Case Study: Building & Deploying Burns & McDonnell’s Enterprise AI Program to Gain Efficiencies in Preconstruction Workflows
- Implementing guardrails around uploading sensitive client information to protect intellectual property
- Navigating how to robustly train LLMs with redacted datasets to ensure quality of AI outputs
- Packaging complex AI capabilities into simple applications to increase usability for preconstruction end-users
- Determining whether to outsource AI expertise or hire an internal taskforce to align AI strategy with organizational capabilities and protect data sensitivity