QA in AI: How to prevent bad data from undermining your AI implementation

AI initiatives promise smarter decisions, faster innovation, and a competitive edge. But your business’s AI is only as good as the data behind it, and any flaws in the data can show up in much bigger ways downstream.

From subtle transformation errors to biased training sets, poor data quality undermines even the most promising models. As a result, model performance degrades, the organization is open to increased regulatory risk, missed opportunities, and money is spent on fixing errors caused by what should have been a helpful tool.

In this session, the third in our four-part AI Masterclass series, we’ll explore three critical data threats that can derail AI projects:

• Silent transformation errors that slip through unnoticed and cause your models to drift over time
• Incomplete and biased datasets that skew predictions and amplify risks — especially in high-stakes use cases
• Malformed data that clogs workflows, breaks integrations, and slows time to value

We’ll also lay out how automated, end-to-end data testing helps teams ensure the data that powers AI is reliable, complete, and production-ready at scale.

Whether you’re just starting to explore AI or already scaling it within your enterprise, this session will equip you with the tools to maximize your AI investment and reduce the risk of costly surprises.