How Blockchain and AI will disrupt recruiting — and HR will help to let education become the most attractive asset of a society
This article is having a look into current recruiting strategies and analyzes the challenges HR will face in a not-so-far, heavily blockchain and AI-infused, future. But it will also show a possible path that leads to more fairness, productivity and prosperity for all participants — by giving education an operationable value and makes life-long-learning an attractive opportunity.
The following thoughts started with a newspaper article I came across about a year ago. My local government needed to decide what to do with a free piece of land: building a school or a shopping center?! The amount of young students in my area is constantly raising, but for all the planned education projects the municipality needs first money, which can be best accumulated by local business tax… Not an easy decision, though. But what frustrated me the most was the fact that there is even a need to balance between those two things. Why is our society unable to appreciate the long-term value in good (qualitative) education, why do we need to measure it against a system that rules out everything simply by scaling quantitatively?! It is time to give education an operational value that outpaces every other concern.
Hiring with Heisenberg and the human factor
Let’s have a quick look into today’s hiring processes first. Since we luckily overcame peonage, and education generates a more and more diversified class of experts these days companies are forced to optimize and restructure the way of finding talent. Well, at least they should.
Beside all 7plus-step-recruiting-journeys, assessment centres and behavioral-based candidate screenings the process itself remains highly inefficient (money and time-consuming), unfair (heavily biased) and unsatisfying for everyone (due to, also law-induced, intransparency). Here HR is striving to hit the jackpot (means hiring the perfect person) whilst just playing with a limited set of numbers (the right one may just have missed the randomly set deadline), there recruiters spurn the company values they commit to hire to and force candidates to negotiate their salary over their personal “pain threshold” — “since you never know if you buy the pig in a poke”. And by trying to nail down fluid parameters recruiters and hiring managers often ignore that they are themselves a central piece of the puzzle for success or failure of a potential onboarding.
Some may say, recruitment today is more about hiring the person with the right soft skills, not so much the actual abilities. But would a right fit for company A then ever be a misfit for company B? Are there recruiters outside who search for people shy of team building, weak in communication or capitulating in front of problems? Really?
All nice perks and promises (some mix it up with actual company culture) cannot cover the actual purpose of a hire: to solve a company’s problem — ideally in the way the company wants. The construction of values around is simply a compensation for the high opportunity costs for not hiring another candidate who would find a better solution for the next problem (and of course the limitations due to labour protection policies).
But the trend is undeniable. Temporary workers and freelancers are on the rise — and create a competitive advantage for companies who are already shifting over to the so-called “gig economy”. Nasdaq expects 43 percent of the U.S. american workforce to be freelancers by 2020 (in two years, just to get it straight). These problem-based, temporary hires of more and more decentralized network corporations, which were already described in the last article, will exhaust what is currently the strongest factor in the recruiting industry: a personal network of trust. Having someone who is putting a word in for you makes it still way more likely to be hired than just sending over the CV.
Sounds like a perfect attack vector for Blockchain and trustless trust…
A vision for a decentralized You-know-which platform
Last month I received invitations for two new platforms (dock.io and inbot.io) that want to utilize my network to build trustworthy recommendation engines and calculate matches by using artificial intelligence. And in both I, as a networker with the luck to meet some fellows in my business environment, can earn tokens (money) simply because I know people.
It sounds like an odd concept, but it is just the beginning of a redistribution of the benefits within the platform economy. LinkedIn and similar services live from those networks that we are building (and giving away for free), their matching services, recruiting algorithms and job ads are more successful because they are able to connect the dots we deliver. And this is just the obvious part: By asking for endorsements, recommendation letters, interests and even likes they track down our potential fit to a certain job offer. The only problem: All those information in the hand of one company (named Microsoft)? For free? You may guess it, also here there are already some blockchain services striving to build ubiquitous reputation networks — and let people earn tokens (yes, money) for it.
We need to realize one thing: The data points we (co-)create are our capital in the information age. The big tech companies understood this a while ago and developed their models around this, but lastest after the Cambridge Analytica issue or the GDPR implementation in Europe the public becomes aware of the power of data. And yes, if you are a skilled expert in one industry branch and you have studied and worked in this field, it is very likely that your connections and your recommendations of selected people fills open positions much faster and more efficient than any classic recruiting process possibly could. Wouldn’t it be just fair to get also paid for all the effort you spent into building this network?
