Every AI estimating vendor will hand you an ROI calculator with impressive numbers. Most of them are right in the abstract and wrong about your specific shop. This article gives you the framework to build your own calculator using your real inputs, then anchor it against documented LIFT customer outcomes so you know whether the math is conservative or optimistic.
This sits under The Ultimate Guide to Steel Estimating and breaks the calculation into seven steps you can plug into Excel today.
For steel fabricators and erectors, AI ROI is not abstract. It shows up in three places:
The standard ROI framework breaks the calculation into three buckets: time savings, error reduction, and increased throughput. For LIFT, those map directly to labor savings from faster takeoff, avoided loss from fewer estimating mistakes, and capacity gains (more bids per month) from the time you free up. We covered the broader economics in The Hidden Economics of Steel Takeoffs.
To estimate ROI and capacity gain, you need a simple set of inputs:
The BLS Occupational Outlook Handbook puts the median annual wage for cost estimators at $77,070, or $37.05 per hour, as of May 2024. When you load that with benefits and overhead at the typical 1.3-1.5x multiplier, the fully loaded cost lands in the $48-$56 per hour range. Adjust for your region and your specific compensation structure.
First, estimate how much time LIFT can realistically save on an average job. The customer numbers give you a range to work with:
| Customer | Reported time savings |
|---|---|
| SSE | 50-80% reduction across estimating workload |
| Maccabee | 75% on large projects, 50% overall |
| MotionSteel | Equivalent to 40-50% of team time freed |
| MSE | Up to 95% on beam takeoffs |
Build the calculator around three scenarios:
The formula:
Time saved per bid = Current hours per bid × Time-savings %
Example: if the average bid takes 16 hours today and you assume 60% savings, time saved per bid is 9.6 hours.
For more on what AI actually does on a drawing, see How AI Reads Structural Steel Drawings and The Precision Gap: Why "Automated" Takeoff Software Is Failing Steel Estimators.
Capacity gain is where ROI gets interesting. The math:
Current bid capacity = H ÷ T
New bid capacity = H ÷ T_new
Capacity gain (bids/month) = New capacity − Current capacity
Where H is estimator hours available per month, T is current hours per bid, and T_new is hours per bid with LIFT.
Worked example. If each estimator has 160 hours per month and each bid currently takes 16 hours:
This is consistent with what MotionSteel saw in practice. The published case study documents the team going from 30-40 estimates per month to about 70, more than doubling bid volume with the same core team. As General Manager Jay Livesey put it:
"With LIFT, we went from doing roughly 30 to 40 estimates a month to hitting 70 monthly."
Read the full MotionSteel case study. For more on what capacity multiplication looks like across the customer base, see How AI Multiplies Estimator Capacity (With Real Examples) and Breaking the Headcount Barrier: Scaling Bids Without Hiring.
Capacity gain only matters if extra bids turn into profitable work.
The math:
Extra jobs won per month = Capacity gain × Win rate
Extra revenue per month = Extra jobs won × Average revenue per project
Extra gross profit per month = Extra jobs won × Average gross profit per project
A note on win rate. Maccabee's published case study mentions their 3-5% win ratio, which is typical for steel fabricators competing in a broad bid market. At that ratio, doubling your bid volume translates roughly to doubling your won work, assuming the additional bids are no less qualified than your current ones. As Don Fleszar at Maccabee framed it directly:
"If we can increase the number of bids we put out by 50 to 100 percent, we're going to increase the amount of work we have equivalently."
Read the full Maccabee case study.
The strongest documented business outcome in the LIFT customer base comes from SSE, where the published case study reports the company growing from $8 million to an anticipated $40 million in annual revenue over two years after adopting LIFT. Justin Airhart, SSE's COO, attributed the time savings directly:
"Sketchdeck AI's tool, LIFT, has cut my estimating time by 50 to 80 percent, allowing me to focus on growing the business. It's like having an extra team member who never makes mistakes."
Read the full SSE case study. A 5x revenue increase over two years is unusual and depends on many factors beyond the takeoff tool, but it shows what is possible when capacity gains land at the right moment in a shop's growth curve.
Even if win rate stays flat, time saved has a direct cash value.
The math:
Monthly labor savings = Hours saved per bid × Bids per month × Loaded hourly cost
Worked example. If time saved per bid is 9.6 hours, you do 20 bids per month, and your loaded estimator cost is $55 per hour:
Monthly labor savings = 9.6 × 20 × $55 = $10,560 per month
That is roughly $126,720 per year in recovered labor cost, which can be either redirected to higher-value work (more bids, deeper pricing analysis, client conversations) or treated as direct cash savings, depending on how your team is structured.
Most ROI calculators include error reduction as a separate benefit. The numbers here are harder to pin down because not all estimating errors are tracked, but the rework backdrop is well documented.
