AI is everywhere. The software testing industry is flooded with buzzword-heavy solutions, and you’d be hard pressed to find a vendor that hasn’t marked at least one of their tools as AI-powered.
Such overuse naturally invites scepticism, especially given past fads that promised the moon but failed to deliver. But is AI another in a long list of cautionary tales, or does it genuinely herald a new era?
Do Testing Fads Give You a Sense of Déjà Vu?
Something about this current trend is so familiar, you could be forgiven for thinking you’ve been here before.
In the last decade alone, codeless automation, parallel programming, and big data have swept through SDLCs with much fanfare and promise, only to fade as reality set in.
Most fizzled out because the hype exceeded capability, underlying complexity was downplayed, and organisations resisted (wouldn’t fund) the change required to realise the benefits.
Is AI-Based Testing Just The Latest Fad?
There are definite similarities with the current, and rampant, AI washing.
For a start, many testing products labelled as AI-driven lack genuine AI capabilities. Instead, they use simple-but-effective coding techniques that have been around for decades.
As with every other field, this is a case of software testing marketeers exaggerating the current state of technology. Claims that AI can automate everything, generate test suites instantly, or eliminate the need for human testers are just plain false.
Why Software Testing AI Might Just Be Different
Behind the excessive and unrealistic marketing though, lies something undeniable, enough for me to say with absolute conviction that AI isn’t just another marketing fad.
For one thing, unlike most prior SDLC trends, AI is already making a tangible impact:
- Self-healing scripts: AI updates tests as applications evolve, reducing flakiness and maintenance.
- Adaptive test selection: Machine learning targets high-risk areas, boosting testing efficiency.
- Video-based test creation: Teams can generate manual and automated tests from short user videos.
- Anomaly detection and analytics: AI sifts through massive test result sets, highlighting subtle regressions and risks.
AI has already been shown to cut test design and maintenance time, predict defects before they hit production, and fill test coverage gaps faster than manual methods.
AI Could Be The Most Significant Shift in QA… Ever
The reason AI isn’t just another fad? We are in the earliest days of a technology that has the potential to redefine human life in ways not seen since the Industrial Revolution.
In my opinion, AI will have a bigger impact than the telephone, the Internet, maybe even the printing press. It will continuously learn, adapt, and accelerate into something truly extraordinary; for better or worse.
AI is already polarising society. It has created a two-tier system between the AI-literate, confident early adopters and the rest. Those who embrace it see the opportunities, those who fear it see the threat.
The reality probably lies somewhere in between, and AI is already fundamentally changing working patterns across myriad industries.
When it comes to software testing, AI is already augmenting test management (identifying critical test sets, analysing results), generating and interpreting defect reports (even from videos or logs), and automating repetitive checks—which frees up QA professionals to focus on strategy and creative problem-solving.
AI’s role won’t stop at automation. It will become embedded in every stage of the SDLC, making the feedback loop between development, testing, and business goals smarter and faster than ever.
The question isn’t whether AI will change testing, it’s whether you will embrace it and thrive, or not.
Caveats: Tempering Hype with Current Reality
Of course, not every claim is valid, and present-day testing of AI solutions comes with fundamental limitations:
- Requires extensive, high-quality data for training.
- Lacks domain intuition and nuanced understanding.
- Needs human oversight for context, creativity, and strategic decision-making.
- Can be complex to implement, especially at scale.
Organisations that succeed will blend strong QA expertise with thoughtful use of AI, sceptically evaluating claims and investing in upskilling—not chasing magic bullets or quick wins.
Conclusion: AI Is a Defining Leap in Software Development
While the current hype cycles echo many past SDLC trends, AI is already delivering advances that outstrip previous fads.
For software testing professionals, the path is clear: Start now.
Look past the marketing noise and adopt AI with care and education. Measure its impact, stay up to date with advancements and, where you can, help guide your organisation’s AI future.
We may well be at AI v0.1, but as capabilities mature, the impact will only grow.
