Evaluating the Practical Value of SOLID’s AI-Powered Test Automation
Early findings from a deliberately limited trial show how Test Automation reduced manual smoke testing effort – and why that was enough for Stage Zero to move forward.
Stage Zero, part of the Vivica Group, operates a complex, fast-moving software environment with regular production releases and a high degree of custom functionality. As with many enterprise platforms, ensuring release stability requires consistent, reliable QA processes – particularly around smoke testing prior to deployment.
As is common at this scale, Stage Zero’s QA processes relied heavily on manual smoke testing as part of their weekly release cycle, performed across a predefined set of critical workflows. While effective, this approach was time-intensive, repetitive and constrained by available QA capacity.
As part of SOLIDitech’s broader rollout of SOLID AI-Powered Test Automation, Stage Zero decided to participate in a small, deliberately limited-scope trial.
The goal was not to transform their QA process overnight, but to answer a much simpler question:
Is there enough practical value here to justify further investment?
To see for themselves whether or not there was sufficient value in the Test Automation framework, our Stage Zero team set up a trial:
What the trial included:
- Smoke testing only
- A single product
- A direct comparison of manual smoke testing vs test automation
- A short evaluation window
What the trial did not include:
- Full regression testing
- Multiple products or workflows
- Long-term trend analysis
- Production defect rate comparisons
What was measured:
- Time spent on smoke testing before automation
- Time spent on smoke testing after introducing SOLID AI-Powered Test Automation
- The operational effort required to run and review tests
The intent was not to optimise aggressively, but to observe realistic, early-stage behaviour in a real production workflow.
Manual smoke testing is a necessary safeguard, but it comes with trade-offs:
- It consumes a disproportionate amount of senior QA time
- It limits test coverage to what can reasonably be executed within tight release windows
- It leaves little room for deeper exploratory or feature-level testing
For Stage Zero, weekly smoke testing alone accounted for a significant number of QA hours – time that would ideally be spent on higher-value work.
The trial focused on whether AI-assisted automation could reduce this manual overhead – without introducing instability, excessive maintenance effort or additional overhead for the QA team.
Manual smoke testing time before automation
~ 23.25 hours per week
Smoke testing time after introducing AI-assisted automation
~14 hours per week
This represents a saving of:
~9 hours per week
~36 hours per month
It is worth reiterating that these savings only apply to smoke testing within the defined trial scope. No claims are made about broader regression coverage or long-term exponential gains at this stage.
On its own, a ~9-hour weekly saving may not sound transformational. In context, however, it answered several critical questions for Stage Zero:
- Usable? YES. The tooling was practical and usable within a complex environment
- Stable? YES. Automated tests proved stable enough to support real release workflows
- Scalable? YES. Maintenance overhead was manageable
- Valuable? YES. QA time could be meaningfully reallocated without compromising release confidence
This trial showed that the real value of AI-powered test automation is not simply doing the same work faster, but freeing experienced QA capacity to focus on work that still requires human judgment – including feature validation, edge cases and investigative testing.
In practice, the automation acted as a force multiplier for the QA team, supporting their work rather than replacing it.
Based on these early findings, Stage Zero concluded that there was sufficient value to proceed with the next phase – opting into additional SOLID AI-Powered Test Automation licensing to extend automated testing beyond this initial trial.
This case study provides concrete evidence of where AI-powered test automation delivers value in an enterprise environment.
Even within a deliberately limited scope, the results showed a clear reduction in manual smoke testing effort and a more effective use of skilled QA time. Rather than accelerating the same work, automation shifted effort away from repetitive tasks and toward testing activities that require human judgment.
For enterprise customers with custom workflows, frequent releases, and dedicated QA teams, this directly reflects the value proposition of SOLID AI-Powered Test Automation: improving testing efficiency while strengthening – not replacing – QA capability.