I've spent the last few months interviewing over 100 QA engineers and managers, scouring Reddit QA subreddits, and trawling through Quora discussions. Teams across various industries - using tools like Playwright, Cypress, Appium, and Selenium - have shared the same top issues, whether working on web or mobile apps. In this post, I'll walk through these test automation challenges, and then propose how AI, combined with an economical and fast test infrastructure, is quickly becoming the solution for 2025 and beyond.
1. Time Constraint: Squeezed Out of Sprints
The Problem:
The biggest complaint I hear is the lack of time in each sprint to create or update automated tests. Developers often take up 80–90% of the sprint with feature implementation, leaving QA with a sliver of time at the end. If bugs surface late, QA has to juggle retesting, writing new automation scripts, and dealing with code freeze deadlines. As one QA manager said, "By the time devs finish, we have maybe two days left to automate. It's never enough."
This shortage of time leads to partial test coverage, technical debt, and missed regressions. A 2022 Agile Testing survey noted that 60% of teams cite "insufficient time" as a core barrier to achieving full test automation coverage.
Proposed AI-Aided Solution:
AI-driven test generation tools can cut the time to author new tests from days to minutes. By ingesting application flows or user stories, AI agents can propose scripts for Playwright, Cypress, or Selenium almost immediately. The QA engineer then reviews and refines these scripts, rather than writing them from scratch. This "human + AI" model keeps engineers in control while accelerating coverage.
- Looking Ahead: In many of my conversations, teams predict that within a couple of years, AI augmentation will be standard. New-generation QA roles will focus less on manual test writing and more on reviewing AI-generated tests and shaping overall quality strategy.
2. Maintenance Overload: Brittle Scripts Over Time
The Problem:
Even if you manage to automate a decent chunk of test cases, they often break after each release. One minor UI tweak or a new API endpoint can invalidate half your locators. This "brittle script" phenomenon forces QA engineers to spend an inordinate amount of time updating tests instead of creating new coverage. Frequent complaints include:
- Sudden failures in Cypress tests because a button ID changed.
- Selenium scripts timing out due to newly inserted pop-ups.
- Lack of advance notice from developers on upcoming feature changes.
A report from the Test Automation Alliance indicated that 48% of teams rank "continuous script maintenance" as their top QA headache. When dev teams move quickly, QA automation lags in a perpetual game of catch-up.
Proposed AI-Aided Solution:
AI "self-healing" test frameworks are designed to mitigate these headaches. Instead of failing outright when a selector changes, an AI-driven engine can:
- Automatically search for alternative locators or page elements.
- Suggest updated code or generate a pull request for QA to review.
- Adapt waiting mechanisms for asynchronous content (e.g., waiting for an element to be clickable).
By integrating an AI agent that monitors your application's DOM changes and flags or fixes broken scripts, QA teams can drastically reduce maintenance time. More advanced platforms even track changes in real time - imagine receiving an alert the moment a test fails due to a new UI structure, along with a suggested fix ready to approve.
3. Lack of Adequate Test Infrastructure: The Cost Conundrum
The Problem:
Even if you have well-structured automation scripts, you need the right environment to run them at scale - across multiple browsers, devices, and OS versions. Traditional device farms (like AWS Device Farm) often charge per-minute fees, and costs can soar when you run large suites or test on many device types. For startups or smaller QA teams, these expenses can be prohibitively high. As a result, many end up cutting corners - testing only on Chrome, for example - leading to missed bugs in less common environments.
I've watched teams burn hundreds (even thousands) of dollars a month on cloud testing without the budget to expand coverage. One QA lead told me, "We want to test on iOS Safari and older Android phones, but the costs are massive. Management keeps pushing us to cut back."
Proposed Infrastructure & AI Optimization:
- More Affordable Platforms: Next-gen test providers (e.g., Posium) are emerging, offering web and mobile test infrastructure at a fraction of the typical cost. This approach makes extensive parallel testing more accessible, so you're not forced to choose between coverage and budget.
- AI-Driven Test Selection: AI can also optimize which tests to run on which devices, reducing total billed minutes. Instead of running all tests on all environments, an intelligent orchestrator picks the most critical scenarios for each device, based on code changes and historical test data.
