Overcoming Labor Challenges by Applying AI to Extended Workforce Data

Today’s labor market demands flexibility. Unemployment is low, workers want more from their full-time roles, and businesses struggle to find qualified candidates who fit their needs. One of the ways for employers to overcome this challenge is to tap into the extended workforce, consisting of freelancers, contingent workers, gig staff, and vendor‑supplied talent. But oftentimes, this data lives across various systems, making it difficult to manage alongside the rest of your employment data.

Added to this is the complication of AI. On one hand, applying AI to your workforce data can help you stay competitive in the HR space by filling roles faster and creating a better employee experience. But this inability to aggregate all of your workforce data – including contingent workers – in one place makes AI initiatives around HR much more difficult to accomplish.

As pressure mounts to fill roles quickly while keeping costs down, it’s worth exploring how organizations can adequately prepare their workforce data so AI can help them fill roles with the right workers from both the full-time and extended workforces.

The Current Labor Market

It’s a particularly challenging time for companies to find staffing right now in part because of historically low unemployment. As of June 2025, it was at just 4.1%, continuing the trend set by the past few years. With most workers already in roles, there are just fewer people to choose from – about three-quarters of organizations (74%) report struggling to find skilled workers to fill their roles.

What’s interesting is that there are plenty of workers out there, but many employees are now contractors who aren’t necessarily looking for full-time work. The U.S. extended workforce was up to 72.7 million out of about 170 million total U.S. workers (about 42%). A lot of these independent contractors are full-time independent (almost 40%), while nearly 80% are either millennials or Gen Z.

Why contingent workers are being underutilized

It’s clear that contingent workers are now a crucial part of the overall workforce and should be considered carefully alongside full-time counterparts. But there’s a disconnect between systems that is leading organizations to underutilize them, especially when AI initiatives around HR don’t include contractors at all.

For AI to fully count your available workforce, with both contractors and full-timers included, you need to include data from your vendor management system (VMS), plus finance and procurement systems. Aggregating all of this data in one VMS and labeling it in the same way helps your AI tools get the full picture of who is available to fill any open roles you may have.

What AI Can Help You Do With Extended Workforce Data

Combining your workforce data for AI initiatives can obviously help you fill roles with fresh talent. But it can also help you reduce bias against candidates with non-traditional work histories (which describes many contingent workers out there). That’s crucial for avoiding any appearance of discrimination in your hiring practices.

While many organizations may hesitate to invest in new vendor management systems or AI initiatives around HR due to worries over costs, it can actually help rein in overspending on contractors. With professional services (inclusive of contract workers) taking up between 45% and 65% of organizations’ total non-employee spend, that’s a cost that can quickly spin out of control when not properly managed. Using AI on your workforce data can help you more easily see which contractors or organizations you may be overspending on and adjust accordingly.

Ultimately, leveraging AI on the extended workforce is an investment that can pay off in the form of fewer vacant positions, greater efficiency in day-to-day HR activities, better compliance with anti-bias regulations, and cost savings around optimizing usage of contingent workers.

How Can You Implement AI on Your Collected Workforce Data

Once you have your data collected, it’s time to go to work. AI tools can screen applicants, review resumes, and analyze other relevant information in your database. Done correctly, AI can sift through both full-time and extended workforce data much faster than human beings, helping you draw actionable insights much more quickly.

Just remember that your output depends on the quality of data you put into it and how clear you are with your instructions. It requires patience and training, as AI should be thought of as an “intern” for workforce planning and management. You’ll need to be very specific about your asks and give it business context for what you’re doing. For example:

• I’m hiring for [job title]. List 10 key skills that should be included in the job posting.
• Recommend changes to this job ad to make it more welcoming to [target audience].
• Analyze the market rate for this role in this location.

With these insights, you can quickly begin posting jobs in multiple markets, speaking with candidates, and negotiating salaries – all while saving time, money, and compliance risk.

Things to Remember

When it comes to using AI for your extended workforce, you shouldn’t have to spend more money to acquire this data – it’s all about making the most of the data you already have. You likely have a healthy roster of contingent workers made up of current contractors and former employees, for instance. You just might not be making the most of it to satisfy your current job needs.

Compliance is key, as AI can help you reduce bias. However, AI may introduce bias of its own, which is why it’s important to include a human in the loop to vet results. It’s also important to securely connect your workforce data to an internal tool that won’t unwittingly expose information to the outside world, as public AI tools may share the data that’s shared with them.

A private version of popular AI tools like ChatGPT or Microsoft Copilot can help alleviate this risk, but there’s also the issue of actually connecting that data. A VMS that already has AI built into it can maximize your cost and time savings around aggregating your contingent and full-time workforce data and pulling it into the tools that can deliver on the promise of AI.

Ultimately, the goal of AI is to augment your efforts, not replace the human element you bring to your work. Reducing inefficiencies and errors is the goal. A strong AI policy, careful data integration, and proper training can help you use AI to save money, hire the right candidates, and find full-time and contingent workers to fill gaps in your workforce. All of this gives you more time back to focus on strategic priorities and do the work that’s most meaningful to you.

Without AI, there’s no way to keep up with competition that’s already using it to fill roles in a difficult market for employers. Time is of the essence – the sooner you start, the sooner your skill and employment gaps will be a thing of the past.