Fraser Patterson, Founder and CEO of Skillit – Interview Series

Fraser Patterson, Founder and CEO of Skillit, is a serial entrepreneur whose career spans more than three decades in the construction and technology sectors. Beginning as an apprentice carpenter in Scotland before advancing to journeyman carpenter, ironworker, general contractor, and eventually founder of multiple construction-focused businesses, Patterson developed a firsthand understanding of the labor challenges facing the skilled trades. After leading ventures such as Onis Vida and co-founding Bolster, he launched Skillit in 2021 to address the construction industry’s workforce shortage through technology. His unique combination of hands-on trade experience and startup leadership has positioned him as a prominent advocate for modernizing construction hiring and workforce development through data and AI-driven solutions.

Skillit is an AI-powered hiring platform focused on solving one of construction’s most pressing challenges: connecting employers with qualified skilled tradespeople. The company has built one of the largest networks of job-ready craft workers in the United States, spanning dozens of trades and hundreds of metropolitan areas. Rather than relying on traditional job boards, Skillit uses worker profiles, digital skill assessments, and AI-powered matching technology to help contractors source, evaluate, and hire workers more efficiently. The company’s long-term vision extends beyond recruiting, aiming to become the AI labor infrastructure for construction and eventually other physical industries by helping employers identify talent, train new workers, and scale the skilled workforce needed to support infrastructure, energy, manufacturing, and AI-driven economic growth.

You began your career as an apprentice carpenter in Scotland, worked as a journeyman carpenter, high-rise ironworker, and general contractor before founding multiple companies. How did those experiences on jobsites shape your decision to launch Skillit, and what problems did you repeatedly encounter that convinced you the construction industry needed a fundamentally new approach to hiring?

I experienced the problem from both sides. As an apprentice carpenter and later a journeyman, I spent a lot of time looking for work, driving long distances, and relying on word of mouth to find opportunities. Then as a general contractor, I had the opposite problem because I knew skilled workers existed, but finding the right person at the right time was insanely difficult. I was mostly struck by how little data existed about the workforce. We had detailed information about materials, equipment, schedules, and budgets, but almost nothing about the people actually building the project. My thinking was that if you can’t digitally represent a worker’s skills, experience, certifications, and preferences, you most definitely can’t make them discoverable and that realization led to Skillit.

And so we started by building a better way to represent skilled workers digitally by helping them make profiles to represent their skills, experience and job preferences. Over time, that evolved into a platform where data, automation, and AI help contractors source, screen, and connect with that talent.

Today, built on America’s largest network of vetted craft workers, Skillit’s AI helps leading builders like Mortenson, DPR, and Kiewit hire skilled craft talent at a level of speed, precision, and scale never before possible.​

Our mission is ultimately to scale the world’s craft. To use AI to make skilled workers more visible, more productive, and more valuable, ensuring the world has the talent needed to build everything from homes and hospitals to AI infrastructure, energy systems, and the industries of the future.

We think about the mission in three acts.

The first act is solving access to the millions of skilled workers who already exist. The second act is solving the shortage itself. Once you understand labor demand and supply in real time, you can do more than connect workers to jobs – you can use AI to help route more people into the trades, accelerate their development and increase how many become fully productive craft workers.

The third act is expansion. We believe construction is the toughest labor coordination problem in the world to crack and that solving it will give us a foundational superpower to service 100M employers worldwide across all physical industries around the world and so our third act will see us extend our system across all physical industries including manufacturing, utilities and even space.

Ultimately, we believe we’re building more than a marketplace or hiring platform but that we’re building the workforce infrastructure that construction has never had in the form of a system of record for labor, a system of action for hiring, and a system of intelligence for workforce planning.

Many people describe construction’s labor shortage as a talent shortage. You’ve argued that it is often an access and coordination problem. What is the distinction, and why does it matter when designing solutions for the industry?

Most people assume construction’s problem is a labor shortage because it’s genuinely hard to find enough qualified craft workers but it’s definitely more nuanced than that.

Construction actually has one of the smallest labor gaps of any major industry. There’s roughly 8.3 million people employed in construction (about 80% of them craft workers) and most data sources report we are missing around 400,000 workers. That means construction’s labor gap is roughly 5% of demand. Compare that to hospitality at 25%, manufacturing at 17%, logistics at 15%, nursing at 10%, software at 7%, and retail at 6%.

