With more and more employers evolving their recruitment processes to include artificial intelligence, there is an often forgotten need for a parallel evolution to take place around what key performance indicators (KPIs) are used to accurately measure the impact of using AI solutions.
For while certain (traditional), KPIs, like time-to-fill and cost-per-hire, are more relevant than ever [AI offers great potential for increasing efficiency and reducing costs], these old KPIs must be supplemented with new metrics that reflect how AI affects factors like hiring success and compliance rates.
These KPIs below do measure AI’s impact on recruiting and are straightforward to track.
A key challenge of recruiting is finding candidates with the right qualifications to send to hiring managers.
AI is very effective at helping recruiters narrow the candidate pool. For instance, recruiters can use generative AI or intake call analysis to create more accurate job postings that attract the right applicants. Then, you can use the solution to generate a standardized list of interview questions, ensuring interviewer preparedness and fair comparisons.
By evaluating each candidate based on the same criteria, recruiters can screen candidates with more consistency and efficiency, boosting qualified candidate submissions while advancing only the most qualified candidates to hiring managers.
How to measure it
To calculate qualified candidates or submissions per position, divide the number of candidates sent to hiring managers by those who were selected for interviews.
(Qualified candidates or submittals per position = number of candidates submitted ÷ candidates who were selected for interviews)
Benchmark the KPIs before implementing AI, then compare the metrics as your organization adopts AI solutions.
Saving time is a core benefit of using AI for recruiting. In fact, 96% of surveyed recruiters believe AI can enhance talent acquisition and retention while saving them 14 hours of manual tasks each week.
For example, conversational analytics helps recruiters save time interviewing, taking notes, transcribing, summarizing candidates, and manually entering data into applicant tracking systems (ATS).
One case study demonstrated that conversational analytics enabled recruiters to save approximately 75% of the usual time it took to find the right hire. Chatbots and automated interview scheduling can also reduce the time spent on nonstrategic, repetitive tasks.
These advancements empower you to find the right candidate faster, reducing the time to hire. By streamlining the post-interview and data-gathering process, AI enables recruiters to compare candidates’ qualifications more efficiently, reducing administrative burdens and accelerating the overall recruitment timeline.
How to measure it
To track the time to hire, subtract the job posting date from the date the candidate accepted the offer.
(Time to hire = date candidate accepted the offer − date the job opening was posted)
You can also measure how much time recruiters spend on post-interview tasks, such as gathering feedback from hiring managers, synthesizing and comparing notes, creating submittals, and entering data into the ATS, which is related to the next KPI…
Analog processes may appear cost-neutral initially, but the hidden costs of inefficiency can accumulate over time.
Integrating AI automates time-consuming tasks like note-taking and finding interview insights, allowing recruiters to focus on strategic decision-making.
This approach accelerates hiring timelines and in turn, reduces the cost per hire. Plus, many AI solutions offer free versions or trial periods, enabling organizations to experience these benefits without upfront financial investment.
How to measure it
To calculate the cost per hire, document recruitment expenses such as time spent screening and interviewing candidates, advertising, and background checks. Then, divide the total recruitment expenses by the number of hires that resulted from those efforts.
(Cost per hire = total recruitment costs [ex. ad spend + recruiter time + commissions] ÷ total number of hires)
Metrics like time to hire and cost to hire are great for measuring the operational efficiency of your hiring processes, but they don’t capture the full picture.
Improving these KPIs may even be harmful if it comes at the cost of hastily hiring candidates who don’t work out over the long term.
That’s why quality of hire is important to track; it captures the impact of the candidate’s contributions over time.
According to a LinkedIn survey, 39% of talent leaders agree that quality of hire is the most valuable metric to evaluate performance.
AI enhances the quality of hires by optimizing every step of the hiring process. Recruiters can leverage AI to create accurate job descriptions, evaluate candidates’ skills and cultural fit using data, and streamline recruitment, leading to better matches and a positive onboarding experience.
How to measure it
Measuring the quality of hire involves tracking variables that align with your organization’s priorities.
Indicators may include metrics like employee performance, retention, onboarding time, customer service score, and more. Select the metrics that are most relevant, then insert them into this formula, with the option to add weight to certain indicators as fit.
(Quality of hire (%) = indicator % + indicator % / number of indicators)
Tracking interview compliance as a KPI enables you to assess your organization’s adherence to legal and regulatory standards and become more aware of potential liabilities.
Trackable metrics include the frequency of EEOC violations, data breaches, and candidate complaints.
These KPIs are especially important to track because often, organizations aren’t even aware of how much risk they’re taking by not closely documenting their EEOC and data compliance or lack thereof.
AI interview analytics solutions can automatically provide a summary of potential interview non-compliance across EEOC categories to determine where targeted coaching may be needed for hiring managers.
Similarly, you can use AI data security solutions to monitor data transactions in real time and automatically flag any potential compliance issues.
When it comes to making hiring decisions, AI can also assist in mitigating the potential for bias. Recruiters can use interview intelligence to automatically extract objective data from interviews to evaluate candidates based exclusively on their skill sets rather than their personal attributes.
This ensures that every hiring decision is justified and backed up by data.
How to measure it
Calculating compliance KPIs varies depending on the specific compliance area being evaluated.
One way to assess EEOC compliance is to track the number of instances where your AI solution flagged a violation, and in which category, such as age, race, or gender.
At first, your violations may appear to spike if you weren’t previously tracking them at all.
Once you become aware of noncompliance issues through actively tracking it as a KPI, you can train your hiring managers to be better interviewers and observe how violations steadily decrease.
If any violations result in legal settlements or fines, track those expenses too, and how they compare to the cost of preventing them.
Another example is tracking the number of data breaches, remediation costs, and reputational damage. Establishing a tracking system to increase awareness of vulnerable data is a crucial step in ensuring compliance with data privacy regulations and protecting sensitive employee and customer information.
The extent to which AI will reshape hiring processes over the next few years is unknowable.
But if you benchmark these KPIs now and continue to track them as you adopt new solutions, soon enough, you’ll be able to see precisely how much AI has impacted your organization’s success.
