Playwright script, automatically identifying the appropriate fields, buttons, and expected conditions.
Effective testing often requires carefully managed data, configurations, and preconditions. GenAI helps generate realistic, privacy-compliant test data and dynamically provision it into test environments. This removes bottlenecks and reduces the reliance on shared datasets or manual setup.
Scenario: GenAI generates policy records with plausible variations for an insurance claims system, allowing for high-coverage testing of edge cases without touching production data.
Another example is where an AI Agent searches test data in the database of a test environment, based on a prompt instead of complex SQL statements. Using these prompts saves time on setup and maintenance, through providing accurate data in a constantly changing data set.
Choosing which tests to run, and when, is critical for speed and relevance. GenAI offers risk-based test selection by correlating code changes, historical failures, and business priorities. It optimizes test execution by prioritizing the most likely test cases to detect new failures.
Scenario: After a change in the billing module, GenAI recommends executing only a subset of the regression suite, saving 60% of runtime while maintaining risk coverage.
When tests fail, the real question is: what went wrong? GenAI enhances the feedback loop by analyzing logs, system traces, and test results to suggest potential root causes. It can autosummarize issues, flag anomalies, and even propose remediation actions.
Scenario: A failed end-to-end test prompts a GenAI-generated report identifying a recent API timeout and linking it to a backend latency spike observed during the same timeframe.
These enhancements form the backbone of a new, augmented test loop that uses AI not to replace testers but to amplify their insight, speed, and impact. By embedding GenAI at each decision point, teams can create automation systems that adapt, learn, and continuously evolve with the software they support.
While GenAI enriches how we answer key automation questions, a broader shift is underway when we use GenAI to orchestrate all questions together. Using agentic systems redefine how verification, validation, and exploration are performed. These systems combine orchestration layers, browser automation, and AI-driven agents that not only execute tests but also learn,adapt, and contribute to continuous quality improvement.
Traditionally, testing revolves around three key activities:
In earlier automation strategies, verification was the easiest to automate since it dealt with known inputs and expected outputs. Validation required more human insight, and exploration relied almost entirely on tester intuition.Agentic systems now enhance each of these in transformative ways.Verification becomes continuous and traceable. Intelligent agents execute structured checks across UI and API layers, monitor behavior, and log outcomes in real time. These agents don’t just run tests; they evolve test logic based on patterns, reuse, and historical signals, aligning perfectly with GenAI-driven selfhealing and adaptive testing strategies.Validation becomes more grounded. Instead of relying solely on human walkthroughs, agentic systems can replay actual user flows, compare outcomes to expected behaviors, and highlight deviations. This bridges the gap between business intent and system behavior, especially valuable in ambiguous or evolving requirements.Exploration, long considered the most human-centered practice, is augmented, not replaced. Based on the input of an expert on what to explore, agents perform wide-ranging navigation, mutate inputs, and surface anomalies, which the expert can then interpret and refine. This collaborative model of hybrid intelligence scales exploration while preserving critical human insight. Don’t let agents randomly explore but be guided by identified risks and the experiences of an expert.
In an agentic environment, automation is not a one-off task; it’s a living system. Each agent execution contributes insights, which testers can analyze and convert into new test ideas, improved models, or updated heuristics. This creates a closed-loop learning system, where automation and human reasoning reinforce each other continuously.Agentic systems, enhanced by GenAI, support quality engineering that is risk-based, user-focused, and insight-driven. This modular approach forms a closed, adaptive loop:
This loop turns automation into a living, learning ecosystem. The result is amplified testing, where agents provide breadth and speed, and human testers bring depth and judgment.
Agentic systems also shift how we populate the testing pyramid. Tests can now be generated from multiple sources:
This enables organizations to operate across both shift-left and shift-right strategies, creating a testing ecosystem that learns, adapts, and amplifies human expertise.
In a GenAI-augmented testing landscape, traditional metrics like pass/fail rates or test counts are no longer sufficient to capture the real value of automation. Success must be measured not only by execution but also by impact on quality, speed, decision making, and team effectiveness. With GenAI embedded into the test lifecycle, new dimensions of measurement emerge. Test effectiveness becomes a central metric: Are we validating the right risks? Are we catching issues before they reach production? GenAI can help visualize risk coverage, recommend additional tests, and flag redundant ones, ensuring the test suite remains lean yet meaningful.Another key indicator is maintainability. GenAI can track how often tests are repaired, how often self-healing kicks in, and where repetitive manual intervention is still needed. These insights support ongoing optimization of both tooling and strategy.
On the human side, success includes team enablement. Are developers and testers using GenAI-generated insights to make better decisions? Are root causes identified faster? Are feedback cycles shrinking? By observing collaboration patterns and adoption rates, teams can gauge how GenAI contributes to a culture of quality.GenAI also supports reflective intelligence: it helps execute and explain how, where, and why value is created or wasted. This enables continuous improvement, aligning automation outcomes with strategic goals across economic, environmental, and social dimensions.Ultimately, success in this new environment is not about automation volume but about sustainable impact: executing meaningful tests, reducing waste, enabling people, and delivering insights that shape better products.
The future of quality engineering lies not in doing more of the same faster, but in doing it smarter. Test automation, while essential, is no longer just about coverage and speed; it’s about value, insight, and resilience. Generative AI is not here to replace human testers, but to amplify their capabilities and decision-making power.Where traditional automation is focused on execution, GenAI enhances the entire test lifecycle, from understanding what to automate to interpreting test results and even shaping test strategies. It acts as an intelligent co-pilot, helping teams adapt to change, reduce manual overhead, and surface meaningful patterns hidden in the complexity of modern systems.Sustainable Test Automation, as envisioned by Sogeti, is not a fixed target; it is an evolving practice that thrives on feedback, learning, and shared understanding. When paired with GenAI, it transforms into a dynamic system that validates software and learns from it, anticipates risks, and empowers people.Combining sustainability principles with AI-driven augmentation creates a foundation for long-term value. It’s not just about creating more tests. It’s about building smarter test ecosystems that continuously improve, support human creativity, and align with business goals.
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