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How AI-Powered Tools Can Help Recruiters & HR Teams

Technologies such as Artificial Intelligence (AI) and Machine Learning are powerful tools for HR teams. They can help recruiters sift through extremely large amounts of data, enabling them to interpret resumes better and suggest high quality candidate matches.

For job applicants and even employees, AI can lead to greater personalization within the hiring or promotion process, and thus an improved experience. AI can reveal the most relevant roles for the candidate, streamlining the job-seeking experience, and highlighting internal and external opportunities with greater relevance.

AI for Talent Attraction

Attract and convert qualified talent while connecting the right candidate to the right career opportunity. Find and engage candidates with the right skills and potential for success.

Use AI to:

Create a personalized journey for candidates

People today expect a ‘Netflix-like’ experience in every aspect of their lives: smart recommendations that take the hassle out of searching and filtering. For businesses looking to offer this level of service, AI job matching can suggest roles to candidates that match their skills, experiences and goals. Use AI to offer unique talent journeys through your Career Sites or Candidate Portals, and ensure that potential employees get the best possible, individualized experience when applying to your company.

Let candidates control how they would like to be reached (and ensure you handle candidate data correctly), with AI-powered Preference Centers.

Boost your employer brand by making the whole experience of finding and applying for a job totally seamless.

Find higher quality candidates

As the volume of candidates increases, it’s very useful to find and evaluate candidates based on their skills and potential. AI helps you do that with a degree of automation, to highlight those people most suitable for a given opportunity.

Using AI to match roles and candidates, based on skills, helps you score applicants against what hiring managers are looking for – giving TA teams a streamlined, consistent view of fit, and saving them time by focusing on quality candidates first.

AI can also help you discover contacts you might not have even considered within a more manual process. As algorithms learn which skills tend to be relevant for which jobs, and infers the skills someone might have or learn in the future, it can recommend people for open roles. Make sure the AI-powered recommendations can be explained: you need to know not just who’s a fit, but why the system suggested these candidates for review. In turn, explain recommendations and decisions to candidates.

AI can also look at how often certain characteristics appear amongst your top performers to find relevant contacts for Sourcers and Recruiters, getting as close as possible to their ideal candidate profile.

Prioritize hard-to-fill roles

Another useful application of AI is sifting through the roles in the system and working out which roles will be hardest to fill (i.e. you have a low amount of contacts in your Talent CRM with relevant skills) and how long it is likely to take to fill, based on skills data as well as broader market insights. This can help recruitment teams prioritize the right things, and get started quickly (or apply the extra effort) for the more challenging positions.

Reduce bias in the hiring process

Matching candidates to roles based on skills, rather than arbitrary characteristics, makes the recruitment process fairer and more transparent.

Algorithms trained with the right data can ensure that ‘hidden gems’ are brought to light, from within your organization or wider talent pool, that are ideal for a role – but may otherwise have been overlooked.

If your business has diversity targets, introducing AI to your recruitment and internal mobility initiatives could help you reduce unconscious bias and get better representation, efficiently.

Reduce the time to hire

Find great candidates quickly, with suggested contacts or resurfaced contacts. AI can match the ideal candidate to a role, making the hiring process more efficient.

It should also be able to explain how contacts have been prioritized (why one ranked higher than another) to help recruiters make fast decisions. They should be able to see each component’s weight (or influence) in the decision (e.g. the mix and weight of skills, seniority, proficiency and industry) and what skills impacted the recommendations the most.

AI can also help recruitment teams predict the overall length of time a candidate will stay within their current role (their propensity to leave) and their likelihood to engage, so you can focus your time on the most valuable contacts.

Find time and cost efficiencies

As well as getting people into roles faster, AI-powered technology can help save time and money in other ways. An AI-powered chatbot, for example, could automatically screen candidates, answer their questions, schedule interviews, and drive referrals.

AI can be used to automate workflows in your Talent CRM (such as adding people to pools), and to enrich candidate data (keeping contact information current and relevant in real time)... so you can focus on more strategic work.

AI for Talent Retention

Beyond external recruitment, AI can also be used within internal mobility platforms and Talent Marketplaces to find roles and opportunities for employees who are already at your company. This can help with retention, performance and engagement, and HR teams can fill positions faster while promoting a culture of recognition and progression.

AI can be used to:

Provide career pathways for employees to explore

AI can be used to create a “map”-style visualization of career paths: a way to let employees freely explore career development options from any role, both from a global and industry-based perspective, using data specific to your company and industry.

Motivate employees by showing them the progression opportunities beyond the ‘next role’, and also by showing them career options (for example, lateral moves) that they might never have thought of. Moreover, AI models can help people understand what skills/training they may need in order to make the next step(s) in their career.

Recommend opportunities for each employee

Help employees find suitable opportunities (this could be full-time roles, but it could also include part-time internal projects and learning opportunities to upskill) quickly and efficiently. With smart AI, the recommendations can change following an employee’s interaction with the platform, every time they do so. Team members get complete control over their preferences and career objectives, providing opportunities that match their desired career paths, making them engaged and more likely to stay at your company.

AI can also help match employees with mentors in the business, depending on their career aspirations.

Recommend candidates for open opportunities

Relatedly, AI can look at the database of people in the company – and their skills and potential skills – and help managers find and reach suitable internal candidates. The recommendations could also consider candidates’ interests and goals, and uncover candidates that may traditionally be overlooked.

AI can help you calculate the probabilities of people moving between roles, so you can improve your retention rates and internal mobility performance.

Understand your employees

As well as helping you highlight appropriate internal opportunities, AI can be used even more proactively, to flag when someone may be at risk of leaving. It may be around engagement with your Talent Marketplace, or based on other information about their career so far, but insights about your biggest potential flight risks can be incredibly valuable in designing personalized retention strategies.

The AI in Beamery is intended to provide useful recommendations and help users discover suitable matches, not programmatically steer people into one role. Our models are not meant to replace humans; instead, they give relevant information to human decision makers to make better decisions. Learn more in our Explainability Statement.