Video – Tech and Talent

Earlier this year I was asked to be on a panel to discuss how Talent teams use tech and how the rise of software has changed the experience for candidates and the companies recruiting them.

It’s a long one, but has some interesting observations from the participants.

The Next Big (Data) Thing: Awaiting the Robo-Revolution

As the quest for the shiniest silver bullets in recruitment continues the fields of artificial intelligence, machine learning and “Big Data” are proving to be a great hunting ground for salesmen and sensationalists alike. To the trained eye these fields are distinct and separate, yet when selling a solution that makes a claim in one of these areas there’s a wealth of content that shows us that our industry is capable of misunderstanding them all equally.  Whilst the misrepresentation of technology by those that are selling it is nothing new in the recruitment industry the coverall terms of A.I and “Big Data” have become imbued with a special power.  Part of this magical thinking has increasingly led to loftier and loftier claims for technology that, though still in their infancy, can be skewed for more clickable headlines.

In journalism there’s an eponymous “Law” for this kind of headline. “Betteridge’s Law of headlines” is an adage that states: “Any headline that ends in a question mark can be answered by the word no”.  It is intended to be humorous but seems to work in the overwhelming majority of examples. “Is This the True Face of Britain’s Young?” Sensible reader: No. “Have We Found the Cure for AIDS?” No; or you wouldn’t have put the question mark in. “Does This Map Provide the Key for Peace?” Probably not.  I propose a similar adage for content covering technology and its application to recruitment and HR.  Betteridge’s Law still applies in this area for example “Does AI Mean the end of Graduate Recruitment?“. Nope, it doesn’t.  What about those headlines that aren’t posed as questions? For those I propose Ward’s Law “The more often an article about recruitment uses terms associated with A.I., Machine Learning, and ‘Big Data’, the more likely the results of any study quoted will overstate the efficacy of the technology“. In other words, the more often recruitment is thought of as being “solved”, the further that will be from the truth.

Let’s look at an example.  Recently I saw this headline “Big Data research predicts which CV’s will be invited to interview by recruiters”, which sounds fantastic! It continues with “New research has discovered a way of telling which CV’s are most likely to be picked out from a large pile of job applications by recruiters.”  This type of press release has become formulaic. Characterised by a claim to the potency of the algorithm, some slightly spurious statistics that don’t entirely hold up to further research or misrepresent the original intent of the research, make some other claims where the algorithm hasn’t been tested but would be an amazing disruption “…make it possible to predict a candidate’s future performance simply by scanning their uploaded CV..”, and ending with a sooth-saying doomsday quote from someone in the industry – “In the future we’ll all be fed by tubes and robot overlords will tell us what jobs to do”.

In this example we are given the sample size a “staggering 441,769 CV’s” and the percentage accuracy of 70-80% when graded against human recruiters screening the same 441,769 CVs. That means that in 88,354 to 132,531 cases the algorithm disagreed with the human recruiters and rejected the candidate. That’s quite a number of false positives/negatives, even more so for any company that values a candidate’s experience and values how applicants might be treated in their processes.  Where this element of humanity breaks down even further is that when given another source of data – a cover letter – the algorithm performs worse, the strike rate falling to 69%.  How many of those 132,531 the algorithm did not invite to interview went on to be hired? We’re not told.  The other aspect of this any many similar stories to consider is that humans aren’t great at dealing with large numbers.The reason for this is that our sense of number is based upon two innate systems which essentially deal with small numbers accurately or large numbers only approximately.  We don’t often encounter large numbers, so when we do, it can be easy to struggle to know if that number is statistically significant.  LinkedIn boasts 433 million members and Facebook has 1.65 billion monthly active users but at this scale those numbers are almost meaningless when applied to the hiring goals of one company. Our inability to connect large datasets with real people is rampant. Big numbers dehumanise us, and the bigger the numbers, the worse the effect. If these raw numbers alone aren’t enough for a little doubt to be cast we can look to those elements that the decisions are being made upon.

Whenever I read about a potentially revolutionary algorithm I’m always keen to understand how it is arriving at its results. In particular in these screening algorithms, what is the programmer choosing to include, what do they exclude and what weighting are they giving those elements on which they are basing those decisions? In this example experience, workplace and education are all measured. We’re also told that “Contextual factors were also taken into consideration, such as ‘did the candidate apply in time’ and ‘was the candidate already employed by the company?’”.  Then potentially more problematically, as alluded to in the full version of the PhD thesis this press release is taken from, demographic factors like “age, gender, nationality, marital status, and distance from the hiring companies” are also included.

