Part 2: By 2036 today’s staffing and recruiting agency model may be replaced by AI-based systems that are faster, more efficient and more capable of finding and placing qualified talent. Or will they?
In Part 1: Rise of the Staffing & Recruiting Machines: The Rush to AI-driven HR Technology, we looked at the massive investments being made in HR technology and Artificial Intelligence. Research shows that nearly 50% of jobs may be replaced by AI-based systems in 20 years, and many of those could be in sales, services, and clerical and administration – the heart of any staffing and recruiting agency.
The business value – why AI for staffing and recruiting?
The question has to be asked, even if the answer appears to be self-evident. How will Applied AI systems impact the internal operations of staffing and recruiting agencies? To answer that question, let’s look at some of the big challenges and operational expenses related to current temporary staffing and recruiting agency business models.
From a business model standpoint, today’s temporary staffing agency isn’t really much different from the one started by William Russell “Russ” Kelly back in 1946. At its heart, it’s a business organization that sells units of work by temporary or contract workers to those clients who need units of work done. Sure, technology has been added along with a variety of ancillary services and capabilities to improve the efficiency of service delivery and the variety of services and skills offered, but the basic value proposition remains the same.
Fundamentally then, a staffing agency grows and thrives on the relationships it builds with its clients, candidates, and contingent employees. But there’s a problem – and that’s maintaining those profitable relationships whenever there’s high turnover among agency staff.
Will internal recruiting agency staff turnover drive AI-implementation?
As anyone who’s been in the staffing industry for any length of time will tell you, internal staff retention and turnover is a significant problem. And with every turnover there’s a potential loss of a business relationship. In fact, according to surveys by the American Staffing Association, turnover among front office personnel in recruiting, placement, and sales runs at 50 percent to 70 percent annually.
Anecdotally, you’ll hear staffing agency management and owners lament the “two years and gone” of their internal staff. There are a lot of reasons for this, but essentially it may have much to do with the nature of the jobs and the people hired for them.
First, recruiting and staffing agencies often hire entry-level personnel lacking skills and focused education for the positions of recruiting and sales. While many agencies offer commendable on-the-job training, a significant number of agencies do not. This invariably leads to less-than-optimal staff performance. Second, the high-pressure, reactive recruiting and placement for job orders from customers often leads to internal staff burn-out.
So, as internal personnel costs play a huge role in operational costs and profitability, staffing and recruiting agencies for decades have successfully looked to growing levels of automation to reduce effort and personnel costs. Increasingly, staffing software and recruiting software are simplifying tasks associated with sourcing, reviewing, grading, and placing potential candidates. Likewise, software has greatly simplified the efficient flow of information throughout an agency. Information that drives sales, placements, and all back-office operations.
What AI-based systems will do is take today’s level of automation – augmenting the capabilities of the recruiter or sales representative or back-office finance staffer – and replace them completely if possible. Today’s staffing and recruiting software systems, point solutions developed to automate or enhance the performance of specific tasks, still silo information that’s not terribly well-integrated or analyzable. These systems will become obsolete – except in those markets and niches where maximum efficiency is not a requirement.
AI systems will reduce the need for internal staff, especially for the recruitment and placement of non-specialized job positions, as well as positions in the back-office. That will mean an exponential reduction in the cost of recruiting operations – and higher profits for management and those “super recruiters” who remain.
What will the staffing and recruiting agency of 2036 look like?
By 2036 AI systems, integrated with psychometric, cognitive, and other skills and capability assessments, had revolutionized the education, training, and placement of people into worthwhile careers. These AI’s were constantly and objectively evaluating employees at each stage of their career. With a deep understanding of an individual’s skills, talents, capabilities, and personality, AI-based systems were nearly unerring in their ability to match careers and jobs with people. Far better than any human. But the human touch was still needed.
Mahpíya Lúta stepped into her home office with a steaming mug of coffee in her hand and a purposeful expression on her face. The wall screen lit up as she entered, displaying a variety of information; pre-defined news interests, global stock tickers, and other data relevant to her business and that of her most valued customers, as well as her own schedule for the day. But she was more interested in the central images, updating each few seconds as needed, that highlighted the recruiting and staffing tasks before her.