For now it still sounds a bit abstract, so let’s have a little example to show how it works. The beginning sounds very familiar: Peter is a highly skilled expert, one from those that are in high demand (as you always read). He’s not actively looking for a job, but he followed the invitation from his friend to check out this new dApp, which promises that none his sensitive information will ever leave his phone. So he adds his contact list and CV and connects his diplomas and certificates (which are validated in the blockchain). Soon the peer-to-peer connected (former) co-workers and customers start endorsing or writing recommendations, the network value grows steadily and eventually the AI in the dApp is creating a profile of Peter’s hard and soft skills.
Now the twist: One day the AI of company X suggests to hire a person with skills A, B and C, since these qualities helped to solve a similar issue in the past at a good rate. So the company is offering a bounty (actually two: one for attending and one for finishing the job) via smart contract and minimum match percentages to be achieved to be able to claim this job. And the dApp recruiting algorithm start to ask available clients (without exposing the owner or their information directly) about the fit to the open position. Let’s now say, Peter scored a 95 percent match. Only then he got connected to the job offer and can decide to express his interest. The job issuer is happy to get a fitting candidate and releases the bounty. Here is where the network pays off: The AI “remembers” who contributed to the result that Peter got hired — and a little share of the bounty is paid to the inviting friend, the ex co-workers and the customers who rated him. The AI even pays a small amount to everyone who is highly skilled in the same field, first to appreciate competition and to reflect the demand in the market, but second to make sure that people like Peter develop further (which ensures the self-sustainability and growth of the matching system). Just imagine, you are a scientist with the best skill in rocket fuel engineering in a university. You will get rewarded every time another engineer is hired in the upcoming space race. And the more you learn, the more appreciation you receive.
This service will be more efficient (no time limit, no endless scanning), fairer (personal, subjective impression and valuation is simply switched off) and way more transparent to everyone (by offering full data protection). You may ask: Why should the current recruiting industry move to this type of platform, particularly if they have to pay for all of this? Of course they won’t — at least not voluntarily. But when I, as a networker, with my skills and reputation, receive appreciation whenever someone is hired based on what I contributed, I will share my information primarily on those services. And if HR searches for a proper expert (which will be so challenging, as that those experts will actually hire agents themselves to not get drowned in requests) they need to access those information no matter where they can be found.
No random deadlines, no endless screening rounds, no entropic accumulation of information.
And this will be the end of recruiting as we know it.
#disruptRecruiting and the new value of learning
There is not much left for the analog recruiter in times of AI and gig economies — so HR has to adapt. More than this, by owning the AI that coughed up the demand for a certain skill set to solve a companies problem they still hold a powerful asset in the system. Soon we will see specialized consulting/recruiting firms feeding their AI with client’s reporting structures, tasks, workflows, project plans etc. to calculate the ideal candidate. Those models will learn across company structures, complete industries and eventually even the potential outcomes of different types of diversity setups in teams. Of course those recruiting companies get paid by performance. Only if the expert solves the tasks as demanded, they receive their affiliation payment (Hiring clients: make sure that you don’t give this information for free…). Just imagine the arms race between corporate AI recruiters and the agents of the experts…
But how is all of this helpful for my municipality and their school vs. mall issue, you may ask. Here it comes: The (imagined) mayor of my area is a clever woman. She knows, that her system is feeding this race, since she’s in charge of the education here. And she is a blockchain enthusiast, so she decided to issue KnowTs (KnowledgeToken) to everyone who learns. And when a company wants to hire the experts living in her city they need to buy (and then burn to execute the smart contract) a certain amount of KnoTs in order to be able to issue the bounty / contract to our expert Peter (in our example above). Bottom line? Everyone benefits!
The mayor of my town can favor a school over the shopping center. And companies benefit from this expert knowledge produced in the system. Thanks to #nextgenHR.
Wasn’t there always frustration about the flaw in the concept that private companies benefit from the public spendings into education without giving a measurable equivalent back into the system? Solved by #nextgenHR.
Wasn’t it a big problem that university graduates start with a huge pile of debt into their expert career? Luckily they can make “money” from selling their tokens right away and so even consider starting a company on their own. Enabled by #nextgenHR.
My favourite:
Wasn’t the biggest question of our society that everyone talked about the necessity of life-long-learning in the age of AI, but no one actually knew how to make it more attractive than just to surrender and live with the benefits of a UBI?
There you go.
Of course, this model isn’t perfect. And no one knows what the future will hold. But it is good enough to start the conversation what kind of activities and tasks we want to value, what kind of society we want to nurture, once AI/automation take over the boring, lousy jobs…