This post is to comment on the representation of emerging technology and its application to recruitment, and it’s not my intention to speculate on a possible future of robots replacing humans, but there’s an algorithmic future that’s being neatly swept under the carpet by those who are “pro-robot”.  Research from Harvard University found that ads for arrest records were significantly more likely to show up on searches for distinctively black names or a historically black fraternity.  Research from the University of Washington found that a Google Images search for “C.E.O.” produced 11 percent women, even though 27 percent of United States chief executives are women. (On a recent search, the first picture of a woman to appear, on the second page, was the C.E.O. Barbie doll.)  Google’s AdWords system showed an ad for high-income jobs to men much more often than it showed the ad to women, a new study by Carnegie Mellon University researchers found.  Those who advocate a perfect future will have to confront this research and much more like it.  Whilst it’s often cited by overzealous salespeople that algorithms based on data are free from bias, software is not free of human influence. Algorithms are written and maintained by people, and machine learning algorithms adjust what they do based on people’s behaviour.  All this is even before a well meaning but industry-novice programmer opts to include factors like “age, gender, nationality and marital status” which are explicitly protected in discrimination law. Would an organisation deploying such an algorithm to sift candidates have to expose how the selection was arrived at?  Would candidates still be afforded the same protections?

The problem here is that programmatically applying a simplistic model doesn’t allow for any degree of nuance, and when we’re seeking to measure humans, nuance is everything. Sorting and ranking algorithms for stock in a warehouse have a great advantage over those that seek to catalogue people, and books on a shelf or a can of baked beans in a supermarket don’t have the free will to opt out of the process at any time, but humans do.  Historically, humans have opted out of over-automated processes. I remember a UK bank luring customers back to the fold with the “promise of no automated call centres” and several websites offer the opportunity to “talk to a real person”.  For those companies unwilling to interact at these early stages there may come a time of reckoning when candidates opt instead for a more human process, and not to become a human to be processed.

So how did we get here?  Why is it that the future is either an electric nirvana or a desolate dystopia? Like a lot of science reporting in the media the rise of technology is held up as a scare story, a robo-bogeyman to frighten HR.  Uniquely in the world of HR and recruitment a wealth of the content on the rise of technology is written by those who are selling the products. We have a discourse owned by the vendors, and an audience that doesn’t want to, or hasn’t invested the time to learn about the tech.  It’s no wonder that somewhere in the middle of all this, there is misunderstanding, acceptance and skepticism, and for some money to be made in this new wild west frontier.  For the rest of us there is plenty of content filled with wild claims and spurious statistics, you might not agree with the findings of studies of the claims or the vendors, but I’m sure someone somewhere is ready to tell you that “60% of the time, it works every time”.

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Podcast – Innovation and The Future of Recruitment

Another podcast, and a return to talking with the brilliant Matt Alder.  Matt continues to compile a series of excellent perspectives on recruiting and HR called Recruiting Future, you can listen to the whole series here.

To listen to me try my hand at some futurism and badmouth clumsy attempts to use Snapchat for Recruitment hit play below!

Data and HR – Numbers are Nothing Without Insight

Managing employees and an HR function requires a more holistic approach than exploring issues from the surface.

People use statistics as a drunk uses a lamppost – for support rather than illumination. – E. Houseman (1903)

Our online activity has meant a ubiquitous lens is shone on our lives where the accessibility of data leaves leaders believing every metric can be measured, compared and leveraged, contributing to a curious new world of seemingly ‘crystal clarity’ that was seldom thought possible. The problem, however, arises when data collection occurs without the insight required to take contextual influences into account. Leading the way for deeply consequential misinterpretation and misjudgement to arise. This ubiquitous lens, unless harnessed correctly, stands to confuse as much as it will clarify.

For me, context is the cornerstone of moving from data to insight in a human way, which will clarify, rather than confuse. Nowhere is this more important than in the HR function, where the source of data; humans, operates in a fashion that led author Phillip Lieberman to label us as the “unpredictable species.”