Overnight more than 100 job orders had come in from her customers, and a new order popped onto the bottom of the screen every few minutes. These were categorized by the AI system based on previously identified criteria from priority one, those that required human intervention of some sort, to priority five, those that the AI could easily handle.
There was the usual job order variety: An IT project team of 35 was needed by one of her Fortune 100 customers; a local custom manufacturing operation was looking for a level-four 3-d print designer/engineer; an expanding elder-fitness organization wanted to hire 150 entry-level and 5 managerial level physical therapists; a major construction firm needed 2,500 workers of various disciplines and skillsets for an infrastructure project; a few senior level executives were being sought; and on and on.
As Mahpíya quickly scanned the list, she made gestures to indicate which of the orders she wanted to parcel out to her far-flung team of three other recruiting and staffing experts. This was pretty easy to do, as Argent, her personal AI, had already done a pretty good job of categorizing them based on past experience and each team members’ expertise and skills.
“Argent,” said Mahpíya, “what is the current status on all priority five job orders?”
The AI immediately responded in its clear, gender-neutral voice, “All priority five job orders have been filled. Seven hundred and twenty seven people will go to work today or tomorrow as scheduled through my standard search, credentialing, contact, and negotiation process. I estimate that the gross profit margin on these order placements will average twenty nine point three percent.”
“Good,” said Mahpíya, “Argent, the IT project looks like it may take some creative sourcing; what have you accomplished so far?”
“I revised and implemented some of the gamified sourcing quizzes for possible candidates with the requisite skills and have already identified more than 200 candidates world-wide. I estimate that all candidates will be confirmed, credentialed, and ready for presenting to the customer by 1300 hours tomorrow.”
“That’s fine, Argent. But show me the three top team lead candidates for this order to review and possibly present to the customer by noon today. I want to let the customer know that we’re staying on top of it.”
“Acknowledged.” Mahpíya knew that, next time, the AI would anticipate her request and have the top team lead candidates identified and ready for her review.
“Argent, I see that the priority two, three, and four job orders are progressing normally. Are there any other follow-up calls I should make to any customers or candidates today?” In response, the screen highlighted a dozen job orders that Mahpíya gestured at to move to her to-do list.
Quickly then, Mahpíya began a series of video calls with her customers to get details on the nuances of her own higher-priority job orders. These calls gave her the opportunity to use her own special skills – persuasiveness, empathy, and a well-honed ability to adaptively negotiate. These skills were also paramount to her success when talking through job opportunities with high value candidates. The “human touch” was invaluable here. High level candidates in turn appreciated her expertise – she was a rock-star recruiter after all – and valued her guidance on their own careers.
Even as she went from call to call, Mahpíya monitored the screens before her from one corner of her eye. There was a constant flow of information there as Argent and her team members responded to current and incoming job orders. She could see the ebb and flow of data from their social media channels as well as the shifting real-time KPI’s of their sourcing, marketing, sales, and recruiting efforts. One ticker, running across the bottom of the screen, showed running totals of incoming orders, placements, and fills. All of the back-office functions were handled seamlessly and without fanfare by the AI. The ticker and the big green numbers that highlighted the days’ real-time profit total were all she needed to know that the recruiting agency’s business was performing excellently.
Promptly at 3:00 p.m., Mahpíya conferenced in her team for their wrap-up call. It took only a few minutes to highlight the few action items for tomorrow. It had been a typically good, million-dollar day.
“Argent,” said Mahpíya, “book a ticket for me to Tahiti tomorrow. I’d like to do some reef diving. I’ll check-in as usual. Otherwise, please handle my incoming calls. You’re in charge.”
“Acknowledged,” the AI responded.
Phil McCutchen is a B2B software marketing professional with 25-years of experience in the staffing and recruiting industry. The observations and predictions presented here are based on his own research and future thinking and represent his own opinions.