TL;DR
• Code-centric automation continues to slow teams down as UI changes multiply, making stability and evidence hard to maintain.
• AI code generators don’t solve the problem because they still produce brittle test code that requires constant oversight.
• Live LLM-driven execution introduces unpredictability. Regulated teams need deterministic runs, not improvisation
• A clearer path is intent-driven authoring paired with deterministic engines and Visual AI that detects visual drift and preserves audit-ready evidence.
Request our Governance Readiness Checklist
Teams in regulated environments face a familiar strain. Applications grow in complexity, expectations for fast releases keep rising, and every update requires clarity about what changed and whether required elements still appear as intended. Traditional automation wasn’t built for that pace or level of oversight, and the recent wave of AI coding tools hasn’t solved the core challenges.
A better model is emerging—one that uses AI to reduce the workload of authoring and maintaining tests while keeping execution deterministic, reviewable, and aligned with how people evaluate digital experiences.
This post breaks down why the legacy testing model is hitting its limits and how AI can support a more stable, more trustworthy approach.
Why traditional automation keeps slowing teams down
As digital experiences expand across pages, portals, member journeys, and product flows, test code becomes difficult to scale. Even minor UI changes break locators and assertions, creating unpredictable test runs, delayed reviews, and long maintenance cycles.
Developers are often asked to take on more of the testing responsibility. While this can improve feedback loops, it does not reduce the burden of maintaining code that reacts poorly to UI changes. And when teams already lack time, context switching between product development and test diagnostics becomes expensive.
The result is a predictable bottleneck: too many tests tied directly to implementation details and not enough stability across releases.
Why AI-generated test code hasn’t fixed the problem
The last few years have produced a surge of tools that promise to generate automation code automatically. But teams report the same issues repeating in a new form. LLMs can produce code quickly, yet the resulting output still inherits all the maintenance challenges of coded automation.
AI code generators also excel more at producing new code than updating existing flows. They struggle with assertions, hallucinate element behavior, and require human supervision to validate every step. For regulated teams that must show repeatability and generate evidence for every release, inconsistency becomes a risk rather than a convenience.
If the goal is to escape brittle code, producing more of it is not the answer.
Why live LLM-driven execution creates instability
Another idea gaining attention is allowing an LLM to operate the UI directly during test execution. In theory, this removes the need to write code. In practice, teams quickly run into new risks: undefined steps, inconsistent interactions, slow decision-making, and no reliable way to debug.
Execution in regulated environments must be predictable. It must be reviewable. And it must produce evidence that can be traced, explained, and defended. Live improvisation during a test run undermines each of these requirements.
Determinism matters more than novelty. A testing approach must produce the same result today, tomorrow, and during an audit review.
A clearer path forward: intent-driven authoring with deterministic execution
A more reliable model is emerging that uses AI to simplify authoring without relying on AI to make real-time decisions during execution.
Teams describe test intent in natural language. An AI system translates that intent into structured steps during authoring, where humans can review and adjust. Execution is then handled by deterministic engines and Visual AI that observe the rendered UI and detect visual changes, required-element presence, placement consistency, and contrast.
This separation delivers two advantages:
- People write and maintain far fewer lines of test code
- Test runs become stable, repeatable, and easier to verify
Visual AI provides a complete view of the screen state and compares each run against an approved baseline. When something changes, the system surfaces the difference, captures evidence, and supports reviewer approvals. When the change is expected, one acceptance updates the baseline and applies it across browsers and devices.
The outcome is a testing layer that is easier to maintain and easier to trust.
What this looks like in practice
Teams adopting this approach typically see changes across several parts of their workflow:
- Tests are written in plain language, without selectors or framework setup
- Visual AI validates full screens for layout, presence, placement, and readability
- Changes are highlighted automatically to reduce manual inspection
- Evidence is captured through screenshots, diffs, timestamps, and logs
- Debugging takes place in an environment where runs behave the same every time
- Reusable flows and data-driven steps integrate into the same natural-language format
Instead of managing a growing volume of fragile code, teams maintain intent-level descriptions supported by deterministic execution.
What this means for oversight and compliance
For teams in financial services, healthcare, insurance, or life sciences, the benefits go beyond efficiency.
A visually grounded testing model helps confirm that required notices, disclosures, language-access elements, and other regulated UI content remain present and placed as expected. It documents what changed and preserves evidence for review. It supports consistent experiences across browsers, devices, and PDFs without checking whether values, data, or regulatory text are correct.
Most importantly, it delivers predictable results.
Regulated environments depend on clarity and traceability. When every test run yields reviewable outputs, and every change is captured with context, teams can maintain confidence and release with speed.
If you’re assessing how well your testing workflow supports stability and audit readiness, request our Governance Readiness Checklist. We’ll share the version designed for your stage—whether you’re evaluating Applitools or optimizing an existing deployment.
Frequently Asked Questions
AI testing in regulated environments must be deterministic. Generative AI can help describe test intent, but live LLM execution introduces inconsistent behavior and slow debugging. Regulated teams need predictable, repeatable runs that avoid improvisation and produce evidence they can review and defend.
Visual AI checks the rendered UI against an approved baseline, highlighting visual drift, and capturing screenshots, diffs, and timestamps for audit review. Learn more about Visual AI.
Code-centric UI tests break frequently as interfaces evolve. This creates delays, slows approvals, and complicates reviews. Using intent-based authoring paired with Visual AI reduces locator churn and helps teams maintain consistent coverage with less rework. Read more about PDF change detection and baseline comparison.
No. AI testing can detect visual drift, confirm required-element presence and placement, and preserve evidence. Validation of regulatory correctness, plan data, rates, or clinical content remains a human and organizational responsibility.
