Perspectives

AI failures rarely look like crashes. More often, they appear as confident but incorrect answers, subtle bias, or culturally inappropriate responses.

AI doesn’t just fail with bugs. It fails in silence, in bias, and in behavior. That’s why traditional QA won’t cut it anymore.

As software systems get increasingly complex every day, the challenges of effective testing also escalate. Software testing involves handling large datasets, complex workflows, and shorter release cycles. 

Smarter AI starts long before the model is trained. It begins with the quality of the data you feed it. Data that reflects real-world nuance, cultural context, and human behavior is what sets strong systems apart.

Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes.

Smartphone users worldwide are expected to reach 7.7 billion by 2028. With such an enormous and growing user base, non-functional requirements like performance, security, reliability, and usability can make or break businesses.

In quality engineering, axioms are foundational truths drawn from years of practice that underpin effective, scalable automation systems.

Software systems are becoming more complex and interconnected every day, and as a result, effective testing is more important than ever. 

Article four in the “Quality Engineering for CEOs” series brings you an exploration of the role of software quality in organizational confidence.