Does AI actually make test automation faster, or does the cleanup afterward cancel out the gain? TTC Global ran a four-phase experiment across roughly 21 real Workday HR tests to find out. We measured AI-assisted authoring taking about 65% less time per test, with the full method disclosed so the number holds up to scrutiny.
What you'll learn
- How we estimated a defensible manual time baseline from a single measured test
- Which disciplines actually capture the AI efficiency gain
- here the time savings concentrate by test complexity
What the Four-Phase Experiment Found
- The efficiency gain nearly tripled since 2025: AI-assisted authoring now takes about 65% less time per test, up from roughly 25% in TTC Global's earlier GitHub Copilot study.
- On the one test measured by hand in both studies, time fell from 84 minutes to 24 minutes: a 71% reduction using Claude Opus 4.8, illustrating the scale of the shift.
- Not every model earns the gain: a free, low-tier model left 70 to 80% of complex work to be finished by hand, while frontier models cleared it with minimal rework.
- The pattern from 2025 has reversed: back then, AI helped most on simple tests and least on complex ones. Find out what changed and where the advantage now concentrates.
- Projected throughput reaches about 107 tests per engineer per month with AI, versus about 37 without, using the same delivery model for both so the comparison isolates the AI effect.
Frequently Asked Questions
This report is written for engineering leaders, QA managers, and test automation teams evaluating whether AI-assisted authoring is worth adopting at scale, along with anyone deciding how to combine AI tools with an existing test automation framework.
TTC Global's AI-First Methodology is a structured, five-phase agent approach to AI-assisted test automation, combining AI configuration, disciplined consistency, non-AI guardrails, and human governance.
Across a four-phase experiment on roughly 21 Workday HR tests, AI-assisted authoring took about 65% less time per test than manual work, up from roughly 25% measured in TTC Global's 2025 study. The gain reached 71% on the one test measured by hand in both studies.
Only one test was measured manually in this study. The rest of the manual baseline is a transparent estimate, anchored on that measured point and built outward by complexity band, with the method and the uncertainty shown in full rather than presented as a hard number.
Part 2 measures which AI models are actually fit to carry the AI-First workflow. Part 3 compares GitHub Copilot and Claude Code on cost. Get notified when the next report is released!