Since the Industrial Revolution, the pace of technological advancement has been something of a mixed blessing when it comes to job creation (or retraction). As major industries moved from the age of manual labor into the era of automation (around 1760-1840, if you want to get geeky about it), the world as we know it has irrevocably changed everything about the way we work, with an undisputable trend towards improved operational efficiencies, enhanced worker productivity and outcomes not previously possible in earlier agrarian ages.
If you missed that part of your Freshman year history class or still think Jethro Tull was the name of a singer in the eponymous rock group (they were named after the inventor of the seed plow, for the record), let me refresh your memory.
In a short span of a hundred years, humans witnessed a boom in innovation and advancement that was unrivalled in human history. We talk a lot about the concepts of “disruption” and “innovation,” but advances like the Bessemer process of steel manufacture, the Cotton Gin, the photograph, the telegram, the automatic weapon and sundry other advancements fundamentally altered everything from the way we moved from water wheels to steam engines to the way we now purchase clothes.
Instead of make them at home, as was the norm until the textile mills at the heart of the Industrial Revolution drove down the price of consumer goods to the point where almost everyone could afford such conveniences as not having to, say, knit their own damn socks and stuff. Talk about innovative, right?
Tasks that took days were reduced to mere hours, and what took a dozen men to do manually could now be accomplished by one, aided, of course, by machines.
Welcome to the Jungle.
Travel used to be prohibitively expensive and time consuming, but as Jules Verne proved, by the end of the Industrial Revolution a trip around the entire world – half took Marco Polo almost a decade to traverse only a few centuries prior – could now be reasonably accomplished in 80 days.
The quality of life improved for almost everyone, as did class mobility and the concept of the “self made man” instead of the serf became the paradigm of the new world order. Our daily lives became easier, our work lives more efficient (and our 6 days a week, 12 hour a day schedules were effectively slashed to the current 5 day, 40 hour a week standard, as a bonus). As capitalism took hold, the standard of living increased, from increased leisure time to higher worker pay to true cost of living increases throughout the Western World.
Say what you will, turns out, free markets sometimes work as intended. Then, of course, JP Morgan began building his empire, and things worked out a little differently. But still, we can agree, this is way better than the Dickensian existence of workhouses, child labor and entrenched classism. Even if these things also gave us Donald Trump.
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Of course, capitalism soon turned South as we moved from John Locke to Adam Smith, and shifted focus from the micro to the macro; it didn’t take long before individual imbalance and inequality were created at the expense of building “the wealth of nations” (which largely lined the pockets of a privileged few), and business empires were built on sacrificing human dignity for bottom line outcomes. People were seen as interchangeable, dispensable, and ultimately a business cost that could easily be skimped upon by pushing forward products and processes at the cost of completely commoditizing their people.
In the machine age, we became just another set of cogs that could be replaced as needed, which happened to be often, giving the often squalid living conditions of the lower classes, as outlined in such books as Upton Sinclair’s seminal muck-racking masterpiece, The Jungle, or Joseph Riis’ capturing of the truly heinous existence of immigrant and unskilled workers, crowded into tenements with standards of living that were outrageous even by the standards of the time (the ensuing outcry led to the growth of the Populist movement, so we have that to thank for Trump, too).
Malnutrition, forced labor, unsafe working conditions and laughably low pay became the new normal as classism went from a hereditary concept to an economic one almost overnight. Turns out, rich people almost always win – and as that system became ever more entrenched, the odds against class mobility became ever more daunting.
I think the impact the Industrial Revolution had on society as we know it is pretty obvious, at least in the context of my inner history geek and random knowledge of trivial or esoteric facts (which is both a blessing and a curse). Of course, we’re in a similarly dynamic and seminal era of change right now, probably the biggest since the first throes of the age of industry.
I’m talking, of course, about the Technology Revolution that’s revolutionized our lives so dramatically, the literal “rise of the machines” that has occurred in only a few short decades and fundamentally altered almost every part of our daily existence, from how we shop to how we communicate.
It’s pretty clear we’re also at an inflexion point when it comes to the future of work. And I can’t help but think that we’re all really so selfish and so self-involved that we’ve convinced ourselves artificial intelligence is going to change everything about our jobs and our livelihoods. It’s a trending topic and buzzword at the moment, but the implications for all of us are anything but temporal.
The Product of Progress: 3 Ways AI Will Change Recruiting.
Here’s what the Industrial Revolution taught us. It’s really simple: if you’re a recruiter or sourcer who currently thinks AI could never, ever replace your job, you are mistaken. In fact, you’re not just naive, you’re dead wrong.
I know you’re about to start trolling me about how indispensable you are, and how no machine can ever replace your specialized skills, expertise or years of experience. But here’s the thing: it’s not about you, at all. The biggest change in recruiting will have almost nothing to do with recruiting – the future of the industry will be largely determined by how candidates adapt (and adopt) to new technology.
I mean, if you haven’t figured out the importance of engagement in the first place, or understand no matter what the market looks like, candidates are actually always the ones in control – then you’re either delusional or run a desk for Robert Half.
For those of you who are at least a little aware of the shape of things and the reality of our professional existence, you know there’s more to the story than just right and wrong – there are a ton of shades of gray involving economics, egos and ethics at play here, too. Now, I can’t predict the future – and if I could, I’d sure as hell not be blogging for Recruiting Daily. I’d take my capitalist ass over to some hedge fund and start building enough wealth so that I never, ever had to work again. Hey, the industrial age taught me something too – namely, greed isn’t good, but disposable income sure as hell is.
That said, I can clearly see how the rise of robotics, advances in automation, AI and machine learning will all coalesce to more or less reduce headcount across recruiting, with most of us replaced by machines quicker than you can say, “SkyNet.” Here are some of the biggest areas I think this latest tech revolution is going to again displace an entire category of workers – or at least, severely threaten the profession’s long term viability and sustainability.
