You have seen the demos. You have heard the success stories. But jumping straight into company-wide AI adoption feels like betting the farm on technology you have not tested. Here is what successful fabricators know: the companies winning with AI did not start big. They started with pilot programs that proved value on a small number of real bids before scaling to the whole team.
This article sits under The Ultimate Guide to Steel Estimating and walks through how to design a pilot program that minimizes risk, proves ROI on your own projects, and builds the internal confidence needed for full adoption.
The statistics are sobering. Gartner predicts that at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs, and unclear business value as the primary failure modes.
The main culprits behind failed AI adoption are consistent across industries:
The pilot approach solves all five problems by letting you fail fast and cheap, expose problems while they are still manageable, and build internal champions who drive broader adoption.
This aligns with what the NIST AI Risk Management Framework recommends for any high-stakes AI deployment: govern, map, measure, and manage as iterative functions, not a one-time checklist. Pilots are the "map and measure" phase that prevents the "manage" phase from becoming an emergency.
For the broader case on bringing AI to your team without disrupting workflows, see Change Management for AI in Steel Estimating: How to Bring Your Team Along and How AI Integration Transforms Existing Steel Estimating Workflows Without Disrupting Your Team.
Successful AI pilots follow a predictable pattern. The framework below maps to a typical 90-day window, which is long enough to capture real performance variation and short enough to maintain focus.
Start with non-critical projects where mistakes will not cost you a client or massive rework. This gives your team room to learn without pressure.
Ideal pilot projects:
Measure everything. Document time spent, accuracy achieved, and problems encountered. You need baseline data to prove improvement. Run AI in parallel with manual takeoff in this phase to build a real comparison. The same QA discipline we covered in AI Errors and How to Catch Them: Quality Control Best Practices applies here.
Once your team is comfortable with the basics, test the AI on more complex work. This reveals integration challenges before full rollout.
Add complexity gradually:
Integrate with existing workflows. Start connecting AI outputs to your current systems. Test data flow to Tekla PowerFab, Excel templates, and your estimating software. The export side of the workflow is where pilots most often surface friction that needs to be solved before scaling.
With proven time savings, the question shifts from "does this work?" to "what do we do with the extra capacity?"
Options to test in this phase:
For more on what capacity multiplication looks like at scale, see How AI Multiplies Estimator Capacity (With Real Examples) and Breaking the Headcount Barrier: Scaling Bids Without Hiring.
Your pilot team determines success more than the technology itself.
The Champion. Your most tech-forward estimator. Naturally curious about new methods, respected by peers, willing to document and share learnings. One person.
The Skeptic. An experienced estimator who questions everything. Provides valuable pushback, helps identify real-world problems, and becomes a powerful advocate once convinced. One person.
The Support System. An IT contact for technical issues, a manager for resource allocation, and one additional estimator for validation. Two to three people.
Keep the team small. Three to five people maximum. Larger groups move too slowly and complicate feedback.
You can bring these groups in later, after proving success with the early adopters. The mistake to avoid is loading the pilot team with the people who most need convincing. They will sink it.
Vague goals kill pilot programs. You need specific, measurable targets.
Time savings.
Accuracy comparison.
Project throughput.
User satisfaction. Ease of use rating, likelihood to recommend, biggest pain points identified.
Integration success. Data transfer accuracy, workflow disruption level, training time required.
30-Day Check.
60-Day Check.
90-Day Decision.
For more on whether your shop is ready for this kind of pilot, see 5 Signs Your Steel Estimating Process Is Ready for an AI Transformation.
Here is what to focus on each week of a typical 12-week pilot.
Install software, set up user accounts, configure settings, and test with a sample project. By end of week, upload your first real project, work through the complete workflow, and document every step. The critical habit: do not skip documentation. You will forget important details later.
Complete three to five small projects. Run them in parallel, AI and manual, and compare results to build confidence in accuracy. The daily routine: upload in the morning, review AI results midday, manual verification in the afternoon, document findings before signing off.
Speed optimization. Stop parallel processing on standard elements. Trust the AI for the work where it is consistently accurate. Focus manual review on complex areas, unusual details, and high-impact items only. This is the inflection point where time savings become real.
Test exports to your estimating software, data flow to Excel, and integration with Tekla PowerFab. Document integration issues and create workarounds where needed. Calculate total time including exports, not just the takeoff itself.
Gradually increase complexity. Add renovation projects, then projects with moment connections, then poor-quality drawing sets, then rush estimates. Weekly reviews become critical. Share learnings with the broader team to build anticipation for the full rollout.
