There is increased speculation that artificial intelligence (AI) will increasingly replace the work of humans over the medium to long term. Already, AI is performing well at the world-class tournament levels in such games as Chess and Go, the latter of which was a major breakthrough. What about actual jobs?
In the long game, they think all human tasks will be out-performed by machines in 45 years. All human jobs would be replaced in about 125 years. So we’re kind of safe for a decade or so. However, there are major concerns about what this change will mean for humanity, as this change may increase economic inequality.
In my opinion, as this relates to workforce planning, the challenge seems most interesting in the transition period. That is, people will get new jobs designing new technologies, and people will make themselves more productive by using technology in the workplace. But there will be more frequent changes, more dramatic changes, and things will happen more quickly.
These changes mean that human resources will be the key party delivering change management, knowledge management, hiring, learning and development, and employee communications. The pace at which people adapt to change will determine success in investment decisions and the retention of engaged customers. But only if you get the metrics right. Anything else, and your organization is sunk.
Within human resources, truth is the first victim of industrial warfare. Does your working day involve manipulation of facts and truth? I bet it does.
Parties to negotiations proffer bargaining positions that are a precise distance from where things are really going. You support the management rights of managers who are clearly wrong. You conduct investigations into discipline cases where the facts are unclear or contradictory. You guard secrets on the tip of your tongue when people blather on at cocktail parties. As you empathize with diverse perspectives, there are multiple truths, things that may be true to one person but not the other.
The game of human resources brings an impish delight that you are at the crossroads between diplomacy, politics, espionage, and psychiatric nursing. If you can suspend your disbelief and stay in the adventure then truth be damned.
Workforce Analytics and the Re-Definition of Truth
But wait… here comes the human resource metrics team. We’re going to screw it up for you.
In order to love spending long hours getting the numbers right, analysts must develop a hatred for incorrect statements of fact. I feel a special kind of disgust when I discover that I have accidentally made a wrong statement. With large amounts of data, there are always errors inside the data I have received. And the more elaborate my calculations, the greater the risk that I have made a mistake, even when I apply good skills.
Between analysts there is a lot of blunt talk about whether formulas, numbers, and facts are correct. If something is wrong, the not-so-secret language of quants includes phrases like “that is false” and “I made a mistake” and “fix that now.” Analysts know how to talk with each other. Then we step into a meeting with non-quantitative colleagues who use very little math and lots of words. Dirty, filthy words, making statements that are incorrect. You know where this is going. Things must be corrected.
These two crowds – the generalists and the analysts – need a common language, an ability to translate words between two cultures. You might have experienced strange conversations involving phrases such as: How important is this fact? Is this really a fact? Is it true or false? Is it important if this fact is incorrect? Oh, look, someone made a mistake! Shall we discuss it with them? How can we talk about this mistake without embarrassing them? Oh goodness, the person who is wrong has the most power; who will talk with them about it? I’ll do it! No, not you. Wait, yes… you can tell them, you’re not a threat.
This dynamic give the human resource generalist an opportunity for an extremely complex skill set. Who in your office is the data translator between truth and context? As a generalist, how good are you at maintaining two mental states, one in which the facts are crystal clear in your mind, and another in which story and posture prevail? Can you pull together a discreet meeting to correct-course on a disputed fact? Can you prevent ill-informed decisions from being advanced, and also keep it quiet about it afterward?
It’s tough work to grapple with poker-faced stares between analysts, subordinates, and superiors while you navigate the rich world of debated facts. If the spreadsheet tells you only one thing that is certain, it is that this time you must do what feels right in your gut. You have been nourished with facts, and now your gut feels right. So put on your game face, and go! Now that’s employee engagement.
Do you know how you’re going to benefit from this screwy economy? There are lots of opportunities to win or lose. How do you make sure you get something out of this world? How do you make sure you land one of the good jobs, and avoid the pitfalls of a fiercely competitive market?
Workforce Data and Inequality by Education
Jeffrey Sachs, the noted economist from Columbia University, wrote a brief overview of the jobs market in the US. Unemployment is near a ten-year low in the United States. This is getting into the news because it kinda looks like Trump’s policies are having a positive impact. But it’s tricky.
What is interesting is the great divide between those with a university degree and those without. Sachs diverts our attention to the more-accurate employment-to-population (EP) ratio which is currently 60.1% “meaning close to three of every five adults is working, still down sharply from the peak rate of 64.6% in early 2000.”
The EP ratio is varied by education level, at 72.1% for those with a degree and 54.9% for those with only a high school diploma.
Sachs notes that job losses in the past decade have been mostly caused by automation and not globalization. He cites the self-serve kiosks at McDonald’s as an example. Policies aimed at saving and creating jobs through restrictions of trade and immigration are “doomed to fail.”
