Same Data, Different Outcomes: How Prompt Variability Exposes Hidden AI Risks

Enterprises often invest significant effort into securing datasets, configuring responsible AI agents, and implementing strict guardrails. But what happens when different users — armed with the same inputs — prompt the system in entirely different ways? What if those prompts lead to radically divergent outputs, even within a tightly controlled environment?

In this session, we’ll explore a recent hands-on workshop designed to surface this very issue. During the session, over business and technical stakeholders interacted with the same generative AI agent, using the same underlying data and capabilities. The only variable? Their prompts. The results exposed a critical but often overlooked risk: prompt variability can lead to unpredictability, misalignment, or even reputational risk — all without any change to the model or its data.

We’ll present this as a case study in how to intentionally design exercises that make the risks of prompt-driven divergence visible to non-technical stakeholders. Attendees will gain practical insight into the limits of AI guardrails and the real-world complexity of human-agent interaction in enterprise settings.