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How hiring is getting easier with AI-sourcing tools
Hiring alone can be tough, and the flood of AI-generated resumes troubling recruiters has made it even tougher. Recruiters now sift through more data than ever to find the right candidates.
Yet, the process doesn’t have to be too complex. AI-assisted candidate sourcing tools are making things easier for recruiters, and we’re exploring how. Read on to learn why hiring is getting easier with these tools and how to use them effectively.
How do AI-sourcing tools simplify hiring?
AI-sourcing tools automate repetitive tasks required in early-stage recruitment. For instance, they scan LinkedIn profiles, code repositories, and public directories to curate qualified candidates for specific roles.
Then, data is extracted from each applicant’s profile, and they’re scored based on the job requirements. Recruiters can simply focus on candidates with the higher scores.
Compare using AI-sourcing tools to recruiters scanning profiles themselves, curating top candidates, and trying to score each candidate. The process gets overwhelming. Software tools apply AI algorithms to automate these repetitive tasks.
Other ways include:
Personalized outreach
After curating the best candidates with AI assistance, the next step is reaching out to these candidates with compelling pitches. This process requires recruiters’ manual efforts, but AI also helps by generating tailored recruitment emails for each recipient. You don’t have to use the AI output exactly as it is.
Instead, AI can create the base email, while you edit it to add a human touch. Just as recruiters don’t want wholly AI-generated resumes and cover letters, candidates generally don’t want wholly AI-generated recruitment emails.
Early-stage recruiting involves back-and-forth emails between recruiters and candidates. AI helps suggest responses in these email threads, particularly for routine inquiries that need simple answers.
Maybe a candidate inquires about the job arrangement (in-office, remote, or hybrid) or asks about the pay range; AI can suggest automated replies based on your organization’s data.
Workflow automation
In the late hiring stages, recruiters handle a lot of administrative work, like scheduling interviews, setting up online meetings, and sending status updates. AI helps automate this work.
Recruiters can delegate AI bots to set up Zoom video meetings for candidates and mark them on calendars. Each candidate will be alerted and sent periodic reminders ahead of the meeting date.
Interviews can be scheduled automatically based on available time slots, and recruiters can receive automated status updates for each candidate’s progress. Things work more easily when recruiters don’t have to spend too much manual effort on these routine admin tasks.
Predictive candidate matching and ranking with AI
Earlier, we mentioned how candidate sourcing tools harness AI to scan directories to discover and rank the best candidates, but it’s worth dwelling on. Let’s dive deeper into how this works.
Semantic analysis
Recruiting platforms usually scan resumes for specific keywords that match job requirements, but AI takes it further than seeking keyword matches.
With natural language processing (NLP), resumes are scanned to understand each candidate’s skills and career paths. This system makes platforms better at curating the right candidates from a pool.
Let’s get a bit technical. AI relies on mathematical calculations for all it does. When an AI model is fed data (like a resume), it converts the data into numerical representations (an array of numbers) called vector embeddings.
The AI model then processes these numbers, and the output is converted back into the same format as the source data.
In this case, a candidate profile and the job description are converted into their respective vector embeddings. The AI model then compares both vectors to calculate their compatibility distances.
If the distance is small, it means the candidate’s profile and the job description are alike, so they’re a good candidate for the job. Otherwise, if the distance is large, it means the candidate isn’t likely a good fit.
Because of the mathematical calculations behind the scenes, AI models often produce better candidate matches compared to systems that focus only on keywords in the candidates’ resumes.
Dynamic ranking
AI also applies mathematical calculations to rank qualified candidates. When people apply for jobs and pass the initial applicant tracking system (ATS), qualified resumes can be analyzed by AI models and assigned respective scores on a leaderboard. This leaderboard helps recruiters quickly find the best candidates to focus on.
Benefits of using AI-assisted tools in hiring
Speed
Speed is the main benefit of integrating AI-assisted tools into your candidate sourcing and hiring process.
Tasks that would have taken much manual effort, like scheduling meetings, parsing resumes, and adjusting calendars, are automated, making the hiring process faster.
Recruiters and HR staff get more time to dedicate to the complex aspects, while routine work gets automated significantly.
Better objectivity
Hiring can’t be 100% objective, but recruiters try their best to be. Companies want the right candidates without bias, but when recruiters are strained after filtering through many CVs, unconscious bias easily sneaks in.
AI-based systems make the process more objective by analyzing resumes based on key metrics. Candidates are weighted on specific factors, and the best ones matching the factors are quickly highlighted.
Yet, there’s a bit of conflict in introducing AI into the hiring process. Analyzing candidates shouldn’t be left completely to AI models, as algorithms lack the human intuition needed to identify strong candidates even when they don’t look like it.
For example, AI models overlook people with non-traditional career paths. These models can have their own forms of bias, based on the data on which they were trained.
Data integration
AI models leverage your organization’s hiring data to help make the right decisions. For example, many applicant tracking systems (ATS) now harness AI to select candidates similar to the previous ones you’ve hired.
The models analyze your previous hiring decisions and use them as a guide for future recommendations. This data integration helps you find the best candidates based on your organization’s unique needs.
Want to know which AI-assisted recruitment platforms can simplify your hiring process? Read our guide to the best recruitment platforms, designed to make hiring easier than ever before.