This is best illustrated by highlighting a data source that is predictable, like books in a bookstore. For the bookstore, doing inventory is easy. The metrics needed are things like how many books are on the shelves and how many have been bought. Once a book is bought, there are no external factors influencing its behaviour not to be removed from that shelf. The book doesn’t decide that its commute is too long, or that its wife no longer wants it to work at its company, or that its pay packet isn’t satisfactory. Thus the likelihood of it leaving the bookstore, (unless I am seriously overlooking something) for any reason other than it being bought or stolen is highly unlikely. This is because the external factors surrounding it are predictable.

Humans on the other hand are a different story. Aristotle wrote that we are “rational animals” pursuing knowledge for its own sake. We live by art and reasoning he said. Whilst I’m sure many HR professionals would love to see their employee population be as predictable as books on bookshelves, myself included, the multiple external factors that make humankind human are unlikely to change soon. Which makes producing blanket metrics and chasing large numbers such a dangerous game, because in doing so, the individual is naturally rejected. It’s like saying “we have increased our likes on Facebook which means that we are doing better as a company.”

All that we get from this is a top line number. What do you actually mean by this? What does this equate to? If these questions aren’t followed up, then where does the return on investment come from? Is this useless? This problem becomes even more salient when you consider that HR systems for the most part aren’t built in ways that enable contextual data to be factored in. Nor will a majority HR systems allow data to be exported for further analysis, leaving you stuck analysing the top line, leaving retroactive approaches to remain.

Until contextual factors can be included within HR systems and data analysis, I personally believe HR isn’t critically equipped to have a discussion around data at all. Instead of building a data set from the ground up specifically focussing on important questions we want answers to alongside the external factors that influence them, we tend to take a retroactive approach by attempting to find data to support new questions. The same applies for the big data phenomenon (which by the way, unless you are Facebook or some other such platform are unlikely to truly have a big data set). Gartner’s definition of big data is “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” Playing with data sets of this nature without taking into account the contextual factors that directly influence decision-making processes as they happen will ultimately lead to skewed results and misinterpretation.

So my advice to those individuals taking the data approach is simple: ensure you keep the human in human resources. I am not ruling out this approach whatsoever, however ensuring HR professionals take a step back is key, especially if we are to glean the results required that are free of bias.

“You’ve got to have an Algorithm!” A Recruiter’s Guide to the A-word

Even if you only have a casual interest in the world of hiring it’s not long before you’ll encounter someone touting a magic bullet, a panacea for the “broken” world of recruitment globally.  Currently riding high on the buzzword bingo cards of these self-styled saviours is the humble “algorithm”.  This isn’t the algorithm you might know from maths or computer science however, the “algorithms” of recruitment are like a sorcerer’s incantations able to transform the world and imbue those that utter the word with sage like prowess and a cloak woven from the finest of Thought Leader hair…

In other disciplines the word algorithm is well defined (a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer), yet despite this there is a shift in understanding when we talk about algorithms for recruitment.  Because the discourse of recruitment is largely owned by those who wish to sell to practitioners the humble algorithm has become the magic in the box that’s too difficult to explain.  Algorithms are the elves who make the shoes whilst the shoemaker sleeps.  It’s because of this lack of understanding that each new tool that’s presented to HR, from personality quizzes based on dubious pseudoscience to true recommendation engines and all touted as having an algorithm with little or no distinction.

For the ease of understanding an algorithm is a set of well-defined instructions for carrying out a particular task. It must be sound and complete. That means it must give you the correct answer and it must work for all cases.  Usually, an algorithm is predictable, deterministic, and not subject to chance. An algorithm tells you how to go from point A to point B with no detours, it doesn’t stop to look at the flowers or to consider other factors outside of it’s available data along the way.

Does that sound like a recruitment process to you?  I’d struggle to find a recruitment process that supplies a “correct” outcome, and the notions of sound and complete aren’t interchangeable between organisations – what makes a developer a great fit for one company might be less relevant for yours. Recruitment processes are subject to chance and to that perfect serendipity of the right person available at the right time.  When a recruitment process is good for both the company and the candidate it bears the unique fingerprint of the culture of the company that created it.

All this must be very disappointing for those fans of buzzwords but fear not! Here’s a new one for you! Whilst I don’t believe in an algorithmic approach outside of tools that aid human efficiency there is a way to describe the recruitment process and still get the reflected glory of using a lovely big word.

