AI or BS? What’s Actually Real in Construction AI

AI is everywhere in construction right now. That’s exactly why contractors need to slow down and ask better questions.

Plenty of construction AI software companies are promising proprietary models, next-generation machine learning, and AI-powered workflows. That’s all fine.

But the truth of the matter is that construction leaders aren’t asking: “Is it AI?”

They are asking the real questions:

• “Does this make me more money?”
• “Does it save me time?”
• “Does it measurably reduce risk?”

Those are the questions that matter.

In our first-ever Togal 20 webinar, we tackled this challenge head-on by discussing AI or BS: What’s Real in Construction AI?

Because construction buyers deserve better than buzzwords, here are 10 ways to tell the difference between real, AI-assisted construction technology — and AI theater.

#1: Start With the Business Problem (Not the Tech)

Algorithms don’t mean much on their own. Helpful tools start by solving workflow pain.

One of the first questions you should ask during a demo is simple: Does this replace my current tool, or supplement it? Either can be valid. But clarity matters upfront.

Then dig deeper:

• What specific workflow problem does this solve?
• Who uses this daily?
• What does their day look like before vs. after?

Red flag: If they lead with “Our proprietary AI model…” instead of “You’re currently losing 6–10 hours per bid because… and here’s how we can help you fix it”, you’re in AI theater.

Real construction technology begins with bottlenecks, not buzzwords.

#2: Look for Proof in the Demo

If the software is real, it should work in a live demo. Every technology has hiccups. That happens. But if all you’re seeing are pre-recorded demos on perfectly clean drawings, you’re not seeing reality.

Savvy contractors should ask for:

• A live walkthrough
• Their own plans
• Edge cases and messy sheets

Red flag: If the demo only works on pristine, cookie-cutter examples, that’s not production-ready AI.

Real AI can handle imperfect inputs because construction drawings are rarely perfect.

#3: Ask About Accuracy, and How Easy Cleanup Is

AI is not 100% accurate out of the box. Anyone who claims that isn’t being honest.

The better question is this: Is it dramatically faster, even with cleanup?

Think about a nail gun. It’s not perfect. You still fix a few nails. But it’s infinitely faster than hammering each one manually. AI-assisted takeoffs work the same way.

Ask:

• How easy is it to clean up errors?
• What happens when it’s wrong?
• How does the system improve over time?
• Is this rules-based automation or actual machine learning?

If the tool creates more cleanup work than it saves—you’re not gaining productivity, you’re paying for friction.

#4: Understand Implementation Reality

Construction is full of tools that looked promising. And then nobody used them. If implementation sounds heavy or confusing, adoption will fail.

Ask practical questions:

• How long does onboarding take?
• What internal resources do I need?
• Who owns this internally?
• What systems does it integrate with?
• What does adoption look like after 90 days?

Cloud-based tools matter here too. Continuous updates and performance improvements simply don’t happen the same way with legacy on-prem systems.

Red flag: If they can’t clearly explain onboarding and adoption, they haven’t done this at scale.

#5: Ask About Data & Security

Construction firms manage sensitive financial and project data. Security isn’t optional.

This is where generic AI wrappers and purpose-built construction AI start to diverge. Some tools simply sit on top of public large language models, sending prompts and project data outside your environment in ways that aren’t always transparent. Others are built within controlled systems with defined security protocols.

Ask direct questions:

• Where does my data live?
• Is my data co-mingled with other users?
• Is it shared with offshore teams?
• Is the AI pulling from the wider internet?
• Are you SOC-1 or SOC-2 compliant?

AI providers should be able to answer these questions clearly, since they know this is an issue of top importance.

Red flag: Consider it a red flag if the provider gets dodgy about answering cybersecurity-related questions.

#6: Pressure-Test the “AI” Claim

AI-washing is when companies rebrand basic automation or scripted logic as “AI” because the market rewards the label. If removing “AI” doesn’t meaningfully change the product, it’s probably just automation with better branding. Real AI should be obvious in workflow impact.

Consider asking:

• What part of this actually uses AI?
• What decisions are automated vs assisted?
• Is this generative AI, predictive AI, or workflow automation?
• What happens if I turn the AI off?

The best AI-assisted tools are exactly that—technologies that assist you to do your work faster, better, and more accurately than ever before.

#7: Compare It to Doing Nothing

There’s wisdom in “if it ain’t broke, don’t fix it.” But if you’re already evaluating AI tools, something likely is broken—even if it’s just inefficiency. In which case, doing nothing is the biggest risk of all.

The key is separating artificial urgency (“AI is the future!”) from operational urgency (“Your competitors are bidding faster and taking market share.”).

So double-check with yourself by asking:

• What happens if I don’t buy this?
• What risk am I taking by waiting 12 months?
• What’s the benefit of buying this now rather than later?

In competitive markets, slower workflows eventually show up in win rates and margins.

#8: Force Them to Prove ROI

Construction is margin-sensitive. Every tool has to earn its keep.

Ask:

• How many hours does this save per takeoff?
• What’s the average ROI for a company my size?
• How long until payback?
• What KPIs move?

For example, one drywall contractor using Togal.AI completed a takeoff in under 20 minutes; compared to nearly a full week in a legacy system.

More importantly, a peer-reviewed study involving the University of Kansas validated a 4x productivity improvement with Togal.AI over legacy tools.

‍Red flag: If a provider can’t clearly connect their tool to margin, speed, labor efficiency, or win rate, the value probably isn’t there.

#9: Learn From Other Pros in Your Space

Marketing decks are polished. Operators are honest. Look at reviews on G2, SourceForge, and Google. Look at the logos on their site. Reach out to peers in your network.

Ask them:

• What surprised you after buying?
• What didn’t work?
• Would you buy it again?

Construction is relationship-driven. Trust the feedback of people who’ve used it in real bids under real deadlines.

#10 Ask: “Would I Bet My Own Money?”

At the end of every demo, strip it down: Would you personally invest your own money in this? Would your estimating team thank you or quietly resent another tool that adds friction?

If the software doesn’t clearly improve speed, accuracy, win rate, margin, or labor efficiency, it’s probably not worth the investment.

The Bottom Line: Construction Buyers Deserve Better

AI isn’t valuable because it sounds advanced. It’s valuable because it changes outcomes. It’s all about utility.

At Togal.AI, this is exactly how we think about AI-assisted takeoffs. We built our AI-backed software to remove the friction that slows down estimators. By automating repetitive clicking, labeling, and tracing that drains time, we alleviate the bottlenecks facing preconstruction.

Because in construction, technology should offer a competitive advantage. If you’re evaluating AI right now, bring these 10 conversations into every demo.

‍And if you want to see what AI utility looks like on your own plans, book a custom demo with Togal.AI today.