Companies’ Priority in the AI Era: Evolving Quality Testing Practices and Data Traceability

The introduction of AI into the tech stack breaks traditional testing methodologies. You cannot test AI processes with confidence without specialized controls in place such as data traceability.

Check out the latest article in The European Financial Review, on how to integrate AI into the evolving business landscape, featuring David Carle, AI and Data Innovation partner at Capital Markets Advisors LLC, and partner at Nexus FrontierTech for North America.

In today’s rapidly evolving business landscape, many organisations remain anchored to an outdated mindset when implementing artificial intelligence, treating it like traditional software where inputs and outputs follow clear, predictable patterns. While familiar, this approach no longer serves modern AI systems’ complexities. While conventional software development allows for straightforward testing and validation—with developers confidently asserting, “We know what data went in and what results should come out,” generative AI (hereafter GenAI), such as ChatGPT, introduces new complexities and risks that demand a fundamentally different framework.