How AI Accuracy Actually Works in Electrical Estimating

Estimators know the stakes. A small miss on a takeoff can move a job from profitable to painful, which is exactly why the conversation around AI in estimating always circles back to the same question. How accurate is it, really? In this episode, hosts Chris Trommer and Sal Huerta continue their conversation with Will Zacher to unpack what accuracy actually looks like inside an AI-assisted estimating workflow. They dig into why generic AI tools can miss the mark on complex bid documents, what kinds of guardrails make a difference, and how the right setup can turn AI into a reliable second set of eyes rather than a source of new risk. The conversation also covers what AI is genuinely good at catching, from line item mismatches to buried contradictions to requirements that don't appear in the plan set at all, along with an honest look at where the technology still has room to grow and how these platforms improve the more an estimator uses them. If you've been curious about how AI accuracy holds up in real estimating work, this conversation gives you a grounded look at what's possible today and what still belongs in human hands. What You'll Learn: •Why AI accuracy is measured differently in estimating than in other fields •How citations and visual references help estimators verify AI-generated findings •The categories of misses AI is best positioned to catch in a bid •How AI can surface requirements that aren't documented in the plan set •Where the technology still has limitations and what's coming next •How AI platforms adapt to an estimator's individual style and risk profile over time •Why the estimator's judgment remains the final word in the bid process