The labor market is one of the most powerful forces in the economy; however, it is often undervalued and ignored.
Ben Zweig, a labor economist, New York University professor and former IBM data scientist, is the CEO of Revelio Labs, a company specializing in labor force market data and workforce intelligence.
In his new release, Job Architecture: Building a Language for Workforce Intelligence, Zweig highlights an overlooked market gap: The labor market holds insightful data and has the potential to save companies from poor hiring practices and unnecessary expenses, but it is chaotic.
“Labor markets are twice as big as capital markets,” Zweig explains. “And, more important, they’re the biggest, most consequential [markets] in the world. This is what everyone does with their day. And they’re so inefficient.” So how does one compile and standardize all the labor data in one place? In a public domain that’s accessible to everyone. Which is exactly how Revelio Labs was born.
Similar to financial markets, data categorization is heavily prioritized, although labor data is highly unorganized. Zweig says taking raw data and categorizing it utilizes large language models, machine learning and AI to lay it all out. Once it’s produced, the potential is incredible: It can detail workforce stats, hiring and firing periods, and more. The database is capable of streamlining data that informs insight and provides answers for strategic business practices.
Applying Data Across the Workforce
Zweig views labor data as a foundation for several logistical questions for companies, rather than a problem-solving tool. For example, he says in a traditional startup, companies view their target goal as being “a vitamin” or “a painkiller,” and Revelio Labs is neither.
“There’s more money to be made in painkillers; if you solve some pain, you’re more in demand. Whereas vitamins are more of a nice-to-have,” Zweig explains. “And I think we are kind of unique in that way where we’re not really solving a key problem that people have in their job. We have just found a way to create value through shared information.”
The information Revelio Labs collects is invaluable for standardizing workforce information. “It was really kind of a solution in search of a problem,” he says. “And we found lots of little problems.”
Data Means Being People-First
Many larger corporations collaborate with Revelio Labs to make sense of their human capital lineup. Revelio Labs analyzes first-party data and the corporation’s workforce, benchmarking it against peer companies, to inform long-term planning and organizational design for sustainable growth.
One major focus of theirs is helping companies organize employee structure, especially with high turnover and frequent hiring cycles—an area where many struggle.
“Very often, one issue that we see a lot with companies is that they’re usually very good at capturing data from their formal W-2 workforce. But sometimes the majority of their company is actually classified as a contingent worker,” Zweig says. That means the bulk of their crucial data is missing.
This exposes another challenge: a lack of retention strategy. Companies face a revolving cycle of resignations and hiring without properly tracking skills and work activities, requiring comprehensive analysis to reveal patterns and break down expenditure and workforce dynamics.
With a people-first mindset, company restructuring after a thorough review of its data can serve as a brilliant foundation. Zweig says this is good data for planning human-centric strategies, and starting with their labor data is a prerequisite for boosting employee engagement and retention.
Job Taxonomies
According to Zweig, job taxonomies are believed to be ideal for better workforce planning and adaptability. And his approach isn’t just for a short-term foundation; it’s for longevity.
Job taxonomies serve as a dictionary for every job in the world. He says when hiring, HR should post high-level job descriptions, rather than a broad category like sales or engineering. This way, skills have a specification, and instead of 20 hires, you might only need five to get the job done.
“Instead of thinking about occupations as... clusters of jobs, maybe it starts to make sense to break down jobs into their component parts,” Zweig says. “When you’re hired to do a job, you’re not hired to do one thing—you’re hired to do a dozen things.”
One job consists of multiple tasks, and rearranging tasks and activities among other employees or AI can align skills more precisely with priorities. “The job architecture is really a collection of taxonomies, patience, skills and, increasingly, work activities and sometimes seniority levels in that as well. But it’s really about how they categorize people,” Zweig adds, proving hiring practices can be refined through job taxonomies and create a common workforce language that enhances long-term organizational intelligence.
Rethinking the Organizational Chart
Taxonomies aren’t only great for transforming hiring methods; they’re also a way to help companies reshape their org chart. Zweig says traditional companies are set up like a pyramid, with more workers at the bottom and several levels in between. Then, each level has a management team.
“If we think about what’s the right kind of hierarchical structure? What’s the right ratio? Do you want to be [a] more flat organization or do you want to be [a] more hierarchical organization?” Zweig asks.
Hierarchy has more procedures and a rigid structure. Flat organizations encourage individual workers to contribute to the company with autonomy. For both, employees seek change and growth, requiring intentional job design and flexible management.
“I think we should think about management as having the job of essentially reconfiguring work,” Zweig says. “Managers really should be in the business of helping their employees—change around the composition of their job, change around the tasks that they need to do to meet the needs of the organization.”
However, the answers aren’t simple. Managers report to high management and leave little room for negotiation and adjustment. Reallocation needs are restricted and need advancement, requiring changes that are not always easy or quick.
Augmenting the Labor Force
Looking ahead, Zweig envisions Revelio Labs as a global public domain terminal, accessible to all industries and designed to assist in workforce decision-making. The company has even released public labor statistics during periods when government data was delayed.
“That’s not something we’re doing for commercial value, but it’s just something that we think should exist in the world. Unbiased, trustworthy, apolitical labor market information,” Zweig says.
Job architecture is evolving, and it serves as a reminder that in the realm of technological advancement, data represents people. It holds the power to shape how, why and where we work—a testament that human innovation remains one step ahead of automation.
Featured image by Andrii Yalanskyi/Shutterstock
This article was first published in the March/April 2026 issue of SUCCESS Magazine. Get your copy here.
