The rise of large language models (LLMs) is transforming how research teams generate insights. But with that transformation comes a foundational risk: hallucinations – when AI outputs sound accurate but are actually fabricated, misleading, or unverified.
This white paper explores:
- What hallucinations are, and why they’re especially dangerous in the context of research
- The technical and operational safeguards top teams are implementing
- Practical questions insight leaders should ask before trusting AI with high-stakes outputs
The goal is not to reject AI, but to embed it responsibly. As enterprises move toward automation and scale, trust and transparency must become first-class features in every research workflow.