The Elimination Of All Sourcing Jobs… Is Being Accelerated By AI

Note: Sourcing jobs led the recent wave of recruiting layoffs at top firms like Amazon and Meta. So in the near future, be prepared for the day when AI-generated search algorithms will permanently replace almost all the work human sourcers have done.

Just like not long ago when ATS screening formulas replaced most human resume sorters/screeners. And all smart recruiting leaders must realize that the elimination of these sourcing jobs will occur. Because in a side-by-side comparison with an AI algorithm, even your top sourcer’s best Boolean search string will fall short in speed, quality, and cost. If you think that you have a long-term future in sourcing, it’s essential that you read on and find out why that will be a painful assumption.

Justifying Why Corporate Sourcing Jobs Should Permanently Go Away

Because recruiters spend 100% of the time finding and hiring “human employees.” Talent leaders have a natural tendency to favor human employees (and the work that they do) over the multitude of emerging software and hardware alternatives for doing the same work. So I hope the reader realizes that being the first to be let go during this wave of layoffs should serve as a clear indicator of the fact that, like it or not. Most sourcing jobs will be permanently going away. Below you will find the top 10+ reasons why smart talent leaders are purposely targeting these jobs for elimination, where the most powerful justifications are listed first.

  • The demand for AI is expanding into recruiting because of its recent positive acceptance by almost every business function – there is little doubt that with the recent positive publicity surrounding AI and the ChatGPT program. Most functional leaders in HR and recruiting are now much more open to embracing the widespread use of AI and machine learning. And this widespread expectation by executives will likely result in more support and funding for any new AI efforts.
  • AI searches produce higher quality prospects – although few human sourcers ever take the time to measure the overall quality of the prospects that they identify. In direct contrast, any AI-driven sourcing process will automatically measure the quality of its identified prospects. The three best measures of quality include what percentage of the identified prospects 1) initially meet the qualifications, 2) the percent that is offered a formal interview, and 3) the percentage of identified applicants that actually become new hires.
  • AI searches are cheaper – because after it is developed, using an automated sourcing algorithm is almost free. Its low cost allows you to search longer, more broadly, and more often. And in direct contrast, during times when the primary goal is to cut costs. Recruiting, retaining, and operating with a large number of human sourcers is quite expensive.
  • AI sourcing is more flexible – because recruiting operates in frequent up and down cycles. And sourcers are especially expensive during periods when they are forced to be idle much of the time as a result of frequent hiring freezes and headcount cuts. In direct contrast, the capacity of a sourcing algorithm can be changed almost instantly to fit any new level of demand.
  • Searching by human sourcers is slow – because AI sourcing is automated, it can continuously operate 24/7. And as a result, it will consistently produce the required prospect list much faster than human sourcers. They only operate eight hours a day, five days a week. And as a result, when you use the slower human sourcers. Your key positions will be vacant much longer, and that lost productivity will be quite expensive.
  • Your AI sourcing results are guaranteed to improve continually – AI algorithms have a competitive advantage because they are driven by machine learning. This means that these algorithms automatically learn from the successes and failures of literally every sourcing search that was made by anyone (human or algorithm) within the recruiting function. However, in contrast, busy sourcers seldom have any free time to identify and learn from their own or the errors of other teammates. And without performance feedback, sourcers often continually use the same search strings, even when the search string for a unique job needs to be completely customized.
  • Sourcing teams create silos that reduce sharing – because most sourcers operate independently with only basic supervision. There is seldom any formal sharing mechanism between the many individual sourcers. A mechanism that quickly and openly shares “what works and what doesn’t work.” Obviously, because the AI software algorithm is completely integrated, it automatically learns from every other sourcing method.
  • Sourcers barely use performance metrics to guide their behaviors – in my experience, most human sourcers resist the use of metrics. However, because AI can only operate in a data-driven environment, this automated sourcing approach will automatically track and report every sourcing effort’s quality, speed, volume, and costs. And because metrics improve the performance of anyone or anything that uses them. The higher positive metrics AI produces will help you build up your image among executives and hiring managers. AI metrics reveal the best sources to use for each job family. AI-driven sourcing will reveal which of your recruiters exclusively use only the most effective sources for each job family.
  • Sourcers primarily rely on Internet Boolean searches, which are seldom the most effective source – the most frequently used search approach for sourcers is using a Boolean search string to search through thousands of “strangers” either on the Internet or in job boards. In direct contrast, an AI algorithm will automatically use the sources with the best track record for the job family. And those most effective sources (in order) are usually employee referrals, boomerang rehires, those that previously rejected your offers, top candidate dropouts, silver medalists, and your employer branding efforts. And not a single one of these “top sources” requires a sourcer to create an external Boolean search.
  • AI will more accurately “screen in” only the best applicants – because many sourcers are also responsible for the initial “screening in” of the most qualified applicants. It’s important to know that with the benefit of machine learning for continuous improvement. An AI algorithm can do this screening much faster and more accurately than any overworked human sourcer. In fact, recently Amazon has led the way by being the first large-scale user of AAE automated technology. Which uses AI to evaluate job applicants and to select which applicants more accurately and objectively should be granted a job interview.
  • AI also produces better job ads – with the use of machine learning. Over a period of time, AI can learn which words and phrases in a job posting have the most attraction power with your target audience. The data will also reveal where those job opening announcements should be most effectively placed. In fact, LinkedIn is currently testing AI-written job descriptions/postings. Because too many human sourcers continually use the same job posting approach over and over out of habit.
  • Diversity recruiting using AI will eventually produce unbiased results – historically, some AI-driven sourcing and screening efforts (at companies like Amazon) have had an adverse impact. However, revealing AI’s diversity issues alone is really unfair. Because the AI percentage of negative diversity impacts was not compared side-by-side to the serious diversity impacts that are routinely created when humans source and screen out applicants. And over time, I fully expect machine learning to produce superior diversity results.
If you only do one thing – use your own research capabilities on LinkedIn to count just how many sourcers have recently stopped listing their current job as sourcing at major technology company. And then use that startling number as a motivator to better prepare yourself and others in sourcing for the inevitable job uncertainty that is in their future.