H-Bomb

heuristic is a technique that helps you look for an answer. Its results are subject to chance because a heuristic tells you only how to look, not what to find. It doesn’t tell you how to get directly from point A to point B; it might not even know where point A and point B are. Heuristics are a practical methodology not guaranteed to be optimal or perfect, but sufficient to accomplish the goals required.  Examples of the use of this method include using a rule of thumb, an educated guess, an intuitive judgment, stereotyping, profiling, or common sense.  In effect, a heuristic is an algorithm in a clown suit. It’s less predictable and it’s more fun.

There are many reasons that the application of technology can make recruitment so much better for all involved but the miscalling of the effects of these improvements won’t help the industry or recruitment as a discipline.  The exaggeration of the effects, range and successes of the “algorithms” is hyperbole at best and at worst a thin veneer, attempting to add shine to the same old business practices shunted online.  The majority of recruitment success stories that herald algorithms as earth-shattering go on to describe a single section of the overall recruitment process being automated not the utopian future in which we are awarded jobs by are robot overlords as they seem to suggest.

Remember that a first round online screen or adding an automated stage into an existing process is less advanced and has less effect than the introduction of the water frames and power looms of the Industrial Revolution, despite what the hyperbolic headlines will tell you.

In the blind solutionism of the HR Tech vendors and those who seek to build personal profile for their technical leaps-that-aren’t, there are real dangers.  Not least of all at risk is the candidate experience, the diversity of our organisations and the objectivity to think critically to improve these tools further.  That’s a high price to pay for the acclaim of an attention grabbing headline.

There are a great number of fantastic HR Tech tools and new ones are arriving all the time.  It is the skill of a modern recruiter to know when to utilise which tool, at what time, to have the maximum beneficial effect.  In seeking to replicate the processes of others or glorify ourselves for our own successes we aren’t embracing a bold new technological stance we’re contributing to the “broken” world we look down on.

Distopia

How to be Happy – Time to call off the “Engagement”

“Yes that’s right, we had a one hundred percent response rate to the survey.”

The speaker from a international airline proudly stated his results. His lovingly compiled pie charts labelled “Engagement” were resplendent in the Power Point behind him.  The audience were incredulous, a one hundred percent response rate how was it possible they asked?

“…well, of course, we made the survey mandatory”.

Whilst there are many different definitions of “Engagement” in the HR world broadly speaking and employee’s commitment to and involvement with their work and their organisation seems a decent place to start.  Like most HR initiatives the will to measure engagement starts out as well intentioned and a move towards concern for the employees themselves.  However, too often, in the application and the building of processes around this the notion of “Are our employees OK?” has been replaced with “Are our employees productive?”.  The change is a subtle one and has occurred as the results of surveys have been used as the answers to questions for which they weren’t intended.  Engagement, when measured effectively can be broken down into constituent parts.

  • Intellectual engagement – Does the person feel challenged in their work, are they thinking.
  • Emotional engagement – Does the person feel positive about doing a good job, is there an efficient emotional reward structure in place.
  • Social engagement – Does the role facilitate positive interaction with their co-workers.

When used incorrectly they are answering different questions.

  • Productivity – Is the employee producing the requited amount of labour that the company expects
  • Performance – Findings of surveys here are used as a proxy to explain results.

The shift is small and goes beyond just the semantic. For one set of employers “engagement” is now about measuring what we can “get out” of a workforce.  Like many other terms the human aspect has become distanced and now the people in the organisation can be reduced to a resource, counted and catalogued accordingly.  Even the manner in which these surveys are conducted can affect the results.  An annual engagement survey sent to everyone in the organisation is little more than the pointed stick poking at the bloated corpse of your organisation’s apathy.  Everything they measure has already happened, it’s post-mortem and the changes it would be possible to make are already too late.  A few click boxes on a website once a year is a chore not a meaningful interaction, regardless of the best intentions those questions are compiled with.

Some forward thinking companies have found a better solution.  In 2003, Fred Reichheld, a partner at Bain & Company, created a new way of measuring how well an company treats the people whose lives it affects—how well it generates relationships worthy of loyalty.  His Net Promotor Score or NPS was widely adopted and in use of companies of all sizes, segmenting the people into Promoters, Passives and Detractors with it’s simple one or two questions.  There are a great deal of benefits of adopting this approach and adapting it for employees.  When creating an eNPS (employee NPS) the annual laundry list of questions and ratings is replaced with a more frequent check-in, trends in happiness can be linked to real changes in the environment.  An entire team’s mood seems to be changing? Maybe it’s that new office space? Maybe it’s that new manager? Waiting 6 months to a year later to issue a survey is all too late.