According to the Construction Industry Institute, rework represents between 2% and 20% of total project costs, with an average of 12%. PlanRadar's analysis of multiple rework studies puts current rework at 5-8% of total project cost. Not all rework traces to takeoff errors, but takeoff sits at the front of the chain, so mistakes there propagate through pricing, procurement, and fabrication.
For a steel-focused calculator, keep this simple:
Annual error savings = Current annual cost of estimating mistakes × Expected error reduction %
You can anchor expected error reduction to LIFT's documented accuracy range. MSE's case study reports overall accuracy on AI takeoffs in the 95-99% range, which is what allows the team to use the output as a trusted baseline rather than a starting point that needs full re-verification.
Even if you do not publish a default error-reduction percentage, the calculator can show the structure and let the user set their own value based on their historical change-order and rework data. For more on the accuracy side, see Speed vs Accuracy: Can You Have Both With AI?.
Every ROI calculation must subtract the investment cost.
The math:
Year-1 total benefit = Labor savings + Error savings + Extra gross profit
Year-1 net benefit = Year-1 total benefit − (License cost + Onboarding cost)
Ongoing-years net benefit = Annual benefits − License cost
ROI (%) = (Net benefit ÷ Total cost) × 100
Payback period (months) = Total cost ÷ Monthly net benefit
The CFMA Construction Financial Benchmarks Report shows industry average net profit margins running around 5-6%, with specialty trades around 6.9% and heavy industrial around 4.1%. At those margins, adding overhead without guaranteed backlog is risky. Adding AI-driven capacity is comparatively low-risk because the cost is bounded and scales with usage, and because the labor-savings line alone usually justifies the investment well before win-rate gains are counted.
For pricing context on AI estimating software, Dan Cumberland Labs' 2026 pricing analysis shows AI estimating tools ranging from $35 per month for budget options to $149-$299 per month per user for mid-market platforms. Steel-specific tools typically sit at the higher end because the AI is trained on a much smaller and more specialized dataset.
Curious whether your team is ready for this kind of pilot? 5 Signs Your Steel Estimating Process Is Ready for an AI Transformation gives you a quick checklist before you run the calculator.
To make this real, here is a one-page paper calculator you can rebuild in Excel:
| Input | Your value |
|---|---|
| Estimates per month | __ |
| Avg hours per bid (current workflow) | __ |
| Loaded estimator cost per hour | __ |
| Avg win rate | __% |
| Avg gross profit per won project | __ |
| Input | Your value |
|---|---|
| Assumed time savings | 40% / 60% / 80% |
| Annual LIFT cost (license) | __ |
| One-time onboarding cost | __ |
| Calculation | Formula | Your value |
|---|---|---|
| Time saved per bid | Current hours × time savings % | __ hours |
| New hours per bid | Current hours × (1 − time savings %) | __ hours |
| Current bid capacity per estimator | Available hours ÷ current hours per bid | __ bids |
| New bid capacity per estimator | Available hours ÷ new hours per bid | __ bids |
| Capacity gain | New capacity − Current capacity | __ bids |
| Monthly labor savings | Hours saved per bid × bids per month × hourly cost | $__ |
| Extra gross profit per month | Capacity gain × win rate × gross profit per job | $__ |
| Year-1 net benefit | (Labor savings + Extra profit) − (License + Onboarding) | $__ |
| Payback period | Total Year-1 cost ÷ Monthly net benefit | __ months |
| Customer | Result |
|---|---|
| SSE | 50-80% time savings, $8M → $40M revenue over 2 years |
| MotionSteel | 30-40 → 70 estimates/month, doubled capacity, 40-50% team time freed |
| Maccabee | 75% time savings on large jobs, 50% overall speed |
| MSE | 95% beam takeoff reduction, ~1 work week per estimator per month, 95-99% accuracy |
If your math lands well below those customer outcomes, your assumptions are conservative and the real result will likely be stronger. If your math lands well above them, your assumptions are aggressive and worth pressure-testing against your actual project mix.
A few benefits do not fit cleanly into a calculator but matter for the decision:
For more on how AI tools change daily work without disrupting existing workflows, see How AI Integration Transforms Existing Steel Estimating Workflows Without Disrupting Your Team and Change Management for AI in Steel Estimating: How to Bring Your Team Along.
A defensible AI ROI calculation for steel estimating has five moving parts: time saved per bid, capacity gain, revenue from extra wins, labor savings, and software cost. The math itself is not complicated. The hard part is being honest about your current numbers (most shops do not track bid-level hours precisely) and conservative about your time-savings assumption.
If the conservative case (40% time savings, win rate held flat, labor savings only) still pays back within six to nine months, the investment case is strong. If it pays back faster than that, the upside on capacity and win-rate gains is gravy.
The first step is simple. Run an upcoming bid through LIFT in parallel with your current process, measure the real time savings on your specific drawings, and plug that number into the calculator. You can start by booking a live demo.
