Today’s workforce is rapidly changing and oriented towards mobile, flexible, and short-term project-based work. Millennials and Gig economy find work-life balance, work satisfaction more important than 9-5 jobs and permanent employment. An Intuit Global Study predicts that 40% of the total workforce will be contingent workers by 2020.
This new workforce landscape has forced organizations to become more agile and “boundary-less”. This has also raised a lot of challenges for organizations. How to sift through and identify the best talent from the multichannel talent pool? How to ensure that the right talent is working in the right place and at the right time? Are these workers performing well enough or they are turning in overheads? In addition to that, identifying the right blend of permanent and contingent workforce, monitoring them and their performance, and being compliant with changing laws and regulations becomes very difficult.
We have already heard of Managed Service Providers (MSP), Vendor Management System (VMS) and Freelancer Management System (FMS) solutions and their transformation in recent years. With the continuous need for improvement and a competitive edge, companies are demanding more sophisticated solutions to manage their talent pools, and make processes efficient and effective. With emerging Artificial Intelligence (AI) and Machine Learning (ML) trends, organizations have more choices to overcome the challenges of managing contingent workers and streamline their workforce management.
Let’s take a look at some of the key challenges where AI can be helpful:
1. Forecasting the demand and supply
Many times organizations fail to predict the accurate requirements for future contingent workers and the right time for hiring them. This can adversely impact the performance and cost of the project or task.
In order to overcome this, organizations can leverage AI-based solutions which can utilize inputs from multiple data points and historical data. Such AI-based tools and algorithms can predict the talent demand, assess the internal and external talent supply and define the inbound and outbound sourcing strategies for workforce planning. Here, data points can be captured from an organization’s internal talent pool, sourcing channels, external talent profiles, experience and skills required, and so on.
2. Multichannel sourcing and candidate matching
Due to global workplace culture, a myriad of sourcing channels and tough competition for talent, it becomes challenging for any organization to get the best talent when required.
Organizations have already started optimizing sourcing by pulling data from multiple sourcing channels, social media, professional platforms, freelancers’ job boards, and talent pools. Further, an AI-powered system can find the best candidate based on specified criteria such as availability, skills and experience, performance ratings, and more. An algorithm can match the talent profiles against job requirements and rank eligible workers based on their suitability score. This can reduce the time to fulfill the talent requirement.
Nowadays, organizations also prefer direct sourcing or self-sourcing instead of depending upon third-party staffing agencies and external sources. AI can also help in creating an internal talent pool and managing it.
3. Decision making
To leverage your contingent workforce most effectively, organizations must first ensure that the right workers, with the appropriate skills, are doing the right jobs at the most competitive costs. Scheduling, quality, and performance play a major role in this. Let’s take a look at applications of AI for managing the same.
Scheduling
AI-based algorithms can leverage a large amount of structured and unstructured data available with the organizations and identify the scheduling, project or task allocation as per the skills and performance and compensation for contingent workers. This will help in aligning with changing regulations and creating a fair work environment.
Quality and performance
Every organization is trying to find a way to control the quality, performance, and cost of their contingent workforce. Imagine if there are any reports which can showcase all these details and predict the future outcomes w.r.t. cost, performance as well as the quality of the entire workforce. Definitely, that would be a dream come true for any organization and that’s exactly what AI is here for! With such comprehensive reports, organizations can define strategies for effective workforce planning and performance optimization.
4. Compliance and risk
Contingent workforce landscape needs to be continuously monitored for changing regulations for different geographies, work types and roles, etc. We have heard about incidences where companies faced lawsuits and penalties due to deviation from regulatory measures. AI can screen through multiple channels of regulations and then identify potential risk for contingent workers.
AI can help organizations to identify deviations from the benchmarks or rules and raise an alarm before getting into penalties and lawsuits. For example, if there are any regulations for minimum wages and minimum contingent workers in a specific region then after crossing that benchmark, AI-based systems can identify the risk and recommend suitable mitigation plan. Organizations can also avoid worker misclassification issues using such intelligent systems.
5. Total talent management
Longtime organizations are willing to get more visibility and control over the contingent workforce management, akin to full-time employees. You might have heard about “Total talent management or Blended workforce”. What exactly is it? In simple words, it’s nothing but getting a holistic picture of the entire workplace including full-time employees as well as contingent workers, which again includes freelancers, temp workers, consultants, and so on. In such a holistic picture, we also need to consider the involvement of other systems such as time and attendance tracking, payroll and compensation, training, performance, and so on.
AI can be beneficial in providing the useful insights for this total talent management. Such systems can define the workflow, processing rules, effective completion time, and cost associated with each step and easily streamline the entire process of managing a diverse workforce. AI can be helpful in identifying the requirements, available skills and defining the ideal blend of permanent and contingent workers.
Apart from the above areas, AI can also help in onboarding and collaboration, learning and development, feedback and rewards, and so on. AI and ML-based chatbots can act as a personalized assistant to candidates as well as employers.
The International Data Corporation (IDC) recently forecasted that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021. With rapid technology adoption, we can expect organizations to embark on new opportunities for AI in contingent workforce management in the upcoming years! Does this sound relevant or aligns with your product roadmap? Please connect with us on contact@harbingergroup.comand we would like to exchange an idea or two and explore collaboration opportunities.
