AI Cost Estimation Accuracy: How Close Can It Get?

The question I kept getting from other GCs after testing AI estimating tools for several months: “But does it actually get the number right?” Fair question. Vendor demos show clean BIM models and tidy material schedules. Real jobs have hand-sketched plan revisions, incomplete soil reports, and subs who quote $40/SF one week and $55/SF the next. This article is an attempt to answer that question with the numbers I could actually verify.

What I Examined

This analysis draws from three categories of evidence:

Published accuracy standards. AACE International maintains an estimate classification system (Recommended Practice 56R-08) that defines five estimate classes by maturity of project definition and expected accuracy range. This is the industry’s own benchmark for what “good” looks like at each stage.

Vendor-published performance data. I reviewed publicly available case studies and technical documentation from Togal.ai, STACK, Buildee, ProEst, and the Procore estimating module. Where vendors published specific accuracy claims, I noted the project type, size, and data inputs they used. Vendor data should be treated skeptically, but it is useful for understanding the ceiling of what these tools claim.

Third-party and academic research. McKinsey’s 2017 construction productivity analysis established a baseline: the average large construction project runs 80% over budget and 20 months behind schedule. More recent academic work on machine learning in construction cost prediction has explored what training data quality means for model performance.

The AACE Classification Baseline

Before evaluating AI tools, it helps to know what manual estimation actually achieves. AACE’s five estimate classes define accuracy as a range around the final project cost:

• Class 5 (order-of-magnitude, 0-2% project definition): -50% to +100% accuracy range
• Class 4 (schematic/conceptual, 1-15% project definition): -30% to +50%
• Class 3 (design development, 10-40% project definition): -20% to +30%
• Class 2 (construction documents, 30-70% definition): -10% to +20%
• Class 1 (detailed/lump-sum bid, 50-100% definition): -5% to +15%

These ranges are not failures. They are the expected accuracy for manual estimates at each stage of project development. Any AI tool claiming to beat these benchmarks needs to specify which class, which project type, and what input data was provided.

What the Numbers Show

AI Performs Best on Class 5 and Class 4 Estimates

For early-stage budgeting, the AI tools I reviewed consistently performed within the Class 4 accuracy range and often approached Class 3 accuracy, with 15-25% variance from final costs in most published case studies. Buildee, which focuses on conceptual cost planning, published results from multi-family residential projects showing estimates within 12-18% of final construction costs when given basic square footage, location, and occupancy type. That is a genuinely useful result for an early feasibility check.

For Class 5 ballpark estimates, the tools are even more competitive. Given only a project description and location, Procore’s cost library and STACK’s historical data integrations can produce order-of-magnitude numbers in minutes that used to take an estimator half a day to rough out. The speed advantage here is real and not dependent on BIM.

Class 3 Accuracy Is Where It Gets Complicated

For estimates used in actual bidding, accuracy is harder to pin down and more dependent on inputs. Based on available data:

• Projects with complete architectural drawings and a clean BIM model: AI-assisted estimates can reach 8-15% variance from final bid prices in favorable conditions.
• Projects with PDFs, hand drawings, or incomplete scopes: variance typically increases to 20-35%, comparable to a manual Class 4 estimate.
• Projects in high-cost-volatility markets (coastal metros, remote sites): variance expands regardless of input quality, because the underlying cost data in most tools lags real market conditions by 6-18 months.

Togal.ai’s published case studies for commercial tenant improvements show takeoff quantities within 3-5% of manual counts for standard assemblies (floors, ceilings, walls). Where their numbers diverge is in pricing, not takeoff. A precise SF count of flooring does not help you if the tool prices that flooring at RSMeans regional averages that are 20% below what your local flooring sub quotes.

The RSMeans Problem

Most AI estimating tools either use RSMeans cost data directly or train on datasets built from it. RSMeans publishes location-cost indices that adjust national averages by city, but those adjustments lag the market. In San Francisco, Boston, and Manhattan, actual subcontractor pricing can run 40-60% above the RSMeans city index. In rural areas, labor availability and mobilization costs can swing a number equally far in the other direction.

ProEst and other tools that allow you to import your own historical project data can close this gap, but only if you have clean historical cost data organized by CSI division. Most mid-market GCs do not. Spreadsheet estimates from five years ago, if they even exist, are rarely formatted in a way that imports cleanly.

Limitations That Matter for Mid-Market GCs

Supply chain volatility. RSMeans and similar databases update annually or quarterly. Lumber prices moved 300% and back down in a two-year span during 2020-2022. No AI tool predicted that, and no database-driven tool can account for it prospectively. For materials with volatile spot pricing, AI-generated estimates need to be treated as baselines, not bids.

Unusual site conditions. Soil bearing capacity, dewatering requirements, underground utilities, and access constraints rarely appear in the drawings that AI tools read. These are the items that turn a $2.5M office renovation into a $3.2M project. AI tools have no way to see what is not in the documents.

Local labor markets. An electrician in Phoenix costs significantly less than one in Seattle. Most AI tools apply a location multiplier, but that multiplier does not capture current labor shortage conditions, union jurisdiction boundaries, or the fact that every sub in your market is booked out six months.

Subcontractor bid variability. The spread between the low and high bids on any trade package is typically 15-25% for a well-scoped job. AI can give you an expected cost, but it cannot tell you whether the market will be competitive when you go to bid.

What This Means for Estimating Practice

AI estimating tools are genuinely useful for two specific things: faster preliminary budgeting and faster quantity takeoff. For a Class 4 or Class 5 estimate where you need a ballpark number to tell an owner whether a project is feasible, these tools can cut your time in half with comparable accuracy to a manual estimate.

For final bid estimates, AI is a sanity check, not a replacement for calling your subs. Use the AI-generated number to verify your manual estimate is in the right range. If your hand-built estimate comes in at $3.8M and the AI tool says $2.2M, that is a signal to find out where you diverged, not a signal to lower your bid.

The GCs getting the most value from these tools are using them at the start of the estimating process (fast ballpark for owner conversations) and as a check at the end (does my number make sense?). They are not using AI to replace subcontractor solicitation.

Where More Data Is Needed

The published evidence on AI estimation accuracy has several gaps that matter for mid-market contractors:

Longitudinal tracking. Most vendor case studies compare the AI estimate to the bid price, not to the final cost at project completion. A study tracking AI estimates against actual costs at closeout, across 50 or more projects, would be significantly more useful. No one has published this.

Mid-market project types. The available case studies skew toward large commercial, multifamily, and healthcare. There is almost no published data on AI estimation accuracy for retail tenant improvement, light industrial, or residential additions, which represent a large portion of mid-market GC work.

Tool performance on subpar input data. Vendor demos use clean drawings. Studies of how these tools perform on real-world PDFs with hand markups, missing sheets, and partial specifications would be more useful than curated demos. Independent testing by someone with access to real project archives would change the conversation.

Until that data exists, the honest answer to “how close can it get” is: within 10-15% on Class 4 work with good inputs, and closer to 20-30% on anything with incomplete documents or volatile local pricing. That is better than nothing and useful for preliminary budgeting. It is not yet reliable enough to replace a fully-built estimate for a competitive bid.