Steel Estimating Explained: Why It’s Still Manual and How AI Helps

Author: David Zabka, Detailing and Fabricating Product Manager, ALLPLAN
 

Steel estimating is emerging as one of the most practical entry points for artificial intelligence in construction.

Despite growing interest in AI across construction, real adoption remains limited. According to a recent industry report highlighted by Construction Dive, approximately 45% of construction organizations have not yet implemented AI at all, while another 34% remain in early pilot phases. Fewer than 2% report using AI across multiple processes, and less than 1% have fully embedded it into their operations.

In other words, while the potential of AI is widely recognized, most of the industry is still searching for where it delivers immediate, practical value.

Steel estimating is one of the clearest examples of where that value is beginning to take shape.

In a discipline where precision matters and margins are tight, not every workflow is equally suited for automation. But steel estimating is different. The very characteristics that make it tedious and time-consuming for humans, including manual takeoffs, repetitive calculations, and structured decision-making, make it an ideal candidate for AI.

Despite years of digital transformation, steel estimating remains fundamentally manual.

Most estimating workflows still require teams to review structural drawings, identify each member, measure it, and enter that data into estimating systems. Even with tools like Bluebeam and Tekla PowerFab, the process depends on human interpretation and manual input at nearly every step.

Experienced estimators can move quickly, but the work is inherently time-intensive. It’s not uncommon to hear that a single structural drawing sheet can take close to an hour to process. On larger projects with dozens of sheets, that translates into days or even weeks just to complete the takeoff portion of an estimate.

This creates a bottleneck at one of the most critical points in the design-to-build workflow, where projects are won or lost.

Estimating determines what work a fabricator can pursue. When capacity is limited, teams are forced to prioritize, often turning down viable opportunities simply because they don’t have the time to bid them.

This challenge is not theoretical. For many fabricators, it directly limits how much work they can pursue. At Master Steel, a structural steel fabricator and erector, estimating capacity became a clear constraint.

As Clayton Laird, Chief Business Development Officer at Master Steel, explained, “We’d get great opportunities in, and it was frustrating knowing we just didn’t have the time to pursue all of them.”

Steel estimating sits at an interesting intersection. At a strategic level, it requires deep expertise, including understanding design intent, assessing risk, and building a competitive bid. But much of the day-to-day work is repetitive, structured, and based on well-established patterns.

Structural systems follow consistent rules. Member types, connection logic, and framing layouts tend to repeat across projects. Estimators spend a significant portion of their time performing tasks that are necessary but not necessarily complex, such as reading drawings, identifying elements, measuring lengths, and organizing quantities.

This is exactly where AI performs best.

Artificial intelligence excels at processing structured data, recognizing patterns, and executing repeatable tasks with speed and consistency. When applied to steel estimating, it can take over the most time-consuming portions of the workflow while leaving the higher-value decision-making in the hands of experienced professionals.

The result is not a replacement of the estimator, but a rebalancing of their role.

The biggest shift happening today is in how takeoffs are performed.

Traditionally, estimators have had to manually work through each drawing, measuring and counting elements one at a time. AI changes that dynamic by analyzing entire drawing sets at once, detecting structural members, and generating quantities automatically.

Instead of building an estimate from scratch, estimators can begin with a generated output and focus on validating and refining it. This shift is redefining what modern steel estimating software can deliver in terms of speed and accuracy.

As teams begin adopting AI estimating software, that shift becomes clear in practice. As Laird described it, “Instead of one person looking at your estimate, now you’ve got two in essence — the AI doing its part, and the estimator checking it.”

That shift alone significantly reduces the amount of manual effort required while improving consistency across estimates.

For fabricators adopting AI in estimating, the most immediate impact is an increase in throughput.

In practice, this is already happening. At Master Steel, a structural steel fabricator, the team was able to increase estimating productivity by more than 60 percent, allowing them to process significantly more work without increasing hours.

This kind of improvement changes how estimating teams operate. Instead of being constrained by time, they gain the flexibility to evaluate more opportunities, respond faster to bid requests, and spend more time refining their approach.

It also reduces many of the common challenges associated with manual estimating. Errors from data entry, missed elements, and inconsistent assumptions become less frequent when much of the foundational work is automated and supported by visual validation through generated models.

The broader implication of AI in estimating is not just efficiency — it’s capacity.

Fabricators that can process more estimates in less time gain a meaningful competitive advantage. They can pursue more projects, be more selective about the work they take on, and position themselves earlier in the bidding process.

In many cases, speed matters as much as accuracy. Being one of the first to submit a well-supported estimate can influence how general contractors evaluate partners and structure their bids.

For smaller or mid-sized fabricators, this shift is particularly impactful. AI allows them to scale their estimating capabilities without scaling their team, enabling them to compete more effectively with larger organizations.

Despite these advancements, steel estimating is not becoming fully automated.

Human expertise remains essential for interpreting incomplete information, making judgment calls, and developing a winning bid strategy. Estimators still play a critical role in aligning estimates with real-world fabrication constraints and business objectives.

What is changing is how their time is spent.

Instead of focusing on manual tasks, estimators can concentrate on higher-value activities, such as reviewing outputs, managing risk, and making strategic decisions that directly impact profitability.

Steel estimating is one of the first areas in construction where AI is delivering immediate, measurable results.

As adoption continues, the workflow will continue to evolve. Estimating teams will increasingly use AI earlier in the process to evaluate whether a project is worth pursuing, not just to accelerate work after a decision has already been made.

For fabricators, the question is no longer whether AI will play a role in estimating. It’s how quickly they can incorporate it into their workflows to stay competitive.

Steel estimating has always been a critical function tied directly to growth and profitability.

AI doesn’t change that — it removes the friction that has historically limited it.

By automating the most time-consuming parts of the process, fabricators can unlock more capacity, pursue more opportunities, and make better use of the expertise already within their teams.

Steel Genie by Allplan is AI-powered estimating software that reads structural drawings, gathers material lengths and quantities, and generates a 3D estimating model with connections in minutes.

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