Sachs recommends that America prioritize quality education and job skills, preserving and improving the vital role of public schools. He also recommends America boost the incomes of lower-skilled families through government transfers to break the multi-generational cycle of poverty that discourages skill attainment by the kids. Sachs also points to countries that provide a higher quality of life through better government spending in health care, child care, and other programs, which boost the quality of life regardless of job success.
Public Policy and the Political Divide
I personally agree with Sachs, but I lean that way anyway. I’m well-educated, I come from a union family, and I live in an urban area. I also work in the public sector, and I’m Canadian.
I think the hardest thing for liberals to understand is the opinions of those who are locked-down by hardship and who decline this “expert” help. It’s true at the aggregate level that we should ensure people get help. But at the individual level, people who have actually been poor perceive that overcoming hardship is a case of perseverance, getting out to look work, staying away from high-risk habits, and keeping an eye out for the occasional physical threat. If there are millions of people who have this individual opinion, it bundles together into a block of pro-market voters. The prescription of public-policy solutions seems policy-correct but off-target in the messaging.
It’s hard to make sense of it all. To understand this, you need to know that there are secret whispers amongst all economists that “everything causes everything else” in a big hot mess. It’s still possible to have a thriving economy for strange reasons. Trump could still cause the economy to grow by accident. To what we attribute our personal success or failure is frothy right now, and it’s reasonable to not take anyone’s judgments to seriously, including your own.
The simple question we should ask when a few big winners bring in the gold, is will you get your cut? The winners think they get to keep it all. We’ll see.
On average, you can get a new job making eye contact. That’s because the new technology just can’t get this right. While you brace yourself for massive technological disruption, new business models are emerging where your hands and your heart will guide you through the next era of technology and employment.
Dustin McKissen of McKissen + Company wrote an intriguing article in July 2017 about non-degreed workers displaced by technology. The article is blunt: My Father-In-Law Won’t Become a Coder, No Matter What Economists Say. It’s a great critique, because it gets into the problem that technological change is supposed to be good for us “on average,” a concept that only makes sense to economists. If one million old jobs are eliminated, and a million-plus-one new jobs are created, an economist would talk in terms of a net gain of one job. Yay! However, the one million people who lost their jobs don’t see this change as positive, and they are perfectly entitled to speak as humans who have a voice, a home, a family, and a vote.
I endorse McKissen’s view that this human resources topic is highly political. What does the fast-changing world mean to those who are displaced? While the father-in-law is currently fine for work, the company is encouraging sales staff to get their customers to place orders online. Will that man have the same job, or any job, ten years from now? You see, if there is political blowback from those who are adversely affected by this net-positive change, the voice coming from the dis-employed may affect the viability of our economic and political system. McKissen calls for a new ideology, a new “ism,” that bypasses the politics of left vs. right.
Customer Engagement is Connected to Employee Engagement
I personally think the new ideology is starting to become evident. The idea is that business performance is hyper-sensitive to the work of engaged employees delivering meaningful experiences to engaged customers. For lack of a better word, let’s call it “double-engagement.”
Technology is just something that ramps-up productivity of those who advance the double-engagement experience. The use of wearable technology, hand-held computer devices, and links to large databases and artificial intelligence simply empower the front-line worker. The workers do what the technology cannot: make eye contact with customers, express empathy, display a sense of service, and show responsibility for getting the goods into the client or customer’s hands. Profits, investments, and public policy are just along for the ride, and people who are big in those areas need to stop pretending they’re the boss. This new model can be found in other articles, such as here and here.
It’s noteworthy that McKissen’s father-in-law works in the sale of food. Whole Foods was recently bought-out by Amazon; what does that mean for the future of food shopping? It is increasingly apparent that the retail sector is at risk of being savaged by online shopping. Sure, we’ll still be buying food a decade from now. But how will the food get from online order to a front-door delivery?
The Workplace Culture of Customer Engagements
In an article from the New York Times, there was an eye-opening exposé of the life of those who deliver food after the online order. It turns out that new technology is only efficient until the requested groceries make it to the last mile. In “the last mile problem,” tactile and emotional challenges emerge in a very human way.
The bananas must not be refrigerated, almost everything else must be kept cool, there is more than one optimal temperature for cooling, the milk must be stored upright, and apples must not be stored in a confined area with lettuce. Each hour of delay in getting the groceries to the customer eliminates one day of shelf life. The traffic is unpredictable, the parking rules are unpredictable, and there is physical effort to getting the containers from car to front door.
And the carton of eggs must be presented and inspected by the customer. Apparently intact eggs have a do-or-die influence on customer satisfaction. So this satisfaction is micro-managed by a devoted delivery person, in a face-to-face conversation. Double engagement.
The wages are modest, but the tips can be good. Why would someone provide a tip to someone delivering groceries from an online order? Because a worker put some enthusiasm and promptness into helping the customer get what they really wanted. How could you not tip this kind of service? As a customer, the cash rightfully belongs in your own hand, or the person who helped you. Why would your money go to anyone else?
