Two projects can look comparable on paper and tell you very little about each other. A cost per square foot only helps if you understand what sits behind it.
One project might include furniture and technology while another records construction cost alone. One might have been priced eighteen months earlier in a different market, with a different specification and a different way of measuring floor area.
Establish a consistent basis for comparison
The figures are not necessarily wrong. They may have been recorded in different ways, at different times, for different scopes. Those differences can distort an early estimate if the team compares the headline numbers without reviewing their basis.
Before historical projects support a new budget, the team needs to establish what was included and excluded, how the area was measured, when the work was priced, where it was built, and what level of finish was assumed.
That review also needs to distinguish a difference that can be adjusted from information that is simply missing. An incomplete record should remain an open question rather than becoming an unsupported assumption in the next estimate.
Make the records easier to review
AI can help read across past estimates, group similar work, flag differences in scope or pricing, and explain the basis of an initial comparison. That gives a cost manager a more organized starting point for review.
Someone still needs to decide whether a past project is genuinely comparable and what adjustment is appropriate. The supporting records need to be available so that the team can question the comparison and understand its limits.
Good benchmarking depends on how well the data is understood and used. A larger portfolio becomes more useful when the context behind each project's costs can be carried into the next decision.