In any boardroom conversation about technology investments, there’s often a quiet but looming presence—Quality Assurance (QA). Despite being essential to maintaining a robust web presence, QA is frequently seen as an unavoidable burden, a bottleneck that slows down releases and eats into budgets.

This perception aligns with the phrase “the elephant in the room”, which refers to a significant issue everyone is aware of but reluctant to discuss openly. For those unfamiliar with the term, here’s an excellent source explaining its origins. It highlights situations where the topic is too large or uncomfortable to ignore, yet no one wants to address it directly.

When it comes to QA, however, this perception is outdated. With advancements in AI-driven automation and the availability of SaaS tools, QA doesn’t have to remain that figurative elephant in your technology discussions.

The Traditional QA Bottleneck

Historically, QA has been a time-consuming, manual process. Developers hand over their work to testing teams, where human testers meticulously search for bugs, inconsistencies, and performance issues. While thorough, this approach often leads to:

• Delays: Testing cycles can take days or weeks, especially in large web applications.

• Costs: More manual hours mean higher labor costs.

• Human Error: Even the most skilled testers can overlook issues.

These challenges have painted QA as the “necessary evil” of software development. But this perspective is no longer valid.

Enter AI-Powered QA

Modern QA automation, supercharged by artificial intelligence, flips the script. Here’s how:

1. Automated Test Generation: AI can analyze the DOM (Document Object Model) of a web page, understanding its structure, and automatically generating Selenium or Cypress test scripts. This drastically reduces the manual effort needed for test creation.

2. Visual Regression Testing: AI can compare snapshots of web pages to identify pixel-level changes, ensuring consistent user experiences across updates.

3. Performance Monitoring: Tools powered by machine learning, such as Datadog or New Relic, continuously monitor web applications, spotting bottlenecks and anomalies in real time.

4. Smarter Debugging: AI doesn’t just find bugs; it suggests fixes by analyzing patterns across codebases and past issues.

The SaaS Advantage

The rise of SaaS platforms for QA brings even more accessibility and scalability:

• No Infrastructure Hassle: SaaS tools handle the infrastructure, so you don’t have to worry about managing test environments.

• Pay-As-You-Go: Flexible pricing models let companies scale their QA efforts according to need.

• Seamless Integration: SaaS QA tools integrate effortlessly with CI/CD pipelines, enabling continuous testing and faster deployments.

Examples of these platforms include BrowserStack, TestRail, and Applitools, which combine AI and SaaS to deliver reliable and efficient QA solutions.

The Bigger Picture: Improved Customer Experience

Automating QA isn’t just about saving time or cutting costs—it’s about delivering better products faster. For companies with a strong web presence, this means:

• Fewer Errors: Customers experience fewer bugs and disruptions.

• Faster Innovation: Developers can focus on creating new features instead of firefighting old ones.

• Enhanced Reputation: A smooth, reliable user experience builds trust and loyalty.

Why Now Is the Time to Rethink QA

If your business is still stuck viewing QA as the elephant in the room, it’s time to change the conversation. By embracing automation, AI, and SaaS solutions, QA can transform from a bottleneck into a competitive advantage.

So, let’s talk about the elephant — and more importantly, let’s move it out of the room for good.

Yours truly,

Markus Karjalainen
CEO and Co-founder