Artificial Intelligence for Contingent Labor: Benefits and Challenges

Artificial intelligence (AI) can have a significant impact on companies that use independent contractor talent—from finding and sourcing contractors to engaging and onboarding them. AI tools can effectively source prospective independents, assess them without bias, and onboard them efficiently. With more companies drawing on contingent labor to build optimized or blended workforces, AI tools can be beneficial in locating in-demand skill sets and high-value talent.

Before company leadership adopts AI, it is important to work cross-functionally to understand where and how AI might give the business an edge. It is also helpful to consider how the company will address challenges that may arise in applying AI to contingent labor.

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How AI Can Help Your Contingent Labor Program

To leverage AI most effectively in your contingent labor workforce program, consider what areas can generate the biggest benefits. Below are examples of where AI might be of value within an organization. Keep in mind that your particular areas of value will depend on the size of your business and your particular industry.

Fine-tune role and project descriptions

What keywords in a role description will attract responses from the most qualified independent contractors? What language can unintentionally signal gender or age preference? AI can review linguistic choices and suggest alternatives based on its algorithms. AI can also “score” your project description to indicate how effective it will be in attracting the right independent professionals. It can indicate rate ranges for a given project or role based on the data it learns from.

Directly source independent talent

AI-based software can scan sites like LinkedIn to identify potential candidates who are independent contractors. The software can go further by sending emails or texts to the individuals it has identified.

Learn more about direct sourcing

Acquire unbiased candidates for consideration

Video interviews of contractor candidates can help eliminate some decision bias through a standardized experience where candidates answer the same set of questions. This way, each interviewee has an equal opportunity to demonstrate their expertise and talent. AI tools can take this concept a step further, eliminating additional potential bias by applying algorithms that mine data about nonverbal communication like facial expressions and body language along with candidate responses.

The tool could then build a report that ties this data to an assessment of how well each candidate fits the company and the project. By configuring the algorithms to ignore data like race, age, or gender, you could improve the likelihood of getting the right person in the right role and contribute to increased diversity.

Individualize onboarding

AI can create customized onboarding paths for contractors who are new to the enterprise based on the role they’ve been engaged for. This can help them get up to speed quickly and more productive sooner.

Unlock more value in your talent network

Your talent network could also incorporate AI tools to help hiring managers re-engage the right talent for a particular role. AI could also be used as a relationship management tool. For example, it could offer each member of your network professional development suggestions that would enhance their value to the enterprise and help grow their independent business.

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Analyze project proposals

Once you have received proposals for the project, AI can analyze them using natural language processing and machine learning to screen contextually. Such tools can also rank candidates to help with shortlisting.

Anticipating AI Challenges

AI tools are only as good as the objectives they are optimized for. Therefore, leveraging AI in a contingent labor workforce program requires careful consideration about where it would be most valuable and what implementation challenges may be encountered.

For example, depending on company culture, people may not trust AI-driven decisions. This concept is known as “algorithmic aversion” and can be mitigated through training and communication about AI tools and how to interact with them.

Another point to consider is even though AI can reduce bias in decision-making, it’s only as good as the datasets it is trained with. To address this concern, be sure to create processes and standards to set the criteria used by the AI.

Though AI regulation in the US is in its very early stages, there may also be legal risks associated with such tools. In addition to keeping track of developments in AI itself, it’s important to monitor the regulatory space to make sure any initiatives take place with appropriate risk management measures.

Staying up to date on developments in the AI arena can be beneficial throughout an enterprise. Given the growing role that an optimized workforce plays in competitive advantage, it is worth putting AI for contingent labor on the strategic radar. While AI tools may be deployed elsewhere in the organization first, the challenges and lessons learned in those initiatives can help when the time comes to apply it to independent talent.