The Estimator’s Edge: Where AI Wins and Where it Whiffs

Artificial intelligence in preconstruction continues to be a hot topic.

Every week seems to bring new promises of fully automated estimating, instant takeoffs, one-click bids, or software that supposedly replaces entire workflows overnight.

But inside real estimating departments, most teams are asking a much more practical question: Does this actually make us more efficient?

While AI absolutely has the potential to transform preconstruction workflows, the firms seeing real results today are not treating AI like magic. They’re using AI-assisted software as a practical tool to eliminate repetitive work and reduce operational friction — while simultaneously keeping construction professionals firmly in control.

The reality is simple: AI wins in some places. And it whiffs in others.

During a recent Togal20 webinar, Kelsey Formost sat down with Johnny Maghzal — former estimator and member of Togal’s founding team — to discuss where AI is genuinely creating value in estimating workflows, and where the industry still needs a reality check.

The conversation focused less on flashy AI promises and more on practical questions construction teams are asking every day: What actually saves time? What improves workflows? And what still requires estimator expertise?

The Problem Nobody Likes Talking About: Estimating Still Involves a Lot of Coloring and Tracing

For all the complexity and expertise involved in estimating, a surprising amount of time is still spent doing highly repetitive manual work:

• Tracing rooms.
• Coloring floor plans.
• Measuring walls.
• Renaming sheets.
• Repeating the same actions hundreds or thousands of times across a bid set.

It’s tedious. It’s time-consuming. And for many estimators, it’s the least valuable part of the job.

The irony is that estimators are highly skilled professionals who make critical financial and operational decisions for projects worth millions of dollars — yet many still spend hours manually tracing polygons in PDFs.

That disconnect is exactly where AI-assisted construction takeoffs begin delivering value.

Not by replacing estimators, but by removing the repetitive tasks that slow them down!

Where AI Wins: Eliminating Repetitive Tasks

The most effective AI tools in construction estimating are not replacing judgment. They’re accelerating execution.

AI works exceptionally well when applied to repetitive, rules-based tasks like:

• Identifying rooms and spaces
• Detecting walls
• Recognizing patterns and finishes
• Classifying tags
• Surfacing information buried in drawing sets
• Automating repetitive tracing workflows

Instead of manually outlining every corridor, room, or wall assembly, estimators can increasingly use AI-assisted workflows to identify and map those elements automatically — then validate and refine the results themselves.

The estimator remains the captain of the ship, and is still responsible for:

• Scope interpretation
• Validation
• Constructability understanding
• Trade coordination
• Quantity review
• Final pricing decisions

AI handles the repetitive groundwork, and the estimator applies expertise.

In practice, this shifts estimators away from low-value clicking and toward higher-value thinking.

That’s where the productivity gains start becoming meaningful.

As Johnny Maghzal said, “We have a lot of AI features in Togal, but it doesn’t mean you’re going to click one button and it will do everything for you. This is still a takeoff tool — but we can be ten or twenty times faster than traditional workflows.”

Where AI Whiffs

While new technologies are great, big promises also need a reality check.

Despite the hype, estimating is not becoming a one-click process anytime soon. Construction projects are too nuanced. Drawings are too inconsistent. Scopes vary too widely. And every estimator knows that the details matter.

No AI system fully understands:

• Intent behind incomplete drawings
• Trade-specific judgment calls
• Local market conditions
• Scope gaps
• VE considerations
• Constructability concerns
• Client expectations

And that’s before accounting for the fact that estimating workflows differ dramatically between companies, regions, and project types.

A $10 million contractor in Texas may estimate projects completely differently from a billion-dollar contractor operating across multiple states. That variability makes fully autonomous estimating extremely difficult.

Which is why the most credible AI conversations in construction focus on empowering estimators to do what they do best.

The Compound Interest of Speed

One of the most overlooked advantages of AI-assisted takeoffs is how speed compounds operationally over time. Saving a few minutes on a single task may not sound transformational. But when those savings happen:

• Across every plan set
• Across every estimator
• Across every office
• Across every bid cycle
• Across an entire year

…the impact becomes significant.

Faster takeoffs create ripple effects throughout preconstruction:

• More bids completed
• Faster turnaround times
• More opportunities pursued
• More time for review and coordination
• Less estimator burnout
• Better resource allocation

This is where modern estimating platforms begin delivering value beyond the AI itself. The real ROI often comes from workflow acceleration.

Large contractors are already seeing this play out in measurable ways. As estimating teams reduce repetitive manual effort, they create more capacity for strategic work — without necessarily increasing headcount at the same pace.

Speed compounds over time, and operational efficiency becomes a competitive advantage.

The Advantage of Cloud Collaboration

Some of the biggest workflow improvements in modern takeoff platforms come from cloud-based collaboration.

Historically, estimating workflows have been filled with operational friction:

• Downloading files
• Managing versions
• Emailing PDFs
• Coordinating updates
• Sharing spreadsheets
• Tracking permissions
• Reconciling “latest” files

Those inefficiencies add up quickly — especially across distributed teams.

Cloud-based precon platforms are changing that dynamic by allowing multiple stakeholders to work from the same live environment in real time:

• Estimators can collaborate across offices.
• Superintendents can access quantities from the field.
• Teams can review takeoffs simultaneously.
• Junior estimators can receive live guidance and validation.

Instead of downloading reports, attaching files to emails, and chasing version control, teams can increasingly work from a shared source of truth.

For many firms, this collaboration layer becomes just as valuable as the AI layer itself. Because while AI accelerates tasks, cloud-based workflows reduce operational friction across the entire estimating process.

Built by Estimators, Not Just Software Developers

One of the clearest themes emerging across construction technology is that domain expertise is crucial. Construction workflows are nuanced.

The best takeoff, estimating, or precon tools are rarely built by teams operating purely from a software perspective. They’re built by people who understand:

• Bid pressure
• Scope gaps
• Plan ambiguity
• Trade coordination
• Drawing organization
• Quantity validation
• The day-to-day realities of preconstruction

That context shapes product decisions in important ways.

Features like 3D takeoff visualization, live collaboration, pattern recognition, or wall-tag classification are valuable because they solve specific estimating problems — not because they sound impressive in a demo.

Good construction technology reflects actual field and estimating workflows. And increasingly, construction firms are recognizing the difference between software designed for presentations and software designed for production environments.

What Practical AI Adoption Actually Looks Like

The firms successfully adopting AI in takeoffs and preconstruction are generally taking a practical approach.

They are not expecting full automation.
They are not removing estimators from the process.
And they are not chasing technology for its own sake.

Instead, they are asking practical questions:

• Is this faster than our current workflow?
• Does this reduce repetitive effort?
• Does this improve collaboration?
• Does this integrate into our process?
• Does this help our estimators focus on higher-value work?

The future of estimating is not fully autonomous construction workflows operating without human oversight. The future is estimators equipped with better tools that reduce friction, accelerate production, improve visibility, and help teams scale easily.

Maghzal says, “Go into the mindset of asking, ‘Is this tool faster than my current tool?’ instead of asking whether it has AI or not.”

Interested in seeing what AI-assisted takeoff software can do for your estimating team? Request a custom demo of Togal.AI today!