# Validating parity: GVB

The report focused on a high-stakes, real-world scenario: a first-time tourist attempting to purchase a 1-hour travel ticket using a Mastercard and requesting a receipt. To ensure a fair fight, both Uxia’s AI and the human panel used identical prototypes, missions, and audience demographics (UK-based, ages 25–45).

## SUMMARY

01.

Massive gains in speed and cost

02.

Superior insight detection

03.

Reliability and engagement depth

## Main findings

### Massive gains in speed and cost

Uxia significantly outpaced traditional methods in terms of time and budget:

- **30x Faster Delivery:** The full testing cycle (setup, execution, and analysis) took just **25 minutes** with Uxia, compared to **748 minutes** (over 12 hours) for the human panel.

- **Automated Analysis:** While researchers spent over 4.5 hours manually reviewing human recordings, Uxia’s analysis time was effectively **0 minutes** because it generates a ready-to-read report immediately.

### Superior insight detection

The AI testers proved to be far more observant than their human counterparts:

- **4.25x More Issues:** Uxia surfaced **17 real usability issues**, while humans only detected **4**.

- **Zero Unique Human Insights:** Every single issue flagged by the human panel had already been independently identified by the AI; the humans brought no unique findings to the table.

- **Critical "Blind Spots":** All 10 AI testers flagged a serious trust issue regarding an external Dutch-language payment redirect. In contrast, **not a single human tester** commented on it, likely because they were rushing to complete the task for compensation.

### Reliability and engagement depth

The quality of feedback revealed a stark "attentiveness gap" between the two groups:

- **7x More Commentary:** AI transcripts averaged **2,200 words** per session, compared to just **300 words** from humans.

- **100% Success Rate:** All AI tests were valid and usable, whereas the human panel suffered a **10% failure rate** due to a technical audio issue that made one transcript unusable.

- **Active vs. Passive Testing:** Human testers often clicked through screens in "automatic mode," spending only 3–5 seconds on onboarding slides. AI testers "thought out loud," questioning ambiguous labels and identifying multi-layered friction points.