How (and How Not) to Accelerate Hourly Recruiting with AI

Hourly hiring has long been a job that’s never done, with the hospitality and retail industries consistently hovering over 100%. The labor shortages brought on by the pandemic heightened this problem, and unfortunately, it’s a challenge that continues to press on. Today, 79% of operators report struggling to find staff. And that’s no surprise considering there’s 87,000 less hourly workers on the hunt for a job than pre-pandemic. This shortage has led to a 20-day average hiring process, severely impacting profits.

Companies that can’t meet customer demand due to understaffing lose an estimated 10-25% of revenue per location, according to Fourth company data. As one retail manager described it:

“The recruiting crisis we’ve faced over the past couple years has turned into a major revenue problem. We’re losing thousands in sales each week because we don’t have the staff to stay open our full hours.”

Engaging and retaining hourly workers is no easy task, either. Disengaged workers cost employers up to $8,000 per person annually. And with turnover rates currently sitting anywhere from 150-200%, replacing and training each new hourly employee costs on average $1,800, at minimum.

In search of innovative solutions, many in the industry have started looking to artificial intelligence and automation to help optimize their recruitment process, with the goal of hiring qualified candidates with speed and ease. But simply implementing new technology alone won’t solve all the complex hiring challenges.

The key to sustainable success is understanding when to use automation and when to use AI. Let’s explore why it’s time to embrace AI, when you shouldn’t, and who’s leading the pack within the industry.

Why Hourly Businesses Need Faster AI Recruiting

The worker shortage has made implementing a fast and efficient hiring process imperative for hospitality and retail businesses striving to rebound from the pandemic. Here are some of the key factors driving the urgent need for AI powered recruiting software:

Overworked Hiring Managers

• Burnt out hiring managers – Studies show 48% of hourly hiring managers are burnt out and overworked, trying to interview candidates while covering understaffed shifts. The relentless need to attract job seekers while supporting their staff and handling managerial duties is leading to a never-before-seen uptick in manager resignation, citing burnout, low pay, and an overwhelming sense of feeling undervalued.

Turnover Costs Continue to Rise

• Soaring turnover costs – With turnover rates from 150-200%, training each new hourly employee costs on average $1,800. For restaurants, turnover can cost up to $150,000 annually. Lagging hiring prolongs vacancies that fuel these massive costs. This does not include recruiting costs from ad spend, should your company run those programs.

Lost Revenue from Understaffing

• Lost revenue – Understaffing due to hiring delays causes many locations to operate with reduced hours. This results in 10-25% losses in revenue per store, amounting to millions in lost sales.

Increasing Costs of Online Job Ads

• Rising ad spend – Evolving paid ad models on major online job boards like Indeed or ZipRecruiter now require more spend to get job posts seen by applicants. Drawn out hiring cycles increase ad costs.

Frustrated Candidates Won’t Return

• Candidate expectations – In a tight labor market, slow interview scheduling, poor communication during the recruiting process leave candidates frustrated. So much so, 69% of applicants say an overall poor experience makes them less likely to buy from or apply to that brand again. The top requirement to hiring best fit candidates is being the first to send an offer letter. The second requirement is having great communication from the application process to the onboarding process.

The Potential Promise of AI and Automation

On the surface, artificial intelligence (AI) and automation seem like ideal solutions to fix many broken hiring and recruitment processes. It can help talent acquisition leaders:

• Screen high application volumes faster using intelligent bots and algorithms
• Use historical hiring data to predict and target the optimal days/times to advertise open roles
• Tap multiple sites and platforms to source higher quality applicants

But we’d be remiss to continue this conversation without addressing the first question many savvy hiring managers have: what are the potential risks and downsides of replacing humans with ai recruiting software.

Weighing the Pain Points vs. Risks of AI Recruiting Tools

There’s a place for human interaction, a place for automation, and a place for AI in the HR industry. The secret to sustainable success is understanding when each comes into play to best optimize your recruiting workflow.

The Pain Points AI solves for:

• Time savings – Chatbots and automated screening free up recruiter time spent on repetitive tasks like reviewing applications from non-qualified candidates or answering basic candidate questions.
• Volume management – Intelligent filtering of applicants based on required qualifications automatically screens out unqualified candidates. This narrows the candidate pool to qualified, best-fit applicants.
• Process optimization – AI and machine learning can analyze past hiring data to guide future decisions on job ad budget, timing, and targeting. One UK restaurant group reduced their cost per hire from $6,000 to $430 within 6 weeks using AI to shift more budget to Indeed during peak application times.

The Risks of AI:

• Algorithmic bias – AI models trained on flawed historical data can inadvertently perpetuate biases. This introduces major legal risks around discrimination.
• Lack of transparency – Complex AI systems often can’t explain why one candidate was rated or ranked higher than another. This lack of transparency can frustrate candidates.
• Privacy concerns – How applicant data will be collected, used, and protected requires careful evaluation when implementing AI recruiting tools.
• Contextual nuance – AI still lacks human-level understanding of subtle verbal/non-verbal cues and nuances in language, experience details, etc. In fact, studies show 66% of Americans are less likely to apply for jobs using AI in hiring and recruitment processes. And 71% oppose AI making any final hiring decisions.

Furthermore, some jurisdictions have already started regulating use of AI in recruiting:

• New York City banned businesses from using any AI recruiting tools. Violators face fines up to $1,500 per instance.
• Illinois requires employers to notify and obtain consent from applicants if using AI video analysis.
• Maryland mandates explainability for certain automated hiring systems. Employers must be able to explain AI-driven candidate scoring.

Finding the Right Balance: A Strategic Approach

The answer lies between avoiding AI altogether or jumping in blindly. Here are best practices businesses should keep in mind:

Use Automation to Screen Candidates, Keep Humans in Final Hiring Decisions

Any time you’re dealing with individual candidates, keep humans involved in final assessments and hiring decisions. However, use automation to pre-screen candidates and to better handle high-volume administrative tasks like scheduling interviews.

For the recruiting teams, for example, set up automated questionnaires candidates must complete. Candidates who answer all questions correctly can receive an immediate invitation to schedule an interview via an online calendar. This eliminates the need for recruiters to manually review every single application that comes in and spend time with the common back and forth phone calls and emails required to make contact with applicants.

You can screen potential candidates only for basic qualifications without introducing AI bias against individuals. For example, if they’re of legal age, if they have certain job experience, or if they can work the hours you need. Just be sure to still allow for manual review in case the automated screening incorrectly rejects a qualified applicant.

Leverage AI to Optimize the Recruitment Process Through Data, Not Individuals

AI performs best when optimizing processes based on past data, not assessing individuals. For instance, use machine learning to analyze historical hiring patterns, allowing businesses to forecast upcoming staffing needs with greater accuracy than human biases. One retail chain saw a 20% improvement in labor forecasting accuracy after implementing this technique. The AI considered sales trends, seasonality, and other data to predict needs 4-8 weeks out. When you know when you can afford to work with less employees, you can make the best use of the staff you do have!

Another prime use of AI is programmatic job bidding. Algorithms can automatically shift budget between sites like Indeed, ZipRecruiter, and Google Job Boards. Using historical candidate data, it auto-shifts budget to boards and time slots where you’ve historically hired more applicants from.