1. Process Over People: The most obvious, of course, is that any area of our business where distinct inputs and outputs occur – stuff like screening, sourcing and assessments – will largely become automated, with the intermediary role the recruiter plays rendered unnecessary by the ease of candidate and hiring manager self-service. This is just how machines work – put something in, get something out – that is, if we’re not talking about legacy HCM or ATS systems or InMail, of course.
When you can cleanly identify elements of a Boolean String, as an example, and then automatically refine or generate those strings based on previous results, algorithms and historical aggregate data, then that is a process that’s already become more or less automated, even if you’re still building them the old fashioned way. In fact, as technology like natural language search improves with more data and more sophisticated semantic capabilities, machines will easily develop a better command of word associations, related terms and modifiers than your brain is even capable of, even if you’re the best Boolean black belt in the world.
The positive part of this is that we can standardize our processes to better and more objectively assess a candidate’s ability and skills while removing the inherent biases found throughout the sourcing and selection process. And it’s not only impossible – it’s already happening. Just this week at TechCrunch Disrupt (click here for our full coverage over at RecruitingTools), the founder of Siri unveiled Viv, a true machine learning instance which utilizes “smart” technology from a variety of inputs to adapt and learn to individual preference and optimal outcomes. Similarly, HR Tech startup Textio last week unveiled their newest release, which uses previous job listing performance to forecast not only which postings would perform well, but why – and amplify that voice to the candidates you’re targeting based off of 15 million data points.
The future, it looks like, is now.
2. The Applicant Tracking System: Talk about a category that’s going to see some dramatic changes – I mean, these systems have to advance, or else, they’re going to be rendered more obsolete than the recruiters using them sooner rather than later. Think about how easy it is to use Google to find results, and then ask if your ATS is even close to comparable in terms of the ease and accuracy of the results your search returns.
Yeah, not so much. But for you, the recruiter, increased automation will mean advances like dynamic profiling which automatically updates candidate records (which already exists, and is only getting better), unveil previously undiscovered candidates by moving from keyword to semantic search, moving to a standardized resume profile that’s transportable and universal, or even confirming applications or candidate identity via biometrics, such as using your fingerprint to initiate a background check upon submission of application. Don’t believe the hype and ignore the buzzwords. This is what the future looks like – and you don’t need to be an Oracle to see a market rife with disruption.
3. The Employee Experience: Of course, it’s not just how we look for work that’s going to change – it’s how we work, too. The employee experience will be profoundly altered by the rise of AI, whether that’s from changing the way employees interact with customers and each other, where they’re physically located, even what tasks and teams they’re assigned to – all of this can be optimized through machine learning. The perpetual problem with hiring managers – where they don’t trust recruiters to screen and select candidates or submit the right resumes without second guessing or second opinions – will be over, folks.
The blame game and finger pointing will disappear – hiring managers instead will be creating, managing and developing their own pipelines and talent pools, working as end users of HR systems instead of segregated from them in order to directly control the hiring process, leaving them ultimately accountable for recruiting success or failure. Performance reviews, bonuses and promotions will probably be impacted by people metrics like quality of hire for line managers, and consequently, machines won’t be the only things getting smarter about recruiting – our clients and customers will, too. That will free recruiters up to be way more strategic and actually partner on recurring, impactful projects instead of simply filling reqs just in time, all the time, and do so with what’s now more or less a one off transaction (and administrative burden). This should be a win-win for everyone.
Of course, though, like everything that sounds too good to be true, there’s a catch.
Rage Against the Machine.
First off, data isn’t an absolute – it’s relative, even if there are hard numbers and math involved. The outcomes are completely predicated on the stuff we input into these machines in the first place, which leaves human error as a persistent problem which we’re going to have to learn to solve for if we’re really going to get the most out of our machines.
For example, if I told you to define your company culture, you probably can come up with a few keywords or aphorisms (“flexible,” “family oriented,” “dynamic,” etc.) but structuring those amorphous and subjective concepts into structured, standardized data that can actually align what’s on a resume with the right culture inputs is easier said than done. But it’s the data in that’s going to determine success in this new age of automation – as much so, if not more, than the much heralded outputs inherent to the advent of AI in business.
Without the right syntax and structure, without the right raw data and resource alignment, the robots are doomed to fail – or worse, go all HAL on you and more or less go rogue (or more realistically, develop a machine learning disability in terms of analytical efficacy). Performance and programming in robotics are directly correlated – so getting it right even before going online with this new wave of tech is critical – or else, the robots are as doomed to failure as you would be today if as a recruiter, you didn’t know how to adequately or articulately convey your organizational nuances or competitive differentiators in a clear, concise and cogent way.
There’s also, of course, that human element that’s so critical to recruiting: the art of persuasion. Without a closed offer, recruiting has fundamentally failed, no matter what tech you’re using. And no machine will ever be able to do as good a job as recruiters at developing the relationships and repartee with candidates that’s an absolute imparative for getting offers accepted, reqs closed, and business moving forward. Emotional intelligence is still every bit as valuable as artificial intelligence (if not more so, in recruiting at least). I don’t see that ever changing – no machine can ever account for the complexities of human emotion and behavior.
Look at marriages, for example – if we can’t figure someone out after being married for 20 years and have to get a divorce from someone we’ve spent decades getting to know, then it’s unlikely, with apologies to Elevated Careers by eHarmony, that any machine could ever do a better job going any deeper than the same affinity and attraction that drives both romantic and recruiting relationships. Hell, look at men in general.
They’ve been trying to figure women out since we were still in the Garden of Eden, and they still have no idea what the hell we want or need. And despite our many efforts to the contrary, there are just some things, turns out, you just can’t program.