As organizations race to incorporate AI into nearly every aspect of their business, HR has been laser‑focused on using it to optimize their full-time workforce. They’ve used it to find candidates for standard 8-to-5 jobs, compose training materials, write job descriptions, and review resumes from thousands of candidates per position. Yet, the extended workforce often slips through the cracks for AI use and data governance. That gap presents significant risks and missed opportunities as AI permeates every level of work.
What is the extended workforce, and who are they?
The extended workforce – composed of freelancers, contingent workers, gig staff, and vendor‑supplied talent – encompasses external or non‑employee talent working alongside internal teams. This sector has become massive in recent years, growing to 72.7 million U.S. workers in 2024 who are at least partially contracted out of about 170 million total U.S. workers (about 42%). That number has essentially doubled since 2020, when there were only 38.2 million total independents.
Of these independent contractors, 27.7 million are full-time independents (nearly 40%). 51% are millennials while 28% are Gen Z, as younger workers prioritize their independence and ability to shift location and profession as they desire.
It’s clear that organizations looking to make the most of the available workforce should be hiring contract workers. However, spending on professional services – which includes freelancers, contractors and gig workers – can start to balloon without careful consideration. In fact, professional services on average take up between 45% and 65% of total organizational non-employee spend.
Challenges in leveraging AI with the extended workforce
Obviously, cutting costs on hiring is a key HR imperative, and AI can help. Nearly half of organizations are already using AI in HR to reveal critical workforce patterns, take note of unrealized cost savings in salaries, and pivot negotiation tactics away from whatever may not be working.
But, when it comes to the extended workforce, HR still struggles to capitalize on AI in the same way they’ve been able to with full-time workers. That’s because workforce data is often siloed. With data spread across your finance platform, procurement system, and vendor management system (VMS) – which helps you manage your flexible workforce – it’s tough to aggregate it all in one place. AI-driven insights won’t be reliable if the AI tools you use don’t have access to the full scope of your data. Putting incorrect information or directions into AI agents could yield fallacies that can have serious consequences.
Think about it this way: When so much of the workforce is contingent, how can you, as an HR leader, ensure that you’re not introducing bias into your hiring practices? While it’s widely known that AI hiring tools may introduce bias regarding aspects such as race and gender, the automated screening tools you may already be using may also filter out candidates based on non-traditional work history – which often defines freelancers and contractors.
The best thing you can do is get all of your data in one place before introducing automation, which is crucial to time savings and staying competitive. That way, you won’t miss out on great candidates nor risk running afoul of the law, if someone finds they were discriminated against in your hiring process.
What can organizations do with AI and their extended workforce data?
Simply put, not leveraging AI on your extended data is a major missed opportunity – especially when that data has become so rich and the extended workforce is growing by the day. With AI, you could see which employment suppliers are over- or underperforming and adjust accordingly, majorly cutting down on professional services costs.
Given that so much of the younger workforce is choosing to go contingent either full- or part-time, you can make sure you’re getting fresh talent by incorporating the vast network of contractors into your AI-assisted hiring process. You wouldn’t want to miss out on someone who could contribute greatly to your organization or end up becoming the next great leader in your organization, with estimates as high as 35% for temporary to full-time conversion. That’s especially true when you consider that 75% of organizations struggle to find the right talent for their needs.
How can you aggregate your workforce data and apply AI to it?
First things first: put all of your data in one integrated system, including your contingent workforce data. This system ideally has AI features, so you don’t have to manually connect external tools or pull data out of your system and put it into an external AI platform that may expose your private data to competitors or threat actors.
From there, you can get to work on using AI to filter through candidates, resumes, and other data. Be aware that AI is only as good as the data and prompts you’re using. Think of it as an intern or assistant – you’ll need to tell your AI assistants exactly what to do.
Some specific examples of tasks you can give it are:
- I am hiring for [job title], what are 10 essential skills I should list in the job description?
- Suggest improvements for this job listing to make it more inclusive of [target audience].
- Summarize the following report for my executive team. Use bullet points to make it more readable.
A shift in mindset
While organizations may hesitate to engage contingent workers, doing so has benefits beyond expanding your available workforce. With more of the extended workforce counted in your analytics, you can reduce risks associated with bias in hiring practices. Deploying AI on your full workforce data – including contractors – can help you pull from a more diverse pool. That can be a win all around for lowering hiring costs, reducing your liability around hiring bias, and ensuring you’re getting the best candidates to fill every available position.
The AI revolution is about more than just improving efficiency for your full-time staff. It’s also here to help us rethink work itself – who does it, how it’s done, and how success is measured. HR teams who treat the extended workforce as a peripheral issue will find themselves at a strategic disadvantage as more and more of the younger workforce happily moves to contingent status. Making decisions based on partial insights can expose the business to unnecessary risk due to unintended bias and missing out on great talent that competitors will snap up.
The organizations that win in this new landscape and future-proof themselves against coming changes will be those that take a more integrated and intelligent approach to their workforce strategy. AI will undoubtedly transform how we manage people, but only if those people are all counted to begin with.