Why do you hang out with people like you? Because you have to be friends with your friends’ friends. Society does not give you permission to dislike (or not know) your friends-of-friends. It’s called the forbidden triad. There is a complex quantitative puzzle involving triangles with plus and minus signs, all coded and ready for an elaborate statistical analysis. You can peek at the math in this October 2016 overview of the research by Dustin Stoltz, a PhD candidate at University of Notre Dame.
But back to people. The main problem is cognitive dissonance, that feeling you get when you are obliged to maintain two contradictory opinions at the same time. An example may be that you both love and hate a particular family member, politician, or manager in your workplace. Cognitive dissonance makes you uncomfortable, and you aspire to greater comfort. Therefore, you will choose between contradictory opinions and let one prevail over the other. So, you decide that you like that complex person. If you then meet a third-party who dislikes that person, you have to even-out the triangle. You will be motivated to change the third person’s mind, change your own mind, or just stop hanging out with the third party. If everyone does this, friendships and world views will evolve within cliques that are internally consistent, comfortable, and smug. But that’s not so clever.
That is because social networks are held together by people who choose to maintain contradictory opinions. They foster civil dialogue, cultivate plurality, and agree to disagree. It’s not so much that they are smarter, although that may still be the case. It’s that the exploration of the best information and the most diverse opinions guarantees contradiction. You will find attributes that seem contradictory but not mutually exclusive, such as sensitivity and courage. You will find rival facts, such as the prevailing research on global warming and colder winters in your own locale. And there will be facts that change quickly, such the price of oil or a change of government.
Workforce Analytics and the Workplace Culture of Curiosity and Discomfort
If you place comfort ahead of maximum information, then you have to insulate yourself from contradiction. Yet this can be a big mistake in the modern world. How could you possibly choose a stable mindset when the amount of information is exploding, technology is disrupting everything, and ideas and opinions go round the world in a heartbeat. It’s a wild and crazy world we live in. You must choose discomfort, and reject the allure of smug.
In workforce analytics, there is a great divide between colleagues and clients who are curious about new information and those who are not. It often feels like I exclusively support those hungry for the new, who like the challenge, who want to pick up a few tricks. Yet those who are more settled in their views or slower to change need to be brought along for the ride. That is because at the center of the social network people are obliged to commit to, and support, prevailing views. They tend to agree with one another just like you might do with your own friends. Looking outward to the fringes of the network, you might see a wider variety of irregular opinions, trends, and opportunities. The fringe is full of people who are removed from the network in some way, be it marginal legal status, geographic isolation, exclusion, or just looking different. To bring diverse views from the fringe to the center (and vice-versa) obliges us to maintain contradictory opinions.
The prescription that we must become uncomfortable applies equally to social trends, new technology, and disruptive workforce analytics. In your workplace, you may have had one opinion for a very long time. When you are presented with change or new evidence, it is one thing to simply obey orders or comply with the data. But if you really want to be clever, it is far better to hold onto that moment of discomfort for a while to get a sense of what everyone else is going through. Only then can you talk to diverse people who think and live in different worlds. And only then can you fine-tune new evidence to make it presentable to a broader audience.
If we are to disrupt normal ways of doing things through emerging information, we must stand at the bridge between two worlds, be prepared to disrupt ourselves, and get used to discomfort.
Do you wish that there was more equality in our access to information? I do. In the past (i.e. a few decades ago) it used to be far more common for information to be more tightly-held by those with power. However, major employers are pushing data downward into the hands of more people within their organization.
Here is an interesting article about data democratization, a buzzword that warrants some clarity. Author Bernard Marr, in his July 2017 article in Forbes, describes data democratization through general themes. An organization’s internal data is no longer “owned” by the Information Technology department, rather the data is put into the hands of diverse users. Everyone has access to the data and there are no gatekeepers creating an access bottleneck. People from varied ranks and diverse professional backgrounds can use the data to advance their goals. There are down-sides, including redundant efforts by distributed users, concerns about data security, the fact that some data still exists in silos, and misunderstandings by those who don’t deal with the data every day.
It’s important to take this phenomenon seriously as a trend that is building steam, and which is probably here to stay.
In my opinion, the word “democracy” is problematic. For example only those with digital literacy who are inside the organization can take full advantage. Those with more power can use the new information more significantly to their advantage. There also tends to be a winner-takes-all outcome, where the person with the best information and the most sophisticated ability to use it tends to come out ahead.
While you might think that these phenomena imply data is undemocratic, guess again. Electoral democracy, although pure in spirit, tends only to involve between one-half and three-quarters of voters who cast a ballot. Those who are powerful (i.e. business owners and property owners) have a strange ability to get more out of elected governments than others. And those who are the best at politics will tend to win all of the power, leaving others in the dust. Much like parliamentary democracy, data democracy works best for those who have the upper hand. In both cases, the system is a pseudo-democracy of established interests choosing amongst themselves who they will share power with. I think that’s called aristocracy.