The US$200bn global recruitment industry has reached an inflection point – traditional methods of recruitment are being gradually replaced by technology-driven processes and business models. While adoption of technology is playing a crucial part in this transformation, it is also critical to acknowledge another important business driver – the chronic shortage of skills, particularly in niche technology roles. This is negatively impacting business plans, productivity, staff turnover, employee satisfaction and of course, profits.
In a tight market for skills such as Machine Learning, Natural Language Processing, robotics, front-end development, data science etc., some organizations have set unrealistic goals for themselves. Job descriptions tend to cover wider skill areas which do not match the availability of talent. Not many organizations have mapped the jobs available with them with the talent available in the region. In the prevailing market condition, organizations which are able to bring about a balance between ‘talent desired’ and ‘talent needed’ are more successful in ensuring business uptime. To attract and on-board top talent having multiple in-hand offers, flexibility scores over rigidity.
AI has arrived in the global recruitment industry – global trends
According to an estimate by CB Insights, more than US$6bn have been invested into more than 1,000 AI start-ups globally in the past 3 years . AI tools are expected to create value to the tune of US$3tn by 2021.
Bersin (Deloitte) has identified 6 important factors which define the behavioral contours of a ‘highly mature talent acquisition team’. While 5 of them relate to varied functional aspects of recruitment, the sixth relates to the adoption of Artificial Intelligence, Predictive Analytics and other technology tools. Successful companies are six times (6X) more likely to engage in data-driven decision-making compared to less effective teams. These companies, according to Bersin, have reported 18% higher revenue and 30% higher profits than those who don’t use these tools.
With the advent of new technologies powered by AI, the global recruitment industry is already responding to the market movements. The application of these technologies are making the job of recruiters easier and more efficient – from candidate sourcing to final hiring. According to a Forbes article, the recruiting landscape encompasses nearly 70 different technologies which is expected to grow further.
Some of the key business metrics that is driving the change:
- Cost-per-hire: Multiple organizations are vying for candidates from the same resource pool. While there is a need to close positions as quickly as possible, it is equally important to keep costs down. Automation, powered by Artificial Intelligence helps talent acquisition teams save time and cost. It brings together Machine Learning algorithms, data matching, rules, fix and standardized workflows for optimized outcomes such as better job-candidate match, streamlined operations, and an efficient recruitment model.
- Recruiter Productivity: Recruiters are generally tied down by the multitude of responses to a job posting or an advertisement. According to an estimate nearly 65% of the resumes received remain untouched, resulting in potential loss of talent. Added to it is the fact that candidate outreach is often slow and time-consuming. Automation helps recruiters increase their productivity by eliminating manual screening of resumes, widen the potential talent pool by re-engaging past applicants, and freeing up sourcing time which can be utilized in more value-add work. Higher personalization leads to a higher success rate.
- Transitioning from transaction-based to value-based: Recruiters can focus more on candidate relationship building and strategy.
The application of AI has picked up across a multitude of functions. The following figure shows the specialty areas in which the adoption and use of Machine Learning and related technologies is slowly gaining currency.
Top areas for the application of AI in recruitment functions:
Writing accurate & realistic job descriptions – A long list of ‘desired skills’ can substantially narrow down options for a recruiter. While the importance of ‘right’ skills cannot be questioned, businesses sometimes tend to overdo job descriptions to find the right match for a role. However, the expectations may not match reality. AI tools have the potential to apply different scenarios using different combination of skills. This enables the recruiting teams to see how candidate search results differ with small changes in job descriptions. The algorithms can be tweaked according to the needs of the hiring teams.
Expanding the resume source pool – Recruiting teams understand that there is a need to look beyond job boards and LinkedIn, but are often clueless in terms of the alternatives. Leveraging AI-enabled bots is one such alternative – these bots crawl the world wide web scouring thousands of websites including personal ones, forums, chat groups, social media platforms and can come up with potential candidates which hitherto were hidden. While the majority may be passive candidates, this increases the talent pool which can lead to higher hire rate.
Finding passive candidates – In some industries, such as IT and high-tech, there is a dearth of good candidates. At the same time, 1 out of 4 candidates is always looking for a job – that translates to 25% of all available candidates. The use of AI can help recruiters look at many variables which throw light on candidate employment behavior, tenure, career goals etc., that can be matched with job criteria to find the best candidate.
Speeding up processes and pipeline hiring – Unfilled jobs have a cost associated with it; as per Bersin, companies spend US$4,000 per candidate on interviewing, scheduling and assessments. AI-driven automation can make the hiring process easier in which repetitive functions are automated for faster results. For difficult-to-fill roles, recruitment teams face a lot of challenge – many are used to reactive or just-in-time approach. However, this may not work all the time. Machine Learning can help recruiters identify potential candidates and build a pipeline. Having a pipeline can lead to higher success than an ad-hoc approach.
Candidate screening – Machine Learning also helps in screening resumes for keywords and skills to check if a candidate is suitable for a particular role. Chatbots have broader application in the recruitment space. These platforms are continually evolving and today it is possible to pre-screen candidates to ascertain their credentials and suitability. These platforms can sift through candidate details pertaining to employer information, candidate history, and social media activity. Many products have come to the market which leverage Machine Learning algorithms to eliminate bias (gender or racial) from the recruitment process.
Contrary to the popular notion that automation will replace human effort, it is found that technologies work best when it complements human effort. Moreover, there is a need to design, develop, implement and validate AI solutions which cannot be achieved without human input. The advent of technology has however led to increased responsibility for the organizations implementing them:
- Increased need to maintain data to ensure accuracy, review tools for potential bias.
- Continuous need to monitor, assess, train and improve algorithms. The software needs to be trained to ask the right questions and provide the right answers.
- Formulate and maintain a robust data privacy and data security framework.