What’s really interesting is that the industry also reports some of the highest hiring pain of any major industry. 94% of construction employers say hiring is their number one problem – higher than nursing at 81%, logistics at 76%, retail at 74%, manufacturing at 60%, software at 50%, and hospitality at 48%.

So construction has the smallest labor gap and the highest level of pain which means clearly something else is going on. It can’t simply be a shortage of workers, and we believe its really a lack of access to the 95% of workers we already have. As an industry we’ve built sophisticated systems to track schedules, budgets, equipment, materials, and project schedules, but we never built the data layer or hiring infrastructure needed to efficiently connect employers with skilled craft workers across trades, regions, and labor types (e.g. open shop, union, local, per diem etc.).

And that’s why 1 in 20 workers missing feels more like 1 in 3.

The distinction between labor shortage and access matters because it changes the solution. If the problem is purely a shortage, the answer is simply to wait for more workers to enter the industry which is why traditional efforts to fix the problem focus on creating more awareness of the trades or trying to improve training. Those are akin to pouring water into a bucket that’s full of holes. They’re also rounding errors in terms of their impact or take too long to achieve results.

If the problem however is access, then better data, better matching, better coordination, and ultimately better infrastructure can unlock enormous value long before a single new worker enters the workforce.

AI is transforming white-collar recruiting, but construction has unique requirements around certifications, trade skills, jobsite experience, and worker availability. What does AI-powered hiring look like in the skilled trades, and where does it deliver the greatest value today?

The insight that led to Skillit was that before AI can help hire skilled workers, it first needs a way to understand them which is why we built the platform in layers. First, we focused on capturing workforce data. Craft workers don’t spend their days sitting behind desks with a résumé on hand (and if you’re anything like me you have fat fingers from doing years of manual work so typing into the wee screen on a mobile device is a derangedly bad user experience), so we leaned heavily into voice and automation. So today workers can create and maintain rich profiles simply by speaking, and our AI continuously updates those profiles as new information becomes available. For example when they interview with an employer on platform, our AI records the call and updates their profile with any new data.

Second, we built a system of agents that helps recruiters identify, engage, and connect with exactly the workers they’re looking for.

And third, all of that workforce and transaction data creates an intelligence layer that allows companies to query and understand construction labor market in real time.

I think the greatest value today comes from visibility, speed and precision. Our AI finally enables construction employers and recruiters to see the labor they need with pinpoint precision and spend less time searching and more time building relationships with the right people and ultimately delivering business outcomes.

Skillit is building what you describe as workforce infrastructure rather than simply another recruiting platform. How do you define workforce infrastructure, and why do you believe it will become as important to construction as project management or scheduling software?

Most labor companies seem to stop at being a marketplace. They help buyers and sellers find each other and that’s about it. The next step for us is becoming a platform, where our AI helps manage various hiring workflows, automates rote processes, and makes hiring more efficient and scalable.

Infrastructure is something a little different and becomes the system the industry depends on. The place where workforce data lives, where hiring happens, and where labor market intelligence is generated. We’re essentially building three systems simultaneously: a system of record for workforce data, a system of action powered by AI agents, and a system of intelligence that helps companies understand and plan labor markets in real time. When those systems come together, workforce management becomes far more strategic, predictive, and scalable.

I believe workforce infrastructure will become as, if not more, important to construction as project management software because every project begins with people. Before schedules, budgets, and materials matter, you need the skilled workforce to execute the work and frankly to generate any revenue whatsoever.

DPR Construction and Suffolk Technologies are investing directly in Skillit rather than simply becoming customers. What does that signal about how major builders are thinking about labor, workforce planning, and competitive advantage?

I think it signals a broader shift in how leading contractors view labor. For most construction companies, labor is now one of the most important strategic variables in the business. As builders take on larger, more complex projects and perform more work in-house, their ability to source, deploy, and retain skilled workers increasingly determines project outcomes.

Every dollar of construction revenue starts with a craft worker performing work in the field. Workforce planning depends on understanding where talent exists, where shortages are emerging, and how labor can be deployed most effectively.

