Is Workplace Culture the Right Kind of Revolution?

Posted on by Stuart Murray
The wall of plexiglass, by ebt47563 (=)
The wall of plexiglass.  Photo courtesy of ebt47563.

Exactly how do you change organizational culture?  This is a good HBR article from June 2017 about attempts to change corporate culture from the bottom-up.  It’s a story about Dr. Reddy’s, a global pharmaceutical company based in India and led by G.V. Prasad.  The authors are Bryan Walker from IDEO and Sarah A. Soule from Stanford Business School.

Dr. Reddy’s process of culture change began with significant ground research to find out what their staff, providers, and investors needed when dealing with customers.  They brought their goals down to four simple words that brought it all together: good health can’t wait.  Instead of selling the slogan through posters and speeches they chose to demonstrate their purpose through actions.  The initiative named projects in packaging, sales, and internal data to advance the new vision.  There were some immediate impacts.  One scientist broke a number of company rules and produced a new product in 15 days, having prioritized new efforts to match the vision.

The comparison to social movements is important, because movements start with an emotion rather than a call to action.  Movements start small, “with a group of passionate enthusiasts who deliver modest wins.”  Momentum builds through networks, penetrating power structures and leadership.

There are also “safe havens,” places where activists can behave differently from the dominant culture and discuss their goals.  In innovative organizations, research labs are often built as separate mico-organizations that cultivate change as prep-work for the larger organization.  This story resonates with me, because disruptive workforce analytics will occasionally fall of deaf ears.  The analysis needs to be created in a manner which partially ignores pre-existing agendas or presumptions of how things would normally be done.  The decision of whether to apply new ideas might belong within a more formal process, but when experimenting with messy new ideas, to be sequestered is ideal.

Beyond the HBR article two additional models are appropriate to discuss the nature of change.

Innovating Technology and Trends Through Social Networks

The first model is the diffusion of innovations as described by Everett Rogers.  In this model, there is a small avant-garde of weirdos who just get into stuff that is new and interesting.  That crowd of innovators will not have the full opportunity to make money or make it big.  But their new findings diffuse through social networks, based on peoples’ network connections and their readiness to consider new ideas.

There are several hold-outs, such as the laggard crowd who resists change until it is impossible to do so.  The biggest difficulty is the early-stage challenge of “Crossing the Chasm” where the new idea has won-over a small crowd of early-adopters who are about 13.5% of the population.  The challenge is that sometimes there’s something about the new idea that doesn’t mesh with the next crowd, the early majority.  Some examples might be that the new technology has a difficult user interface, or the social trend is incompatible with the conventional lifestyle of those in the burbs.

In my opinion, the classic example of this challenge is the hands-free bluetooth headset that you see people wearing when they’re talking on the phone while walking down the street.  The technology has been in public for more than fifteen years and our first instinct is still that we want the caller to get professional help.  And that’s even when you’re not also angry at them about a misunderstanding.

Using Social Disobedience Tools to Change Workplace Culture From Within

Another compelling cultural change model is the Spectrum of Allies model from George Lakey of Training for Change.  Lakey is highly experienced in training social justice activists in civil disobedience.  I attended a couple of workshops with Lakey when I was part of the labour movement, and his spectrum model is eye-opening.

The key diagram is a semi-circle, kind of like a half-order of a large pizza with six or eight slices.  The idea is that everyone can be categorized according to their level of enthusiasm for, or resistance to, an agenda or new idea.  Then you lay these wedges out in order, with the most supportive categories on the left and the most resistant on the right.  Your goal is to shift all of society one wedge to the left.  That is, the biggest hold-outs still get your attention, you’re just trying to convince them to become only moderately opposed to your goals.  Those in the middle, you can tip towards you slightly.  Those who are with you from the start, those are your strongest advocates and you can start giving them more work.

What really holds the model together is that you are shifting the entire social culture towards your way of thinking, resulting in culture change.  Everyone is a big deal, everyone receives the attention they deserve.  It’s very different from that us-against-them stuff that we’re accustomed to seeing during elections.  And it is very different from the notion that the main difference in the key players is their place on the org chart.

