How a Leading EdTech Platform Achieved 60% Faster Test Authoring with AI-Native Test Automation

About the Company

A global EdTech company with over 20,000 test cases manages one of the industry's most complex QA operations. Their platform serves millions of users across functional, SAP, and API testing workflows, each requiring rigorous quality assurance before every release.

For nearly two years, their QA teams struggled with a testing bottleneck that every enterprise recognizes: manual test creation was slower than development velocity. Their 5 QA teams across 3 business units were buried in regression cycles, defect triage consumed entire sprints, and test coverage gaps meant production defects were slipping through despite best efforts.

After piloting multiple AI testing solutions and fixing 130+ integration issues across their existing toolchain, they found a path forward. This is the story of how they transformed their entire QA operation by giving their team AI-native tools that actually worked with their existing workflows.

When Traditional Test Automation Stopped Scaling at Enterprise Level

These problems aren’t unique to this company. Talk to any enterprise QA team managing thousands of test cases, and you'll hear the same three pain points. Here's what they were dealing with:

1. Manual Test Authoring Created a Development Bottleneck

With 20,000+ existing test cases spread across functional, SAP, and API testing, creating new tests for feature releases took weeks instead of days.

QA engineers spent 60% of their time writing and maintaining test scripts rather than actually testing. Every new feature meant choosing between comprehensive coverage and shipping on schedule.

2. Defect Triage Consumed More Time Than Testing

When tests failed, engineers faced hours of investigation to determine whether they'd found a real bug, encountered environmental issues, or hit a flaky test.

Defect triage meetings stretched across multiple days, and by the time root causes were identified, developers had already context-switched to new features. The team estimated 65% of their QA cycles involved triage work rather than productive testing.

3. Fragmented Tools Killed QA Productivity

Their testing stack included Jira for tracking, TestRail for test management, Jenkins for CI/CD, Selenium for automation, and Confluence for documentation.

Each tool required separate logins, data lived in silos, and no single system provided visibility across the full QA lifecycle. QA productivity had plateaued because they spent half their time context-switching between systems rather than improving test quality.

Why The Company Looked For An AI-Native Test Automation

The company initially explored traditional automation frameworks, but after two years of evaluation, they realized that bolting automation onto manual processes doesn't solve the core problem. They needed a platform that could generate tests intelligently, integrate with their existing tools seamlessly, and scale across multiple business units without requiring massive re-training.

They landed on LambdaTest's KaneAI after piloting multiple vendors because it solved their specific problems: test generation from requirements, automated defect analysis, and unified visibility across their fragmented toolchain.

The pilot spanned three business units, and the team raised 130+ issues during evaluation and implementation. LambdaTest's engineering team fixed them all. That level of responsiveness during a pilot told them everything the company needed to know about what production support would look like.

How Testing Workflows Changed After LambdaTest

After the company deployed KaneAI across all three business units, they saw a significant improvement across all the QA metrics.

60% Faster Test Authoring Through Intelligent Automation

KaneAI became their core GenAI engine.

The company could generate test cases directly from Jira requirements and documentation. Instead of writing test scripts line-by-line, QA engineers reviewed and refined AI-generated tests. This workflow change cut test authoring time by more than half.

The platform also introduced intelligent test generation that understood context: user flows, edge cases, API dependencies, and SAP integration points. When requirements changed, KaneAI updated the tests automatically to achieve the set goals rather than waiting for the QA team to create new scripts.

This setup helped the company reduce its test authoring time by 60%, and regression cycle duration improved by 30%. QA engineers moved from script maintenance to exploratory testing, the high-value work that catches the bugs automation misses.

65% Faster Defect Triage with AI-Native Root Cause Analysis

KaneAI automated the painful part of defect triage, which was figuring out why a test failed. When failures occurred, KaneAI analyzed logs, screenshots, network traffic, and environment variables, then provided real-time root cause analysis with actionable recommendations.

Features built specifically for this customer's needs made the difference:

• Network assertion capabilities flagged API timeout issues before QA wasted time debugging application code
• API testing integration differentiates between UI failures and backend problems immediately
• Database validation caught data inconsistency issues that previously required manual SQL investigation

KaneAI's real-time root cause analysis immediately categorizes failures (environmental issues, flaky tests, or genuine bugs), allowing teams to prioritize real issues instantly. Instead of spending days in triage meetings trying to determine which failures mattered, engineers could focus on fixing actual defects.

The 65% improvement in defect triage time meant faster feedback loops for developers and fewer "wait, which bug were we discussing?" moments in sprint planning.

45% Higher QA Productivity by Unifying the Testing Ecosystem

The platform connected their existing tools rather than replacing them. KaneAI pulled requirements from Jira, triggered builds through Jenkins, executed tests across multiple frameworks, including Selenium, and synced results back to Jira for bug tracking.

While their team still used TestRail for test management and Confluence for documentation, KaneAI provided a unified interface for test authoring and execution.

This unified approach delivered compound benefits:

• Test coverage increased 90% because creating tests became faster than skipping them
• Escaped defects dropped 20% as broader coverage caught edge cases earlier
• 1000+ augmentations for test data meant realistic test scenarios without manual data creation

QA productivity jumped 45% because engineers stopped wasting time on tool friction. They spent their days testing instead of administering their testing infrastructure.

Measurable ROI Across the QA Lifecycle

After full implementation across three business units:

• Test authoring became 60% faster, turning weeks-long test creation cycles into days.
• Defect triage improved by 65%, reducing hours of investigation to minutes through AI-native analysis.
• QA productivity increased 45% as engineers spent their time testing instead of managing tools.
• Regression cycles ran 30% faster through automated execution and intelligent test selection.
• Test coverage speed improved 90%, making comprehensive testing achievable within sprint timelines.
• Escaped defects dropped 20% as broader coverage caught issues before they reached production.

This EdTech company solved a problem that every enterprise QA team knows. Traditional automation doesn't scale when you're managing 20,000+ test cases across multiple business units.

If you, too, are facing similar QA bottlenecks, book a demo with LambdaTest to see how AI-native test automation works with your existing workflow.