Early-stage estimating has always depended on a rather small circle of people who possess the rare ability to convert half-formed concepts into plausible numbers. Most general contractors can name these individuals instantly; they are the ones who can glance at an outline of a building and produce a cost range that, through some combination of experience and instinct, turns out to be sensible more often than not. The challenge, of course, is that this kind of judgement cannot be summoned on demand nor easily reproduced across a growing organisation. This leads either to difficulty in ensuring the quality of all estimates or a significant bottleneck in the sales process. Parametric estimating offers a partial remedy by using measurable project characteristics to infer cost behaviour. People can achieve better estimates with less time and expertise. Yet historically, even this approach required expert calibration. The models were effective only because a handful of seasoned estimators fed them with their observations, refined them over the years, and quietly corrected the places where the maths was overly optimistic. AI shifts the dynamics in a useful way. Instead of relying on a small group of experts to manually encode their knowledge, AI can absorb years of their decisions, patterns, and assumptions simply by learning from past project data. Every estimate, value-engineering exercise, scope negotiation, and painful lesson becomes part of the model’s memory. The expertise remains human; the scalability is provided by the system. Three things follow from this: A wider set of team members can produce reliably good estimates, because the key knowledge is embedded in the model rather than a single individual’s mind. The organisation gains consistency, as similar inputs produce similar outcomes regardless of who happens to be at the keyboard. Continuous improvement becomes a genuine possibility, because each new project feeds back into the system rather than vanishing into a filing structure no one revisits. None of this replaces the experts. If anything, it gives them the breathing room to focus on improvement rather than recreating the same early-stage project estimate for the hundredth time. Their judgement is still essential, but now it can be amplified rather than rationed. If your firm is experimenting with parametric estimation, I’d be interested in hearing where it’s helping and where the human element still needs to guide the machine more firmly.