ENPS-eng

So engagement surveys are too little too late and misused tools for measuring productivity.  eNPS is an improvement but in it’s attempt to be as answerable as possible still misses some of the larger aspects of the employee experience.  Some companies seem content to only measure those periods of time they are extracting effort from their employees, ignoring completely the fact there are external influences that might occur after 5pm.  The current toolset of HR is ill-equipped for the reality that the productivity and performance are great to measure but just as important are those things that employees themselves want to get out of work.

There is an alternative that more progressive organisations are adopting and in doing so re-humanising the process of collecting this data.  Instead of asking if an employee would recommend a place of work or waiting a year to prod at them with a laborious survey they ask a one question daily.  “How happy were you today?“.

Happiness at work and employee engagement are similar ideas but have unique and subtle differences in meaning.  Imagine you are managing a team and told to make them “more engaged” it might sound like a request for more meetings, for incentivising longer hours or an edict to start “cracking the whip”. Compare this with a manager tasked to make their team “happier”, this request isn’t about driving productivity. It feels more like a search for ways to empower the team, remove obstacles and better motivate them.

There are a number of new tools that seek to give a better insight into this broader question, at Forward Partners we use the MoodMap tool from Happiness Works. The tool asks the single question “How Happy were you today?” every day at 5pm.  I can answer in one click of the mouse.  Monthly “Climate Studies” probe deeper and allow more insight but not at the cost of provoking respondent apathy or the feeling that it’s all “too little too late”.  Better yet the tool allows respondents to offer “ideas” for improvements in the workplace.  In our use of the tool it’s been interesting to note that these ideas mirrored almost perfectly sections of Maslow’s hierarchy of needs.  The first suggestions were physiological or environmental – the air-con is noisy, my chair is uncomfortable etc.  In fixing these small, nagging, yet solvable items the “ideas” we’ve captured have evolved too.  There have been ideas for conference attendance, skill sharing, training budgets and social outing suggestions.  How’s that for “engagement”?

moodmap.io example

No software tool is magic and whilst it has been incredibly beneficial perhaps the biggest benefit is in facilitating the conversation around employee happiness.  For those companies looking for a way to gain insight into their employee’s well being and then empowering them to improve themselves and their environment these tools could be a great way.  Of course for the rest of you you have about 12 months to sharpen that pointy stick, or maybe wait to get that insight at the exit interviews?

pointy stick

The Mis-Match of Algorithmic Recruitment

It’s the not so distant future.

A mobile app linked to a wrist mounted wearable wakes you, at precisely the right moment.  It monitors your sleep patterns and pulse rate and greets you each morning with a chipper “Go get ’em!”.  You dress and get ready to leave the house, the fridge has emailed to remind you that you’ll need to buy milk on your return.  You lock the door behind you with a swipe of your cell phone, keys are no more.  Outside, you step into a self driving car and take a different route to the usual commute – the car knew about the traffic before you did.  You arrive at work and boxes are moved into the previously vacant office next to yours.  You weren’t aware of a new co-worker. There were no interviews. They were algorithmically selected from the passive talent pool.  Kept warm on a diet of Pinterest photos of the office and Youtube videos of kittens selected to be the most humanising for the Mega Corp you happen to work in…

As far as predictions of the future go the vision I offer above is hardly advanced.  The technology exists for the wearables, the Internet of Things and the self driving cars, it’s just that last part that seems incongruent.

In the growing adoption of technology for HR departments seeking to differentiate their sourcing efforts, the idea of algorithmic matching is seen to be the magic bullet in the “War for Talent”.  Beyond the clichéd war metaphors and gullibility of HR Tech buyers is the future of recruitment to be left to the robots?

Technology has made the discipline of talent acquisition better.  We’ve moved far beyond the data entry and green screen databases of a decade ago.  As a modern workforce migrates to online services so their digital footprint increases making them all the more easy for the new breed of sourcers to find.  Now the future, according to some, looks set to be the automated addition of new workers and a touted increase in the skill of selection.  I’m no Luddite but I can’t help thinking this is a version of a technological utopianism whose primary supporters are those that seek to benefit financially from the adoption of the technology in question.