What this means for workforce analytics, is that you will require several different vehicles to bring meaningful information into human resource decision-making.  While there will be those who are hungry for the information, there will be others who need to simply be sold on the notion that it is not a threat.  While innovative findings might be compelling amongst an in-crowd, getting the information through cliques and interests will require bridging links and data translation.  You can build new ideas in self-imposed isolation, but at some point you step into public and advance it your ideas through the audience.

But before you step out, please put away your Bluetooth headset.

Peeking Into the Future of Job Elimination

Posted on by Stuart Murray
Google Glass. Byi Karlis Dambrans.
Google Glass.  Photo courtesy of Karlis Dambrans.

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?

At University of Oxford, a survey from the Future of Humanity Institute asked several leading experts how long it will take for machines to outperform humans.  Here is the average forecast for a couple of skill sets:

  • 2023 – Folding laundry
  • 2027 – Truck driving
  • 2031 – Retail sales
  • 2049 – The writing of best-selling books
  • 2053 – Surgery

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.

What’s Fair in a Winner-Takes-All Economy?

Posted on by Stuart Murray
Medal in pieces, by kcxd
Medal in pieces.  Photo courtesy of kcxd.

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.

Meanwhile, new technologies will disrupt every sector and make billions for those who create or innovate these new solutions, leaving everyone else in the dust.

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.

Walking on Eggshells with Technology and Jobs

Posted on by Stuart Murray
Carton, by John Loo
Carton.  Photo courtesy of John Loo.

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?

Data Democracy, For Better or Worse

Posted on by Stuart Murray
Vote Here. By Andrew DuPont
Vote Here. Photo courtesy of Andrew DuPont

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.

Chasing Your Tail, Finding Your Soul

Posted on by Stuart Murray
Chasing his tail. Courtesy of Lil Shepherd
Chasing his tail. Photo courtesy of Lil Shepherd

Do you want to get promoted?  Here’s a quick tip… you probably don’t want to get promoted.  It’s extremely common for people to attribute their hopes and dreams to the single most common solution to their woes, which is a promotion into a higher-paying job.  But that’s not how our souls really work.

The “long tail” is a theory attributed to Chris Anderson of Wired and TED fame.  The long tail theory is that for many cultural products – books, movies, and music – we over-rely on a small number of great works that are immensely popular.  But there is also a very large amount of product that you might have enjoyed, if only you knew about it, it was easy to access, and you didn’t care that much what others thought about your taste.  If you want to get geeky, the long tail web site describes this concept using a power law distribution (1/x), where the popular goods are at the peak on the far left, constituting the “big head,” and the lesser-known goods tail off to the right, going on forever into smaller and smaller numbers, hence the “long tail.”

Bricks-and-mortar storefronts prefer to sell large volumes of popular goods, in order to reduce production and storage costs.  By contrast, things sold over the internet can be stored at low cost and sold in low volumes at reasonable profit.  The internet opens up your access to a greater diversity of concepts, allowing you to bypass overly-popular mainstream content.

It’s important to keep our eyes on consumer data, because consumer-based big data is about one decade ahead of human resources analytics in terms of maturity.

For those in human resources the long tail phenomenon is a good metaphor for career advancement concerns of employees.  Consider our societal obsession with vertical career movement and the opportunity to make more money by working longer hours and enduring greater stress.  Contrast that mainstream goal with the possibility of thousands of careers to choose from, a wide range of work-life balance concerns and solutions, and unusual combinations of hours of work and locations of work.

When people meet with career coaches, the employee will often name career advancement as their primary goal, typically to the rank of Director.  But after some inquiry, it often turns out that there is a deeper personal objective which is more important to them.  It could be energy level, the challenge, pride in craftsmanship, helping others, or greater independence.  But each of these individual objectives can be achieved through more targeted efforts.  Promotion is only one way to make life better, and in some cases it makes life worse if it takes us further away from the deeper goal.

Career decisions and deeper goals will occasionally line up with the mainstream.  For example, it’s usually an all-round good idea to get a degree.  But if your motivations are unique, think of your life choices as the equivalent of an obscure garage band with a cult following of two hundred people.  Sometimes it doesn’t matter if everyone else is doing it.  But it always matters if you’re doing what’s right for you.