Description
AI is changing estimating today, and will continue to evolve workflows in the future. The challenge is knowing which opportunities are worth pursuing.
Drawing on 14 months of real-world implementation, this session shares a practical framework for identifying AI use cases with the greatest potential based on three factors: impact, frequency, and visibility. It then tackles what happens after a solution works: driving adoption, creating repeatable processes, establishing ownership, and scaling across the team.
Attendees will leave with a framework for prioritizing AI opportunities and a roadmap for turning individual wins into lasting, scalable workflows.
Key Learnings
- Score AI use cases against three metrics high impact, high frequency, high visibility.
- Apply a three-question feasibility check before committing build effort.
- Define the change-management framework required to scale documented process, named ownership, and local adaptation.
- Evaluate a pilot against four recorded numbers hours per bid, request-to-quote time, bids per estimator, rework rate.
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