Artificial Intelligence Streamlines Contingent Workforce Management Decision-Making

Originally published by Ardent Partners at

In today’s labor and economic climate, enterprises cannot afford to make poor hiring decisions. And with 47.5% of an organization’s workforce comprised of contingent workers, per Ardent Partners and Future of Work Exchange research, an extended worker hire is just as critical operationally as a permanent employee. The ramifications of a hiring mistake — whether it’s an extended or permanent role — can cost businesses 30 percent of the employee’s first-year earnings, according to the U.S. Department of Labor. However, artificial intelligence is now shaping the future of contingent workforce management (CWM) to help avoid those employment missteps.

CWM Optimization Through Artificial Intelligence

Through artificial intelligence, enterprises can harness the value of structured and unstructured data to streamline contingent workforce management decision-making. AI also opens the door to new user experiences to better attract, acquire, and retain top-performing talent and improve operational execution — all leading to cost savings. Using prescriptive analytics for CWM optimization is an evolving but critical piece of AI strategy. While artificial intelligence has existed for a decade or more, the wider scope of its capabilities is only now being utilized.

Subsets of AI, such as machine learning (ML), predictive analytics, and natural language processing, coupled with complementary technologies like augmented reality and the metaverse are game changers for contingent workforce management optimization.

Putting Artificial Intelligence To Work

Enterprises and HR executives who are not at least exploring the possibilities of AI’s impact on CWM will find themselves at a competitive disadvantage when sourcing talent and executing extended workforce strategies. Ardent Partners and Future of Work Exchange research cites that 80% of businesses expect AI to transform CWM in the year ahead. These are several ways that AI and associated technologies are getting the job done.

  • Enhance the candidate matching process. Enterprises are under pressure to not only attract and acquire the right candidates but do so in a short time-to-hire time-frame. The talent need is often immediate, leading to more costs as the vacancy persists. Enter artificial intelligence that can streamline the candidate screening process by matching critical role-specific skills with existing candidates in enterprise talent pipelines (e.g., direct sourcing, talent marketplaces, etc.). AI can narrow the field even further through questionnaires and even simulated exercises to test candidate skill proficiency — all while increasing hiring speed and attaining higher-quality candidates. With 74% of businesses planning to leverage AI to enhance the candidate experience (per Ardent Partners and FOWX research), it’s clear that the potential of the technology is being recognized. This is critical because it means enterprises can use data to understand how and why candidates are choosing our business or leaving/jetting for other companies. It also exposes gaps in the hiring process that must be remedied to enable real-time hiring capabilities. The war for talent is raging…having a process that essentially finds those talent needles in the haystack is the competitive differentiator.
  • Expand overall total workforce visibility. Much of the value attained by artificial intelligence is more efficient identification, organization, and utilization of data. Prescriptive analytics, for example, provides the optimal use of collected data. When evaluating the total workforce holistically, enterprises need insights into their full-time and contingent employees. What are their skillsets? Which department do they work in? How long have they been contracted with the enterprise? What is their past project or team participation. Answering these questions creates a strategic profile for every full-time and contingent employee. Those total workforce profiles make real-time hiring and seamless succession planning a reality. Transparency into both operational challenges and available talent is a dual threat to lagging competitors.
  • Leverage predictive analytics and scenario planning. Ultimately, organizations want the ability to use data to predict future scenarios and potential outcomes. As a subset of artificial intelligence, predictive analytics is used in a variety of operational settings, particularly for supply chain planning. However, it is just as valuable for contingent workforce management as a predictor of future talent needs. Predictive analytics takes prescriptive analytics and workforce profiles a step further by combining operational and profile data to identify talent deficiencies and operational weaknesses, while also projecting how talent should be utilized to close those gaps. This is transformative for large-scale enterprises with tens of thousands of employees across the globe. It can also be talent-defining in scenarios where succession planning comes into play. So much of the hiring focus is on the “immediate need” rather than the gaps silently forming with aging workers eyeing their next opportunity post-retirement. Predictive analytics can address workforce scalability related to resignations, retirements, labor movements, etc., and how those will shape the workforce short and long term. In the case of a recession or other economic crisis where scalability becomes an essential strategy, enterprises can leverage internal talent data and combine it with market and labor insights to more effectively understand how operations will be affected. Which skills are required immediately versus long-term CWM planning? The ability to scale the workforce quickly and efficiently cannot be understated.

AI Becomes A Permanent Fixture For Talent Strategy

Artificial intelligence is becoming a permanent fixture as part of today’s enterprise operations and talent management strategies. For the contingent workforce, AI serves as an essential technology to streamline candidate pairings with operational needs, while increasing transparency of available skillsets and workforce contributions. Those insights prove valuable when talent gaps appear, or workforce scaling is necessary. Artificial intelligence will continue to evolve and with it, more CWM opportunities will emerge. Today, leverage the AI capabilities that exist to better plan for tomorrow.

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