So many of the products available that claim to have solved matching are the same providers who don’t recognise some of the fatal flaws that their products exacerbate. The primary example of this is the reliance on the quality of data on both sides necessary for a match.  The majority of matching systems are parsing CV’s and then matching against a job description analysed in the same way.  This is exactly the limited key word matching that these systems say is so weak.  Even when other data are relied upon to beef up the input, suggestions of LinkedIn profiles and even LinkedIn endorsements are laughable. Especially in the case of unverifiable LinkedIn endorsements like mine for “Midwifery” and “Cheese Making”.  Of course I’m totally brilliant at both of these things…

Even the more advanced of the matching algorithms that incorporate some elements of semantic search (context of search, location, intent, variation of words, synonyms, generalised and specialised queries, concept matching and natural language processing) are constrained both by the data the candidates provide and the job description or criteria the employer matches against.  Anyone who works in recruiting will be able to quickly see that both of these sources of data are flawed and subject to constant change.  Data in both these areas can be knowingly falsified, incomplete and always out of date.

This data is inherently flawed because people themselves are inherently flawed.  Candidates will always seek to portray themselves in the best light, hiring managers will always add some extra “nice to haves” or even make the work of two people into one mythical job description.  A matching algorithm is forced to make sense of too many moving parts and results will suffer.

In moving towards this style of recommendation the people in the processes are reduced to the status of commodities.  Subtle nuance is lost and the chance for innovation curtailed by inelastic parameters.  People are not a product.  When Amazon presents you with a book based on your buying preferences it has only to reckon with your fickle, transient tastes.  A book doesn’t reject you because it feels it’s too far to get to your house, or because the other books on the shelf don’t feel your reputation is strong enough, a book doesn’t want to work from is own home or have a counter offer from a series of rival readers…people do.

Recruiting is a constant stream of edge cases.  Whilst a matching engine might work for less complex roles at large numbers, it won’t help you compete in winning that all important “War for Talent” you were so desperately spending your way out of.  The current level of technology is no match for the ability of a good recruiter.  This is not an indictment of the technology, it’s an acknowledgement of the greater problem that exists in the institutionally flawed HR departments and Recruiting processes the world over.  Using a tool like this to gain another datapoint to inform decision making is a valid use – it’s the shame of HR Tech that every new tool is paraded as “the answer”.  If the industry could wean itself off it’s obsession with the novel and shiny we might be able to tackle some of these issues at the root cause and realise that the skills we learnt whilst toiling at our green screens might not be entirely redundant.

The Abusive Relationship between HR Technology and its Users

A green screen flickers in the corner of the office.  It is “The System”. Management don’t understand “The System”.  It’s a confusing, alien world.  The bright horizons of technological advance leave those that guard the old ways of working squinting in the glow.  As time moves on the piles of paper and files are replaced with computers and newer instances of the same system.  Functionality moves forward, no longer the electronic filing system, now the system has snaked it’s way into all aspects of the HR world.  The system knows when you arrived, you tell it when you’re going on holiday, it knows you got married, it knows about your children, it will will auto-generate your P45 and alert security to escort you our of the door.

Whenever I happen across an organisation that uses one of the “traditional” HR systems it’s never long before the discussion turns a little Orwellian.  I never hear these complaints from the management tier of the organisations – just those that are forced to interact with an outdated system that has been imposed upon them.  As Human Resources became more computerised, efficiencies were created at the expense of those very same resources it wished to aid – the humans.

The biggest offenders of the dehumanisation of HR Tech are those systems that started life in the minds of the suppliers of manufacturing technology.  If an HR system is has at it’s heart the basic stuff of a supply chain management system is it any wonder that your employees will feel used by the system as opposed to valued or better in control of it.  Of course this doesn’t just extend as far as the end user.  Limitations of a poorly implemented HR system can shape or even change HR policies themselves.  You wanted to give that amazing maternity leave deal? Sorry, the system doesn’t support it.  Wanted to award industry beating compensation tracking? Computer says “no”.

