AI Takes Away the Grunt Work for QSs | Gather

22 Feb 2026 · 1 min

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In this segment, William Doyle explains how AI and large language models can be applied to the vast amounts of site data—access, resource, progress, and health & safety records—to automate the routine, detail‑oriented tasks traditionally performed by junior quantity surveyors (QSs). By automatically reviewing records, flagging issues, issuing early warnings, and updating programmes, AI frees commercial teams from manual data wrangling and compensates for the current shortage of skilled QSs. The overall message is that AI can remove the administrative grunt work, allowing QSs to focus on higher‑value activities and making the whole team more efficient.

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Will, how can we leverage AI to unlock the full value of site records? We have the foundations of lots of different project sets of data: access data, resource data, progress data, health and safety data. We've got so much data, we actually now got to a point where we don't know what to do with it. Using large language models, using AI allows us to do more with that data quicker. My personal view is is that we will start to automate processes that we traditionally ask the most junior person in our team to do. Review that record, tell me what the issues are, flag anything, tell the team we're updating the program, raise an early warning, raise a conversation event. We can do that automatically now. So we actually give our commercial team the ability to use the data, not have to go through the data. And when people are spread thinly and there's not enough quality QSs out there in the market, we can now empower QSs to do more, take away the grunt work, take away the admin, and actually use technology like AI to make us more efficient.