Machine Learning and Artificial Intelligence are Driving Recruitment Innovation

As demand for faster and more efficient talent acquisition, recruitment and onboarding processes continues to increase, the drive to employ existing and emerging artificial intelligence (AI) and machine learning (ML) solutions will follow. Strategic use of AI and ML can help clients identify suitable talent quicker, optimize communication processes for candidates, and onboard more efficiently, freeing up recruitment staff to focus on the human dimension.

Growing Use and Demand for AI

Speaking about the growing demand for AI in recruitment, Mark Brandau, principal analyst at Forrester’s CIO team told ZDNet that, “all vendors are moving in that direction, without question. It’s the way of the future.” 

Even a small-scale target deployment of automation using AI and ML can vastly improve these processes and generate usable data that companies can employ to enhance their recruitment and retention strategies. “What’s interesting about AI is the amount of data that can be generated that can then be used to enhance the recruitment approach,” says Hector Cerezo, VP of Operations and Talent Acquisition at Framework Science.

AI and ML can allow the processing of large volumes of applications, scheduling of interviews, and even, as in the case of Framework Science, the communication of feedback post-interview

Data about both clients and candidates can be analyzed using ML, making matching more precise and speeding up the process so that time to hire decreases. This is now a critical differentiator for businesses. 

Cerezo explains that cutting time to hire to 49 days has had a significant positive impact on client and candidate experience. It means that the right talent isn’t snapped up by a competitor while clients are waiting on the usual, often slower, processes to complete – and those not selected receive timely feedback and can move onto other opportunities. It’s a win-win.

AI and ML can allow the processing of large volumes of applications, scheduling of interviews, and even, as in the case of Framework Science, the communication of feedback post-interview. There are manual tasks that can be removed from staff’s workload so they can focus on where they are needed.

Human Touch Remains Crucial

It also increases transparency for all, so that clients have access to the data they need when they need it, and candidates are also able to track their journey through the system. A dashboard provides easy access across a range of data points so that metrics can be tracked, and richer data can be captured and shared. This also provides an interface for clients to provide feedback that in turn becomes a data point for future recruiting activities.

While there is still much to be done to extend AI and ML use and scale up the deployment, it is set to revolutionize the business of recruitment and onboarding, and simplify outsourcing processes across the Nearshore. It is not about replacing hiring managers and recruitment staff, but rather about deploying those resources to the places where they can have the greatest positive impact, improving efficiency and still allowing for the human touch.

The human factor remains a crucial central component of talent acquisition and management, but enhancing it with the right AI and ML solutions allows for greater impact across all facets of the workflow.