AI is creating a new layer of human work behind the technology, from data capture and labeling to physical demonstrations for robotics. Companies are increasingly relying on globally distributed talent and managed workforce solutions to produce and verify training data at scale.

OVERLAND PARK, Kan., Nov. 10, 2026 /PRNewswire/ -- Artificial intelligence is creating a less visible category of human work: people hired to train technology. A December 2025 Oxford Economics report estimated the U.S. data-annotation market at $2.7 billion to $5 billion in 2024 and projected it could reach $10.3 billion to $19 billion by 2030. The industry directly supported nearly 200,000 flexible earning opportunities that year, illustrating the scale of human work already developing around AI training.

Bryan DiGiorgio, founder and CEO of 1840 & Company, a global staffing and business process outsourcing provider, said the company is already supporting AI-training projects. "We can talk about the here and now. We're scaling it," DiGiorgio said.

AI Training Moves From Labels to Physical Tasks

Some of the work is relatively simple but must be performed at scale. DiGiorgio pointed to autonomous vehicle training as an early example. Contributors may identify trees, stop signs, or traffic lights in images, so a system learns to distinguish objects it will encounter on the road.

The work is expanding as AI moves further into robotics and physical tasks. In egocentric video capture, contributors use head-mounted cameras or smartphones to record what they are doing from a first-person perspective. The recordings become training data that AI systems can analyze frame by frame to learn how people identify objects, move their hands, sequence actions, and complete a task. For robotics, those examples can help a model connect what it sees with the actions needed to handle an object or carry out a process.

Projects can capture activities such as folding clothes, making a bed, handling different objects, or performing commercial tasks. Some require standard phone cameras, while others use more advanced devices or sensors. "Think of it as the difference between reading a book on how to drywall and watching someone actually go through the subtle pieces of how you hang it," DiGiorgio said.

Creating Work Outside Traditional Payrolls

Some of the AI-training work DiGiorgio is encountering is organized around project-based contractors rather than permanent employees. Unlike traditional workforce models, these projects can require companies to coordinate specialized contributors across multiple countries, often for specific tasks or defined periods.

AI training can instead rely on large numbers of people completing data-capture and training tasks across countries.

That structure can make some of the activity less visible in payroll employment measures. The U.S. Bureau of Labor Statistics excludes self-employed workers from its establishment survey, which tracks nonfarm payroll employment, although its broader household survey captures some forms of self-employment.

"The data is being collected in a very different way," DiGiorgio said. "A good portion of this work is being done by independent contractors. There's this ballooning of jobs occurring right now that people aren't aware of."

From Supplying Workers to Delivering Verified Hours

DiGiorgio described two workforce models that 1840 supports AI-training projects. In a staffing arrangement, the company sources, contracts, and compensates contributors while the client manages the work. In a managed program, 1840 takes on greater responsibility for delivering an agreed amount of completed and verified data.

Those logistics can include finding businesses where commercial activities can be recorded, distributing cameras or other equipment, coordinating contributors, and dealing with hardware availability across locations. When equipment cannot be sourced locally and has to be brought in from another country, shipping and customs can add cost and complexity. "The phone itself may cost what you pay a person, or even more," DiGiorgio said. "There may not be enough devices in that geography. If you have to go to the main distributor, then you may start talking about customs."

Scale adds another layer. DiGiorgio said large projects can require millions of verified data-capture hours, with the work being divided among several suppliers. "None of them can really support the volume on their own," he said.

DiGiorgio sees the growing demand as an opportunity for workforce providers that can adapt as AI training expands. "AI systems are becoming more sophisticated," he said. "The ability to source, coordinate, and manage human contributors across global markets is becoming increasingly important to AI development."

About 1840 & Company

Bryan DiGiorgio is the Founder and CEO of 1840 & Company, a global staffing and business process outsourcing provider headquartered in Overland Park, Kansas.  Transitioning between industrial revolutions, the year 1840 inspired the company's focus on helping businesses excel between their different phases of growth. Today, he leads 1840 & Company in helping businesses build and manage flexible workforces through global staffing, direct hiring, and business process outsourcing. Powered by its proprietary WorkforceOS, 1840 & Company supports businesses across the workforce lifecycle, with capabilities spanning talent sourcing and hiring to workforce management and operations in more than 150 countries. For more information, visit 1840 & Company. 

References

• Grand View Research. (2026). Data annotation tools market size, share & trends analysis report, 2026–2033. grandviewresearch.com/industry-analysis/data-annotation-tools-market
• Grand View Research. (2026). Data collection and labeling market size, share & trends analysis report, 2025–2030. grandviewresearch.com/industry-analysis/data-collection-labeling-market
• Grand View Research. (2026). AI annotation market size, share & trends analysis report, 2025–2033. grandviewresearch.com/industry-analysis/ai-annotation-market-report
• Oxford Economics. (2025, December 15). The economic impact of the data annotation industry. oxfordeconomics.com/resource/the-economic-impact-of-the-data-annotation-industry/

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