4. Debugging & Reproduction Woes: The 'It Works on My Machine' Dilemma
The Problem:
Nothing frustrates a QA engineer more than a bug that can't be reproduced in a developer's environment. Maybe it shows up only on certain devices or emerges under specific network conditions. When devs can't replicate the issue, it languishes unresolved or gets dismissed as "user error." In the meantime, customers keep encountering it in production.
These challenges faced by test automation engineers can lead to serious bottlenecks in debugging and resolving defects. Issues include:
- A crash reported on certain Android OS versions, but devs tested only on the latest version.
- Intermittent login failures on slow networks that devs never simulate.
- Complex environment mismatches - staging vs. production data differences - that hide the bug from dev tests.
Proposed AI & Tooling Solution:
- Better Observability: Tools like Playwright's trace viewer or Cypress's video capture can record test sessions, so devs see exactly what happened at failure.
- AI-Based Root Cause Analysis: Some AI-driven platforms automatically aggregate logs, environment variables, and user actions to pinpoint the likely cause of a crash.
- Environment Cloning: Containerization plus cost-effective cloud test providers allow QA to replicate staging or production conditions on demand.
5. Test Flakiness & Alert Fatigue: The 'Crying Wolf' in Automation Testing
The Problem:
Flaky tests - tests that fail intermittently for reasons unrelated to actual bugs - are the silent killers of QA credibility. Over time, these false alarms trigger "alert fatigue." Engineers start ignoring failures, assuming the test is just acting up again. The real danger? A legitimate defect can slip through unnoticed.
From my interviews, I've heard multiple horror stories about test automation challenges, including:
- An end-to-end test for checkout flow passing 90% of the time but randomly failing.
- Teams ignoring notifications from Slack or Jira because they assume it's "that same flaky test again."
- Legitimate production issues discovered too late because flakiness diluted the alerts.
Proposed AI Solution:
- Self-Healing Tests & Stable Waits: AI-based tools can dynamically adjust locators or waiting times when the application's state is loading, reducing random failures.
- Intelligent Failure Analysis: Rather than labeling every failure equally, AI can quickly classify a test as truly broken vs. possibly flaky.
- Quarantine & Triaging: Once flaky tests are identified, they're quarantined, investigated, and fixed without bogging down the main suite.
When evaluating solutions for test flakiness, check out our guide on how to pick AI-powered testing tools to ensure you select one with robust flakiness management capabilities.
The Panacea: AI + Economical Test Infrastructure
Over these last few months, it's become clear that the future lies in AI-driven test engineering plus affordable, high-performing test environments. Why?
- Speed & Coverage: AI "co-pilots" generate tests rapidly, bridging the time gap at the end of sprints.
- Continuous Maintenance: Self-healing scripts curb the busywork of chasing brittle tests, giving QA engineers more time to tackle advanced scenarios.
- Low-Cost Scalability: Without cost-effective environments, extensive testing remains out of reach. Platforms like Posium break that barrier, allowing broad coverage without breaking the bank.
- Smarter Debugging: AI-powered root cause analysis minimizes "it works on my machine" deadlocks.
- Stable Test Suites: Automated flakiness detection and healing ensures a failing test is truly a sign of a bug - not just a false alarm.
When I look ahead, I see QA teams increasingly adopting AI workflows. Instead of sprint's-end automation scrambles, we'll have real-time script generation and healing as features evolve. Faster, less brittle, and more cost-effective - precisely what modern QA needs to succeed.
Final Thoughts
From time crunches to brittle scripts, high infra costs, elusive bugs, and flakiness-induced alert fatigue - these five test automation challenges dominate the concerns of QA professionals today. By combining best practices with new-gen technology, teams can finally make automated testing as robust and efficient as we've always wanted.
If you're looking for a platform that integrates AI agents with cost-friendly, high-speed test infra, check out Posium. It's pioneering a new wave of end-to-end testing solutions designed to handle web and mobile at scale - helping QA teams tackle these challenges head-on.
Request a demo today to see how Posium can transform your testing process.
Written by
Naomi Chopra