The contractors that can do that faster and more accurately will clearly have a meaningful competitive advantage over the next decade.

Construction remains one of the least digitized industries in the world. What barriers still exist to broader AI adoption across workforce management, and how quickly do you expect those barriers to disappear?

The biggest barrier isn’t technology but data. Most AI systems are only as good as the information they can access, and construction historically hasn’t captured workforce data in a reliable or structured way. We know a tremendous amount about projects, materials, and equipment, but surprisingly little about the skills, experience, and availability of the people performing the work.

The good news is that this is changing quickly and AI is making it dramatically easier to capture and maintain workforce data through voice, automation, and everyday workflows. I think we’ll see workforce AI adoption accelerate significantly over the next three to five years as the underlying data layer improves and the labor market remains under pressure.

Your platform relies on verified worker data and skills assessments. How do you balance automation with the realities of evaluating craftsmanship, experience, and jobsite performance that often cannot be fully captured on a résumé?

Construction is ultimately a craft, and I don’t think craftsmanship can be fully reduced to a résumé or an algorithm. That’s why we’ve always believed the future is AI plus human judgment, not AI instead of human judgment. Every worker on Skillit is evaluated through

structured screening processes, and we combine that with assessments, employment history, customer feedback, and increasingly, signals from the broader network.

Over time, we envisage our platform moving toward a reputation system where workers, supervisors, project managers, and employers all contribute to a richer picture of performance. AI will of course help organize and analyze that information, but trust is still earned through the quality of work delivered in the field.

Data centers, semiconductor facilities, energy projects, and large-scale infrastructure developments are driving unprecedented demand for skilled trades. Which sectors do you believe will place the greatest pressure on labor markets over the next five years?

The biggest pressure will come from AI infrastructure, energy, and advanced manufacturing. Those projects are unique for both their scale and concentration with data centers, semiconductor facilities, power generation, transmission infrastructure, and advanced manufacturing all competing for many of the same highly skilled trades.

We’re entering what may be one of the largest physical build cycles in human history. The capital being deployed into AI infrastructure and energy alone (some $600B over the next decade from just four companies alone) could exceed entire generations of historic infrastructure investment on highways, roads and electrification combined. That demand will place extraordinary pressure on electricians, pipefitters, welders, HVAC technicians, operators, and many other critical trades.

Looking ahead, how do you see construction hiring evolving over the next decade?

Will we move toward a more dynamic labor marketplace where workers are continuously matched to projects, or will traditional hiring models continue to dominate?

I think hiring becomes dramatically more dynamic, predictive, and automated. Today, most construction hiring is reactive – a project needs labor, someone picks up the phone, and the search begins. Over time, AI will enable companies to forecast labor needs months or even years in advance, identify shortages before they happen, and automatically surface the right workers.

That doesn’t eliminate recruiters because construction is high-risk work and needs humans in the loop but it does make them more strategic. The best recruiters will be operating with the leverage of an entire team of AI agents behind them and the result will be a labor market that moves faster, creates better outcomes for workers, and allows contractors to take on larger and more ambitious projects with greater confidence, perhaps driven by where they now understand labor to be, rather than the other way round.

If Skillit succeeds in its long-term vision, what will the construction workforce ecosystem look like in 2035, and how could AI change the way millions of skilled workers discover opportunities, develop careers, and move between projects?

Our vision is unfolding in three acts. The first is solving access. We want every skilled worker to be visible and every employer to be able to find the talent they need, regardless of geography, trade, or labor source. The second is solving the shortage itself. By understanding labor demand at scale and using AI to guide workers into high-opportunity careers, we can help create more skilled tradespeople and accelerate their path to productivity. The third is expanding beyond construction into manufacturing, energy, utilities, and eventually every physical industry.

More broadly, I believe AI should make humans more essential, not more expendable. As a former carpenter and ironworker, I’ve spent my life around people whose skill and judgment create the physical world. Every bridge, hospital, factory, power plant, and data center exists because of human craftsmanship. Technology is at its best when it amplifies that capability rather than replacing it.

If we succeed, AI won’t replace skilled workers but instead can help the 1.4B of them around the world discover better opportunities, build more successful careers, and contribute to projects that would otherwise might be impossible.

Thank you for the great interview, readers who wish to learn more should visit Skillit.