HRTech Interview with Oleg Pravdin, Head of Product at Lumber

Oleg Pravdin, Head of Product at Lumber chats about the challenges surrounding employee management in the construction industry with a focus on how new-age HRTech platforms help alleviate them:

__________

Tell us about Lumber and what inspired you to build this type of HRTech platform?

Construction is the second-largest industry in the world. It also runs on some of the most broken workforce systems imaginable.

I’ve seen it firsthand — foremen tracking 40-person crews on paper, payroll teams deciphering handwritten timecards at midnight on Friday, compliance officers piecing together prevailing wage rules across three jurisdictions simultaneously. This isn’t just a data problem. It’s a systems problem — one where the tools have never kept pace with the complexity of the work.

Most HR platforms were built for offices. Stable teams. Predictable schedules. Construction is none of those things. Crews move. Rules change. Projects overlap. And the industry runs on margins that leave almost no room for error — a typical construction project operates at 3–5% net margin. A single misclassification, a missed fringe contribution, a late certified payroll filing — any one of those can erase the profit on an entire job. In an office environment, a payroll error is an inconvenience. On a job site, it’s the difference between finishing in the black and going upside down on the project. 

What drove us was a pretty simple question: What if the workforce system actually understood construction? Not just stored data about it — but interpreted it, validated it, and flagged problems before they became expensive.

That’s the version of workforce management we wanted to build. Not a digitized version of the old process. An intelligent one.

What are the usual workforce management issues that plague modern construction teams, and how do tools like Lumber help?

Three things break construction workforce management consistently:

First: the data lag. Time is captured in the field. Errors are discovered in payroll — days later, after those errors have already compounded. By the time someone notices the wrong classification was applied, you have overpaid, underpaid, or violated a wage determination. The damage is done.

Second: rule complexity at scale. A single project can involve Davis-Bacon federal requirements, a state prevailing wage determination, multiple active CBAs, and workers from different trade classifications — all with different overtime rules. Managing that manually isn’t just tedious. It’s genuinely risky.

Third: workforce variability. Construction teams are not static. Workers rotate between projects, classifications shift mid-job, new hires come on as old ones roll off. Every change is a potential compliance event if your system isn’t built to handle it in real time.

The shift AI enables is moving from record and process to interpret and validate. Instead of a time card sitting in a queue until Friday’s payroll run, an intelligent system can flag an anomaly — wrong cost code, missing certification, overtime threshold hit — the moment the data comes in. That’s the difference between catching a $40,000 error and writing a $40,000 check.

Catch more HRTech Insights: HRTech Interview With Hari Kolam, CEO and Co-founder of Findem: Featuring Findem’s GliderAI

Why are employee benefits and engagement practices in the construction sector more challenging than in other industries?

In my opinion, the construction workforce doesn’t fit any of the assumptions traditional HR models are built on — and that creates challenges well beyond just benefits administration.

Start with the employment cycle. A worker can be with an employer for a single day or twenty years. The terminate, hire, and rehire pattern is constant — someone wraps up one project and gets brought back three months later for the next. Benefits eligibility, credential tracking, PTO accruals, and onboarding paperwork have to be managed across that kind of cycle without dropping the ball. Most systems aren’t built for it.

But engagement in construction is also multi-dimensional. Yes, accurate and on-time pay matters a lot. But so does making sure a worker’s certifications and credentials are current before they step onto a job site. So does helping a foreman understand which crew members are actually available and qualified to work that job. So does ensuring a worker knows their PTO balance, their fringe contributions, and what benefits they’re entitled to even if they’ve only been on-site for six weeks.

What AI does in this context is bring consistency to a workforce that has almost none by default. Eligibility is tracked automatically. Credential expirations get flagged before they become a compliance issue or a safety risk. Crew composition recommendations can be informed by historical performance and qualification data, not just whoever’s available. The goal isn’t just to pay people correctly — it’s to manage the entire employment relationship intelligently, even when that relationship looks different every quarter.

For industries like construction, where people management is more challenging because of increased temp workers or project-based workers, how are modern HRTech innovators using AI to alleviate this?

The most impactful applications right now fall into three categories:

Real-time anomaly detection. Instead of discovering that a worker was misclassified for three pay periods during a quarterly audit, AI can flag the issue at clock-out. Unusual overtime patterns, missing apprentice certifications, incorrect cost code assignments — these can all be caught before they become payroll problems or compliance violations.

Automated rule interpretation. Wage determinations, union agreements, and compliance requirements aren’t simple documents — some CBAs run 300 pages with embedded tables, conditional fringe rates, and jurisdictional carve-outs. AI systems can read, parse, and apply those rules at a worker-classification level, without a human having to manually map every scenario. That’s the difference between a compliance function that scales and one that breaks under project volume.

Workforce intelligence and forward-looking planning. Most construction teams operate with rearview-mirror data — they know what labor cost last quarter, not what it’s trending toward. AI can analyze historical patterns to surface early warning signs: projected cost overruns, crews running thin on a critical trade, overtime escalating toward a threshold that triggers a renegotiation clause.

What makes all of this particularly significant in construction is the transient nature of the workforce. Workers cycle on and off projects continuously — some for days, some for years. That terminate-hire-rehire pattern creates a constant stream of onboarding events, compliance checks, and eligibility decisions. AI doesn’t get tired of that volume. It handles the 500th re-hire with the same accuracy as the first.

The thread connecting all three is a shift from reactive administration to continuous decision support. Human oversight doesn’t go away — it just gets focused where it matters most.

Five thoughts on the future of AI and HRTech?

1. AI becomes the system of record — not just the system of storage.Today’s HR platforms store what happened. Tomorrow’s will validate, interpret, and act on it in real time. The shift from database to decision infrastructure is already underway.
2. Compliance stops being an audit function and starts being a default.Right now, compliance is something you check for. In three to five years, every workflow — time entry, payroll run, job costing — will be compliance-enforced by design. The question won’t be “are we compliant?” It’ll be “the system wouldn’t let us not be.”
3. Domain-specific AI outperforms general AI by a wide margin.A general-purpose AI model has no idea what a WH-347 is, how fringe contributions interact with overtime calculations, or what the carve-outs are in a California prevailing wage determination. The most valuable AI systems will be trained on industry-specific rules, edge cases, and data. Construction — and every other complex industry — will get its own purpose-built intelligence layer.
4. Human roles evolve toward exception management and strategic judgment.AI will handle the routine. Humans will handle the nuanced. That’s not a threat — it’s a better use of skilled people. A prevailing wage specialist reviewing five complex edge cases per day is more valuable than one manually processing 500 standard calculations.
5.  The gap between AI-native and AI-adjacent businesses will widen fast. Companies that embed intelligence into their core workflows will operate at a fundamentally different cost and accuracy level than those that bolt it on as an afterthought. In construction — an industry with notoriously thin margins — that gap becomes existential quickly.

Read More on Hrtech : Why SWIFT is Too Slow for Your Global Workforce?

[To share your insights with us, please write to psen@itechseries.com ]