Final Thoughts

I have been accurately predicting large-scale recruiter layoffs for over five months now. And I have also been describing the future of recruiting, where I list every recruiting element recruiting that will be dramatically changed by AI.

However, during these last few months, I have found that even the positive act of alerting people is extremely controversial. Because, of course, no one ever wants to hear a single word about the possible demise of their job family. But also, because there are such a high number of people who are currently working as sourcers, consultants, and vendors.

Of course, before anyone outright denies this trend. I would remind them that the elimination of entire job families is not new. Because it has happened before to travel agents, copy machine operators, taxicab drivers, file clerks, and interview schedulers.

Therefore, I suggest that those that are in any way involved in any aspect of recruiting that doesn’t involve convincing or selling a candidate. To either begin looking for a job outside of recruiting (i.e., sales, training, or customer service).

Alternatively, begin proactively strengthening every aspect of your candidate convincing capabilities (i.e., convincing a prospect to apply, convincing top candidates not to drop out early, and selling the finalist on accepting your offered job) because convincing/selling will be the only area in recruiting that isn’t going to be dominated by technology.

Yes, I Have Been Predicting The Demise Of Sourcing For A While

I’ve continuously predicted the decline of sourcing jobs. And yes, “the sky has been falling” for a while, but most simply haven’t looked up to notice it. For example, sourcers used to screen resumes, but now ATS technology does this job 75% of the time. And you can’t ignore Amazon that recently offered buyouts to all (yes, every single one of them) of their medium/lower-level staff in TA. They can lose all of this talented staff because they also announced that AI driven automation will take over the role of resume screening and job matching.

Also, many with the title of sourcer have only been masquerading (I call them SINO’s [Sourcers In Name Only]). Because they actually consider browsing LinkedIn to be sourcing. When TA becomes fully data-driven, they will be history. And finally, because this new AI iteration is so powerful, everyone must realize that, unfortunately, it makes all of my previous technology threats pale. In the end, just like taxi drivers and travel agents, even though there will be a few remaining that won’t mean that the profession isn’t being decimated. So yes Balazs, I still publicly predict that all routine work in recruiting will be replaced by technology. And the only thriving area will be candidate convincing/selling.

Finally, I recommend that you take a step back. And track what has happened over the last decade to all other well-established “search jobs”. You’ll find that each job has succumbed to automation (boosted by AI). Now when searching for information, you no longer go to a reference librarian, you use Google’s algorithm. And when looking for a product, you no longer ask a salesperson, you rely on Amazon’s algorithm. And when looking for a top restaurant, you use Yelp. Yes, sourcers face the same future as librarians. And that future is ugly.

Author’s Note

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About Dr John Sullivan

Dr John Sullivan is an internationally known HR thought-leader from the Silicon Valley who specializes in providing bold and high business impact; strategic Talent Management solutions to large corporations.

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