I am currently working on a large-scale infrastructure project and I’m curious about the practical steps to integrate Generative AI (ChatGPT, Gemini) into our risk management plan. Can these tools accurately predict schedule slippage based on historical qualitative data, or are they still better suited for just drafting documentation?
3 answers
Integrating these models into your risk management framework requires a shift toward structured data inputs. While they excel at drafting the Risk Register, their true value in 2025 lies in sentiment analysis of stakeholder communications to identify "hidden" risks that aren't yet in the logs. You should feed the model historical post-mortem reports to identify recurring patterns. However, always ensure you are using enterprise-grade versions to keep your project's proprietary data secure and avoid any potential "hallucination" in critical path calculations.
Have you considered if the current prompt engineering techniques are reliable enough for financial risk?
Start by using them to categorize your qualitative risks into buckets; it saves hours of manual work.
I agree with Sharon. Categorization is a low-risk, high-reward starting point for any project manager.
That's a sharp observation, Gregory! For financial forecasting, we usually treat the AI as a secondary validator. It shouldn't replace a Monte Carlo simulation, but it can explain the "why" behind the numbers in a way a spreadsheet can't.