I’ve been a proponent of AI-assisted research tools for years, so my team’s recent decision to step back from Iris.ai and return to structured database searches wasn’t made lightly. After a six-month pilot, we found the efficiency gains weren’t materializing for our specific use case in vendor risk assessment.
The core issue came down to precision and auditability. While Iris.ai excels at discovering tangential connections across disciplines, our work requires highly specific queries on compliance frameworks and audit trail standards. We’d often get fascinating, broad results, but miss the narrow, regulatory-focused papers we needed. The time saved in initial discovery was later lost in manual filtering.
More critically, for compliance documentation, we need to demonstrate exactly how we arrived at a source. The “black box” nature of some AI recommendations made it difficult to reconstruct our search logic for audit purposes. With traditional Boolean searches in our specialized databases, the pathway is perfectly clear and reproducible.
I still believe there’s a place for tools like Iris.ai, particularly in early-stage research or cross-disciplinary innovation. But for regulated, repeatable processes where precision and a clear audit trail are non-negotiable, we found the old ways are still the most reliable. I’m curious if others in governance or compliance-heavy fields have had similar experiences.
Review first, buy later.