Contingent workforce management, especially for highly skilled or expert talent, is continually evolving to be a strategic element of the total workforce management. Due to fast paced changes in required skills coupled with supply demand challenges, especially in technology sector (e.g.IT/ITES), contingent workforce is increasingly becoming very complex to maneuver. Naturally, one would expect a very high level of technological influence driving productivity in this area of services procurement.
Interestingly however, organizations around the world still appear to struggle with basic issues – matching the best candidate for the job and time-to-fill the position. Apart from the classic vendor management systems (VMS) such as Fieldglass, IQN etc., we do not yet see any significant influence of the latest technological trends in this area. VMS tools along with MSP (managed service provider) have largely been in use for tracking, monitoring controls areas (procure-to-pay) and primarily focus on assurance of outcome as against means to achieve the outcome. Compare this with other supply chain categories – production, travel, HR etc., where we find a great deal of disruptive technologies and solutions that are focused on the means to achieving business goals, for example – higher productivity, efficiency etc.
Part of the issue here is that in most organizations, the contingent workforce is generally treated as one – “the contingent”, largely transactional compared to the permanent workforce. However, this could change in coming years as the total talent management (TTM) concept is getting more traction. The focus of contingent workforce (CW/S) procurement is now evolving – from traditional workforce/labor staffing focus to SOW services, from VMS to more integrated digital solutions based on next-gen technologies – Data predictive analysis, AI based learning systems, Robots-a-Service etc.
While, there is no doubt that the industry will eventually source and manage all human capital and services in a highly integrated digital way, question remains as to how evolving technologies will drive and enable those processes and what the end state will look like.
Although specific functionality may vary slightly based on industry and related use cases, the promise of AI in the contingent workforce space is similar across the board –
Interestingly however, organizations around the world still appear to struggle with basic issues – matching the best candidate for the job and time-to-fill the position. Apart from the classic vendor management systems (VMS) such as Fieldglass, IQN etc., we do not yet see any significant influence of the latest technological trends in this area. VMS tools along with MSP (managed service provider) have largely been in use for tracking, monitoring controls areas (procure-to-pay) and primarily focus on assurance of outcome as against means to achieve the outcome. Compare this with other supply chain categories – production, travel, HR etc., where we find a great deal of disruptive technologies and solutions that are focused on the means to achieving business goals, for example – higher productivity, efficiency etc.
Part of the issue here is that in most organizations, the contingent workforce is generally treated as one – “the contingent”, largely transactional compared to the permanent workforce. However, this could change in coming years as the total talent management (TTM) concept is getting more traction. The focus of contingent workforce (CW/S) procurement is now evolving – from traditional workforce/labor staffing focus to SOW services, from VMS to more integrated digital solutions based on next-gen technologies – Data predictive analysis, AI based learning systems, Robots-a-Service etc.
While, there is no doubt that the industry will eventually source and manage all human capital and services in a highly integrated digital way, question remains as to how evolving technologies will drive and enable those processes and what the end state will look like.
Although specific functionality may vary slightly based on industry and related use cases, the promise of AI in the contingent workforce space is similar across the board –
- First, it will enable organizations to make better hiring decisions.
- Second, it will allow businesses to more efficiently manage their contingent workforce.
- Finally, it will enhance the user experience.
Following five areas stand out as having interesting applications specifically to contingent workforce management –
Adaptive Job Assessment – One of the VMS’ most critical function is to assist hiring managers in finding the best candidates to fill job openings. AI can help take this matching to the next level, using machine learning coupled with adaptive online assessments and games to predict job performance for specific roles. In this way, AI could potentially enable hiring managers to make wiser decisions that would decrease time to fill, maximize talent and reduce cost.
Unilever has been experimenting with this concept for their fresh hire for over 2 years now. For example, imagine a scenario – a candidate would fill out a job application, play a set of online games and submit video of herself responding to questions about how she would deal with challenges of the job and only after several such rounds would encounter the first human in the process based on recommendations of the algorithm.
Social and Behavioral Data Mining – Today, there’s a wealth of social and behavioral data available over the web, social media networks and more. However, it’s been difficult for organizations to harness this unstructured data. Advances in cognitive computing and deeper machine learning are starting to enable businesses to put this data into action. One of AI’s biggest strengths lies in its ability to sift through millions of unstructured data points and make sense of them quickly. Like how individual customer behavior profiling is done today, enterprises could leverage the functionality to mine social and behavioral data and use it to better match job candidates to open positions and reduce time-to-fill.
Predictive Analytics – Another possibility of AI big-data combine is to more accurately predict the trends in the skills needed by the marketplace. Today, capacity planning around contingent workforce is a major challenge across the industry, both for user enterprises as well as the suppliers. This mismatch of skills and capacity is a major cause of long time-to-fill and consequently significant loss of productivity. Advances in predictive analytics will help eliminate non-productive gaps and enhance overall productive capacity in the industry.
Embedded Intelligence – The best VMS platforms make users’ workflows easier, simpler and more efficient. The next frontier in this area is AI-powered embedded intelligence — a form of virtual assistant that would help users with tasks throughout the contingent workforce management life cycle. Business processes, managerial preferences and company policies could be built into the system to help the virtual assistant “bubble up” more relevant suggestions. Advances in this type of functionality will drive user engagement and deliver a better customer experience.
Augmented Reality – What if you could create a virtual office? With augmented reality, this scenario can become real. With contingent workforce managers and workers alike increasingly on the go, the ability to leverage augmented reality to bring a company’s physical locations or workspaces to life has exciting potential to improve efficiency. This is more so, since future workforce will likely not want to confine itself to a specific work location, type of office space etc.
In conclusion, the increased integration of AI into existing technologies is poised to unlock a wealth of opportunities for companies. Combined with human expertise, this augmented intelligence will help businesses drive cost savings, increase talent quality and improve the effectiveness of their contingent workforce programs multi-fold.
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