Shift focus to rollout preparation. Document best practices. Create training materials. Build an FAQ database. Calculate detailed ROI. The final deliverable: a one-page business case for full adoption.
Starting too big. Testing on a massive complex project immediately is the most common failure mode. Begin with projects under 50 tons. Early wins build confidence. Early failures kill momentum.
No executive sponsor. Pilots without C-level commitment lack the authority to get resources when challenges arise. Secure the sponsorship before starting, not after.
Comparing apples to oranges. Testing AI on projects unlike your typical work produces meaningless results. Use representative projects from the past year.
Ignoring change management. Focusing only on the technology and not the people. Technology rarely fails outright. Adoption does. Spend equal time on training and communication.
Rushing the timeline. Pressure to show immediate results compresses the pilot into an unrealistic window. Commit to the full 90-day pilot upfront. Behavioral change takes time.
Poor documentation. Losing the insights and learnings that would have justified expansion. Assign someone to document daily.
Technology does not drive adoption. People do.
The right champions are not always the most senior people. Look for:
These people become your champions, regardless of title.
The most powerful champions are converted skeptics. Maccabee estimator Dawn Hargraves's framing of her own experience with LIFT shows what conversion sounds like:
"I actually appreciate that it's not 100% perfect because it keeps me engaged and checking the work. We can catch any issues while still saving massive amounts of time."
That is not a champion repeating marketing copy. It is a careful estimator describing her own discovery that the partnership model lets her stay engaged in the work that matters. Read the full Maccabee case study.
The conversion process:
Time savings are just the beginning. The full ROI story is more compelling.
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. Loaded 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.
The math:
Monthly labor savings = Hours saved per bid × Bids per month × Loaded hourly cost
Worked example. If your pilot shows 60% time savings on a typical 16-hour bid (9.6 hours saved), and your team runs 20 bids per month at $55 loaded cost, the monthly labor savings is about $10,560. That is roughly $126,720 per year in recovered capacity, even before counting any wins from additional bids submitted.
For the full ROI framework, see AI ROI Calculator: Estimating Your Potential Capacity Gain.
The most compelling pilot outcomes are the second-order benefits, which are harder to quantify but matter for the long-term decision:
For more on the broader economic case, see The Hidden Economics of Steel Takeoffs.
These published customer stories show what the pilot-then-scale pattern looks like in practice.
Maccabee Industrial used LIFT to align estimating capacity with fabrication expansion. The published case study documents 75% time savings on large projects, 50% overall speed improvement, and full team adoption within four months. Don Fleszar, one of their estimators, framed the math behind their decision 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."
Maccabee's case is the strongest published example in the LIFT customer base of a structured rollout. Read the full Maccabee case study.
MotionSteel adopted LIFT after retirements and resignations left them short-staffed. Their case study documents going from 30-40 estimates per month to about 70 with the same core team, more than doubling bid volume. General Manager Jay Livesey:
"It was a no brainer. I've been estimating for probably 8 years using just the good ol' highlighter and paper, wishing for a program that could automate this process."
Read the full MotionSteel case study.
SSE Steel Fabrication's published case study reports 50-80% time savings on estimating, with COO Justin Airhart describing the impact directly:
"Sketchdeck AI's tool, LIFT, has cut my estimating time by 50 to 80 percent, allowing me to focus on growing the business."
Read the full SSE case study.
King Steel's published case study documents cutting estimation time on complex structural projects roughly in half through a structured implementation. Read the King Steel case study.
Week -2.
Week -1.
Focus: Learn and document. Complete 20+ projects, document all challenges, achieve basic proficiency, build initial confidence.
Focus: Optimize and integrate. Test complex scenarios, connect to other systems, refine workflows, identify best practices.
Focus: Prove and plan. Calculate comprehensive ROI, build scaling strategy, create training materials, make the go/no-go decision.
The fabricators winning with AI did not bet everything on day one. They started with focused pilots that proved value before scaling.
The pilot approach gives you controlled risk exposure, proof of ROI on your own projects, internal champions who become advocates rather than reluctant participants, refined processes before broad rollout, and the confidence to scale aggressively when the case is proven.
The Gartner data is clear about what happens to organizations that skip this step: 30% of GenAI projects get abandoned after PoC, with unclear business value cited as a primary driver. A disciplined pilot is the cheapest way to make sure you do not end up in that statistic.
The first step is simple. Run an upcoming bid through LIFT in parallel with your current process, apply the metrics framework above, and decide where AI fits in your workflow. You can start by booking a live demo.
