Over the past year, AI has dominated conversations across the software industry. Predictions of fully autonomous QA, instant test generation and self-healing automation filled headlines, while fears around job displacement added fuel to the hype. As we move further into 2025, that excitement has begun to settle. Organisations are evaluating AI more realistically and with clearer expectations.
The bubble has not burst, but it has levelled out. This shift has created space for informed thinking about where AI genuinely improves testing and where it simply cannot replace reliable, repeatable frameworks.
At T-Plan, with more than 25 years of experience in visual UI test automation, we have spent the past 12 months working closely with teams who have adopted or experimented with AI tools. What we have seen is encouraging, practical and more grounded than the predictions of a year ago.
AI Has Delivered Clear Benefits, Particularly in Code Analysis
There is no doubt that AI is already improving software quality. It is proving especially helpful in areas such as:
- Faster detection of faults in existing code through automated analysis
- Quicker understanding of complex codebases
- Improved documentation and draft test ideas during planning
- Predictive insights that support early root cause investigation
For many testers, AI has become a helpful assistant that accelerates the investigative side of quality assurance. It enhances productivity without defining the testing process itself.
These gains are genuine and they are here to stay.
Where AI Falls Short: Repeatable, Reliable Testing
Despite major progress, AI still struggles in the areas that matter most to structured automation. The core principles of testing have not changed and AI cannot yet meet them in a dependable way.
1. Unpredictable outputs
AI models do not always provide identical results for identical inputs. In code generation this may be acceptable, but in testing it introduces unacceptable risk.
2. Fragile or incorrect test steps
AI-generated tests often appear convincing but can be incorrect, inconsistent or difficult to maintain. This is especially true for systems with complex or highly visual user interfaces.
3. Limited transparency
Many AI tools cannot clearly explain why they made a decision. In regulated sectors such as defence, healthcare, finance and automotive, transparency is essential.
4. Visual automation remains a specialist field
AI-driven image recognition has improved, but it is still not able to deliver the pixel accuracy and cross-platform consistency required for CAD environments, medical displays, automotive dashboards, defence-grade systems or any GUI-rich application.
Repeatability is essential for automation and AI is not yet designed with this requirement at its core.
A Clear Shift in Opinion Through 2025
A year ago, some organisations believed AI might replace large parts of their testing strategy. The reality has proven more balanced.
Teams increasingly view AI as an augmentation tool rather than a replacement. They use it confidently for:
- Test idea generation
- Early analysis
- Documentation support
- Investigating defects
They avoid relying on it for:
- Reproducible regression testing
- Cross-platform validation
- Safety-critical or compliance-driven tests
- Visual, UI-intensive workflows
- Long-term test script maintenance
This shift reflects maturity, not scepticism. It shows a clearer understanding of how AI fits into a wider testing ecosystem.
Why Deterministic Tools Still Matter
AI excels at interpretation and pattern recognition. Testing relies on precision, consistency and full reproducibility. These requirements continue to highlight the need for deterministic automation solutions.
Tools like T-Plan Robot provide:
- Pixel-accurate validation across platforms and devices
- Stable test scripts that behave identically every time
- Low code and no code authoring for technical and non-technical teams
- Secure, compliant automation suitable for regulated environments
- Reliable automation for CAD, BIM, imaging tools, embedded systems, automotive IVI and more
AI cannot replace these capabilities. Instead, it complements them.
Looking Ahead: AI Will Support Testing, Not Redefine It
AI will continue to influence testing, particularly in planning, prioritisation and analytics. However, the foundations of software testing remain the same. Organisations still require:
- Repeatability
- Accuracy
- Deterministic execution
- Cross-platform reliability
The past 12 months have shown that while AI can make testers more effective, it cannot provide the repeatable automation needed for quality assurance on its own.
A balanced approach is now emerging:
AI for intelligence. Deterministic tools for reliability.
It is not the dramatic shift some predicted. It is a practical, sustainable evolution.
Final Thoughts
AI has become an important part of the tester’s toolkit, but it has not replaced the need for robust automation frameworks. It enhances analysis, speeds up planning and provides valuable insights, yet it cannot deliver the repeatable, secure and stable automation required for critical applications.
As organisations reassess their testing strategies in 2025, many are choosing a combined approach that delivers the best of both worlds. AI provides speed and intelligence. T-Plan provides reliability and cross-platform certainty.
If you are exploring how AI fits into your testing strategy or need a proven solution for dependable visual UI automation, our team is here to help.
Contact us to see how T-Plan can strengthen your testing with secure and consistent cross-platform automation.
