Advanced technology is rapidly transforming the way businesses used to work. In this competitive environment, where organizations are striving hard to be a preferred choice, adopting digital transformation to automate, streamline and make the informed data-driven decision is imperative. With the availability of huge data and sources to store and process data, it is now possible to use data insights for multiple applications. Artificial Intelligence has acted as a lever to catapult the use of Big Data by helping organizations in predicting future outcomes.AI emphasizes building smart machines which nearly exhibit human-level intelligence while making a decision. Machine Learning is a subset of AI which collects, uses, and analyzes data to identify the patterns through learning algorithms on similar data sets and derive insights that can be used to take actions.ML does not take action itself, when its data insights are used to make decisions without human intervention, is when AI comes into the picture.

For those who are unfamiliar, the concept of the technology adoption curve was popularized by Everett Rogers in his book Diffusion of Innovations. Although some HR tech writers have joked that the overall HR technology adoption curve looks markedly different, with a majority of HR tech buyers disproportionately disposed to the far left of the curve, the true speed of adoption of AI in talent acquisition tracks closely to the standard curve. Innovators and early adopters are exposed to the greatest amount of risk from adopting new often unproven technologies. However, due to the considerable competitive advantages that may be realized by implementing breakthrough technology, they also stand to benefit the most. While the innovators and adopters have a greater appetite for and ability to manage risk, for the majority of companies shooting to be part of the early majority is the most strategically advantageous way to implement new technology.

There is a huge data-driven advantage available for staffing firms that successfully integrate AI into their work environments. The technology's ability to analyze big data about your firm and make suggestions based on the analysis is a major part of AI's use in Staffing. With AI analysis, firms can score talent based on past history, such as length of time in their jobs or career path, which can then be used to make better and more informed decisions in the future. This kind of analysis can show a company data that includes employee attrition or failure rates and the likelihood of them getting a certain type of job, or staying with it. Past contractor history and other contextual data can be mined by AI and used as a valuable source of knowledge for staffing firms.

Few assets impact a business as much as its staff, yet few tasks are as dreaded by most managers as recruiting and hiring new employees. That intersection of high value and high cost is where the staffing industry fits. It's also where artificial intelligence may be most disruptive. In Westchester, personnel firms range from branches of multinational behemoths to one-person boutiques that specialize not just in recruiting for a given industry but for certain positions in that industry. Typically, full-service firms divide their operations into three areas: temp labor, temp-to-permanent hires, and recruitment for permanent positions.

You can read the full interview about the future of recruitment and how AI and Machine Learning will have an impact on the HR industry below in Textkernel's blog post. There's been a lot of talk about AI and Machine Learning in the staffing and recruiting industry, but let's define the problem space. If you think about the entire lifecycle of the recruiting process, in the next 5-10 years where will machines really make a difference and where will humans continue to be able to add value? Will recruiters have jobs in 2025? To me, it is without a doubt by now, that AI and Machine Learning will have a huge impact on all areas of business.