Technology in the human resources department became an ivory tower.  The situation worsened as technology advanced in the outside world.  Far from the gaining efficiency technology in human resources forces people to retain knowledge of arcane systems, to manage decaying programming languages and become beholden to dead data structures.  Locked into vendor licensing agreements and having to deal with clunky technology everyday Stockholm Syndrome sets in.  Gradually HR departments began to become more and more like the broken systems they used.  How many HR departments administer to the people they used to represent solely through a system. How many of us have tried to talk directly to someone who works in HR only to be referred to a different part of system.  In building the one-stop shop for everything HR would need, solution providers didn’t stop to consider the the knock-on effects – the people processed by the new breed of catch-all technologies are left feeling empty and embittered.  How many employees have come to resent their colleagues in HR because of the way they are forced to interact by poor software?

The provider of the solutions and those that buy the solutions are in a race to the bottom.  They seem to go to great lengths to alienate both those who try to use the software and those who receive a service via it.  In the ongoing dance between supplier and buyer of HR Technology the dance floor is left all but empty for the minority, whilst the majority stake holders, the users and those that are used, are left un-consulted.  The problem here is a “perfect storm” of wrongheaded software production with a manufacturing bent meeting a buying audience that seem to be wilfully technologically un-savvy.  The buyers of software in human resources are always looking for the new and the shiny, this trend is particularly pronounced in the sphere of recruitment where the improvement is always incremental yet the added value sold to the buyer is always exponential. Is there ever a new recruitment tool that promises an “edge” rather than a magical world changing experience. The naivety of the buying audience allows sub-rate suppliers to peddle hyperbole driven claims like arms dealers of solve-all magic bullets.

How many of the HR buying audience have decided on purchases for less than optimal reasons.  How many of those would candidly admit to having wasted their budgets afterwards?  In my career to date I have used some terrible software that I’ve had to use because of weird purchasing decisions and I’ve heard some terrible reasons for it’s purchase.  “The salesperson used to work here”, “The HR Director knows X from the supplier”, “We held a review and they presented better…” – all lousy reasons, and in all of these cases the person who made the buying decision had very little interaction with the system after the purchase.  The self fulfilling prophecy of imperfect software being purchased for suboptimal reasons continues, locked in, hostages for the term of the next license agreement.

In striving to produce ever more sparkly baubles for HR Directors to purchase in their quest to appear relevant, software producers increasingly look towards other domains and piggyback on their “buzz”.  How many solutions in the HR world are now sporting the reflected glory of “mobile”, “video” or “social” as a reason they will offer increased benefits?  Recently we’ve seen a spate of Tinder clones for recruitment. “Machine learning” solutions who’s matching algorithms seem to be attempting to solve the problem of having hired bad recruiters. Even video interviewing platforms,  because video is the next “big thing”…after all it worked so well for all those cat videos on YouTube.  As Jeff Goldblum’s character said in Jurassic Park “…your scientists were so preoccupied with whether or not they could that they didn’t stop to think if they should” – we’re at a stage where any technological advance is seen as something for recruiters to exploit.  Want to know if a recruiter understands “social”? If they show you all the wonderful work they’ve done with Pinterest and Instagram, they don’t get it.

There is some light at the end of this dark and scary tunnel.  A handful of suppliers are producing software that is not only good for recruitment and HR but good for the users too.  Software at it’s best in HR is responsible for the removal of a lot of the pain of processes, procedures and regulation that would normally cause friction.  A great software solution removes the burden of repetition, it gives momentum and doesn’t detract from HR doing what they used to best connecting with and advocating for the people they work with.  There are some suppliers that understand that HR Technology doesn’t have to be ugly. Using it doesn’t have to leave you feeling miserable and depressed, there are even some suppliers who are making their users lives easier.  There’s the frictionless importing of candidates into the Workable ATS using a Chrome plugin, there are an increasing number of beautiful calendar apps incorporating to do lists that scale to support entire companies and there’s even the easy way to do expenses using apps like Concur or Expensify. The difference is that there’s a great tool for each stage not a mediocre tool for all stages.
The growing fragmentation in the marketplace has allowed for smaller suppliers to enter and give us some true innovation.  I can only hope this also means that the clunky mega solutions of HR history don’t have to be inflicted on many more employee populaces before buyers see the light.  HR departments should realise that whilst technology is the great enabler, when it’s old and outdated it’s a great alienator.  Employees have access to better hardware and software than their employers in many cases and this isn’t tide going to reverse any time soon.  The technically savvy HR managers will win the respect of their organisations or be doomed to lose employees to those that do.  The days of “hired to retired” cradle to grave style bloated solutions are over.  Using the right tool at the right time and having the courage to change that tool if necessary is becoming more and more important.

