The construction estimating profession is at a turning point. In the next five years, the primary role of cost estimators will fundamentally change from generating estimates to validating those generated by AI. This transformation will elevate benchmarking from a best practice to a mission-critical capability that separates competitive companies from laggards.
AI-based tools are increasingly capable of generating estimates, such as generating scope, calculating quantities, and applying pricing faster than any human team could handle. The question is not yes this technology will come, but how quickly will estimators adapt to their new role: validating AI-generated estimates rather than creating them from scratch.
This change is not about replacing experience. It’s about raising it. Consider what happens when an AI generates an estimate in minutes instead of days. Someone still needs to validate these numbers with reality. Does the AI consider local market conditions? Do you understand the nuances of this particular group of subcontractors? Have you considered lessons learned from similar projects that didn’t go as planned?
That’s where benchmarking goes from useful to mission critical. When you generate estimates manually, you inherently carry forward institutional knowledge. Remember that healthcare project where MEP costs were high, or that educational center where site work was more complex than expected. But AI does not have this experiential wisdom. It needs data. Good data. Comprehensive, standardized and comparable data.
This is precisely why robust benchmarking solutions like Eos Cortex are becoming essential infrastructure rather than nice-to-have tools. When an AI issues an estimate, the estimator’s new job is to validate it against selected historical data. Not the generic industry averages, though your the company’s actual project history, normalized by time and location, and structured to allow a true apples-to-apples comparison.
The irony here: As AI makes generating estimates faster, it actually increases the value of the estimator. Why? Because validation requires judgment that algorithms cannot replicate. It requires understanding when numbers make sense and when they don’t, based on experience and context. It requires knowing which red flags matter and which variations are legitimate.
But here’s the catch: estimators can only validate effectively if they have the right benchmarking infrastructure in place. You can’t pressure test an AI-generated estimate against gut feeling or scattered Excel files. You need systematic access to historical project data, organized in a way that allows for quick comparison and analysis.
For companies that haven’t invested in comprehensive benchmarking platforms, this transition will be painful. They will find themselves accepting AI-generated estimates at face value (a risky proposition) or reverting to manual estimation because they lack the data infrastructure to validate automated outputs. Neither option is sustainable.
The winners in this new landscape will be companies that consider benchmarking not as record-keeping but as a strategic infrastructure. You will understand that good benchmarking platforms do more than store old estimates. They transform historical data into a corporate asset that enables rapid validation and moves it from the back end to the start of the process. This will support better decision-making and ultimately make AI tools more useful, not less reliable.
We’ve been here before. When spreadsheets replaced manual calculations, some saw it as the end of the estimator. Instead, it elevated the role, freeing estimators from arithmetic to focus on judgment and strategy. This AI transition is the same pattern, only accelerated.
The way forward
This isn’t new, but it’s worth repeating:
- Standardize how you capture and code project data
- Implement systems that can normalize data across time, location, and scope
- Empower estimators to think like model-validating data analysts
- Document why you believe or choose to adjust AI-generated content
The estimators who thrive won’t be the ones who can generate the fastest takeoffs. They’ll be the ones who can tell you, with data-backed confidence, whether that AI-generated number is solid or suspect. And they will do so using benchmarking tools sophisticated enough to make the validation reliable, repeatable and defensible.