In October I’ll be attending the HR Tech Europe 2014 European Conference in Amsterdam and I’m looking forward to hearing about the future of an industry which is at a turning point.  The old vendors will be there no doubt, but I’ll be looking for the innovators and the upstarts.

Why the Recruitment Revolution won’t be sparked with Tinder – Candy Crush for your Career?

The world of HR and recruitment software seems to be going through something of a renaissance as of late.  The world that was dominated by user-unfriendly bloatware is becoming increasingly fragmented.  As more players rose to fill the gaps in usability for a beleaguered audience so smaller competitors rose up too.  For a small provider or startup, HR is a domain ripe for disruption.  It bears all the hallmarks of an industry that at it’s surface looks unchanged.  For the founders of startups who may have been at the unfulfilling receiving end of so many HRBP’s in larger organisations HR is a logical starting point for your new disruptive software solution.

In the mists of history where HR met software has only led to monolithic structures or rebrands of logistics software. The people in these electronic processes treated in the same way as stock to fill shelves or car parts for an insatiable assembly line.  The same clunking UI that held payroll information for accounts and performance data for HR was rolled out and forced on recruiters for managing the applications of new candidates. The biggest competitive advantage was the supposed “ease” of managing a candidate process.  In effect this led to a system in which people applying to large organisations were held at bay with template emails and auto-responses.

There are a great number of new systems for managing recruiting in a way that is more effective.  If you’re still managing the hiring process for your organisation in a “spreadsheet of doom” now is a great time to change to one of the newer systems – Greenhouse, Lever or my ATS of choice Workable are all enabling their users to manage applicants through the process in amore human way. (Provided you use them in a human way – template emails that sound like template emails still suck).

To match the rise of the new round of applicant tracking systems (ATS) we’ve also seen new tools for other areas of hiring.  Recently we’ve seen large rounds of investment for many mobile based “job discovery” tools.  They all have the obligatory cool names like Jobr, Emjoyment and Blonk. The trait these apps all share is their appropriation of the Tinder style user interaction.  Like a job? Simply swipe and you’ve applied, or at least made contact with the posting company. It’s so easy!  And that’s my problem.

“It’s a Match!” …but does either side really care?

There are enough problems with application processes that are too lengthy but to remove or lower the barrier to application to a simple swipe, by extension, must also lower the thought process behind the application.  Does scrolling through job listings on your phone equate to the same thought and consideration on the candidate side as seeing an advert, being taken to the companies website to learn more and then making an application?  There is an innate disposability in the action of a single swipe, there is little effort either physically or mentally in idly swiping through career options.  As a recruiter, I want more than that.  I don’t want the company I work for in a beauty parade held up for the swipes of someone looking for a Candy Crush Career…

Whilst the act of application, that is expressing interest in a job via one of these apps or polishing a LinkedIn in order to apply, fulfils the basic criteria of “job seeking” it does seem to overestimate the impact of technology on human behaviour.  The “ease” of use for the candidate is the equal and opposite reaction from the Recruiter side who is now given over to service of a greater number of applicants that haven’t really gone to the lengths of application they normally would have.

There are a greater number of applicants and it becomes all the more difficult to find the signal in all that noise.  Those who are not at the coal face of recruiting often tout an increased volume of applications as beneficial.  As if throwing more bodies into the top of the funnel will result in the same level of quality and increased output from the same recruitment team.  Whilst this can be true it’s only true if the quality is maintained. Scaling a recruitment effort is much more than opening yourself up to more applications. The best adverts for vacancies should cause potential applicants to opt in or out and gauge their own cultural fit.  The worst metric for the success of any recruitment effort is the raw metric of applications.

Perhaps at the root of all this is the transient psychology of a Tinder swipe. People are time deprived and the application of the swipe to jobs seems like a saving but in effect shifts a burden to a recruitment function that will only truly engage if they too swipe your application.  Monotonous, machine like swiping. Less and less meaningful engagement. Just as Tinder was a nail in the coffin of notions of romantic love perhaps Tinder-clones for recruitment are just at odds with my romantic views of candidate experience?