Strategy. Innovation. Brand.
Jobs We Won’t Need
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Last week, I asked, Should We Work? and discussed the issues of job elimination over the next 50 years. Here’s a terrific article from Technology Review about how the process works. And, here are my fearless predictions of just some of the jobs that will disappear over the next 50 years. I’ve also included my predictions for the types of jobs that we will still need. It’s not a long list.
Drivers – autonomous vehicles will replace cab drivers, truck drivers, and bus drivers. Tell the vehicle where you want it go and it will take you there.
Surgeons – if a robot can make micro-welds in the factory, it can also replace your heart valve. A robot’s hands don’t shake.
Computer programmers – computers can program computers faster than people can.
Doctors – computers can diagnose what’s wrong with you better than humans can. Humans might have a better bedside manner, but avatars accompanied by a cute puppy are close… and they’re always on call.
Pilots – drones can do a better job, especially in fighter jets. The weak link in military aircraft is the human. Without a human, we can build faster, more maneuverable, and much, much cheaper warplanes.
College professors – MOOCs will rule.
Room service staff – why order in when you can order out?
Accountants – accountants interpret rules and enter data. Computers can do that.
Stockbrokers – computers already account for the bulk of stock trading. In the future, you won’t invest in stocks; you’ll invest in the algorithms that you think can pick the best stocks.
Engineers – most engineers solve structured problems. So do computers.
Politicians – computers can find optimal solutions to problems better than perpetually outraged people.
Many of our white-collar jobs today require people to manipulate symbols and process information. For instance a doctor who is trying to diagnose what ails you needs to interpret lab results, recall symptoms of many possible diseases, fight off fatigue, and evade logic traps. Well-trained computers can do this better.
So what kinds of jobs will be left? I can think of two general categories:
Persuasion – I’m not sure that we can train computers to be persuasive. Being persuasive requires an emotional connection and a degree of trust. Can you trust a computer? Perhaps. Still, I think people will be more persuasive, though maybe only marginally so.
Imagination – Can we teach computers how to imagine? Perhaps. After all, innovation typically results from mashing up existing ideas. A computer could mash up ideas. But it would be fairly random; I don’t think a computer would really understand the possibilities. So, humans should retain a competitive edge in tasks that require imagination.
I’m guessing that the ability to manipulate symbols and process information won’t be enough to get you a job in 2063. You’ll need to be imaginative and persuasive. Is that what we’re teaching in schools today?
An Evolutionary View of Innovation
My sister has a Ph.D. in biology. For her dissertation, she randomly divided fruit flies into two groups and treated them exactly the same except for one variable. She introduced a specific chemical to one group but not the other. Then she followed the effects through multiple generations. I don’t remember what she discovered but her method allowed her to conclusively link cause to effect.
Why did she choose fruit flies? Because she wanted to look at the effects of the chemical over multiple generations and fruit flies create generations quickly. She wanted to know not just how the chemical affected fruit flies. She wanted to know how the chemical affected the evolution of fruit flies.
Could we use evolutionary thinking to solve business problems in innovative ways? Well, there’s a theory that we could develop software more quickly and at less expense through evolutionary techniques.
First we identify a problem that we want software to solve. Then we create, say 10,000 identical sets of code. We introduce random variations into each set, execute the code, and then determine which set comes closest to solving the problem. We take the winner, make 10,000 copies, introduce random variations into each one, then execute the code. We pick the winner and repeat the process. It’s like breeding dogs, only less messy.
With modern computing power, we can generate thousands of generations in very short order. We could almost certainly solve the problem. Additionally, we might generate some very novel solutions. The random variation might lead us down paths that we never would have imagined on our own.
While I suspect we’ll make evolutionary software before long, it does seem a bit exotic. Are there ways we could apply evolutionary thinking to solve more practical, day-to-day problems?
Sometimes I think it’s as simple as asking the question. Too often we make yes/no, either/or decisions – whether-or-not decisions as Chip Heath calls them. But we can always ask the question, is there an evolutionary way of looking at the problem? We might find that there are multiple sub-decisions we could make along the way to the big decision. We can decide smaller issues, test the results, and repeat the process. Each time we do, we create a new generation.
A “generation” in this sense might be a set of market trials, a series of studies, or surveys, or focus groups, or trial balloons. We can find many ways to identify and/or validate market needs. But first we have to ask the question. So the next time you participate in a big decision – especially a big risky decision – be sure to ask yourself, could an evolutionary approach help us here? The answer may be no, but don’t close the door too soon.
Appreciative Inquiry
When I encountered a problem as a manager, my natural inclination was to delve into it with sharply defined questions like:
- What went wrong?
- How did we get here?
- How did this happen?
- Who was responsible?
- What was the root cause?
The first thing you’ll notice about these questions is that they’re all in the past tense. As we know from studying rhetoric, arguments in the past tense are about laying blame, not about finding solutions. The very way that I phrase my questions lets people know that I’m seeking someone to blame. What’s the natural reaction? People become defensive and bury the evidence.
The second thing you’ll notice is that all my questions are negative. The questions presuppose that nothing good happened. I don’t ask about what went right. I’m just not thinking about it. And neither is anyone else who hears my questions.
In many situations, however, a lot of things do go right. In fact, I would guess that in most organizations most things go right most of the time. Failures are caused by a few things going wrong. It’s rarely the case that everything goes wrong. Focusing on what’s wrong narrows our vision to a small slice of the activity. We don’t see the big picture. It’s self-defeating.
So, I’ve been looking for a systematic way to focus on the positive even when negative things happen. I think I may have found a solution in something called appreciative inquiry or AI.
According to Wikipedia, appreciative inquiry “is based on the assumption that the questions we ask will tend to focus our attention in a particular direction.” Instead of focusing on deficiencies, AI “starts with the belief that every organization, and every person in that organization, has positive aspects that can be built upon.” AI argues that, when people “in an organization are motivated to understand and value the most favorable features of its culture, [the organization] can make rapid improvements”.
The AI model includes four major steps:
- Discover – identify processes and cultural features that work well;
- Dream – envision processes that would work well in the future;
- Design – develop process that would work well in the culture;
- Deliver – execute the proposed designs.
The ultimate goal is to “build organizations around what works, rather than trying to fix what doesn’t”.
Paul Nutt compares appreciative inquiry to solving a mystery. To get to the bottom of a mystery, we need to know about everything that went on, not just those things that went wrong. Nutt writes that, “A mystery calls for appreciative inquiry, in which skillful questioning is used to get to the bottom of things.”
I’m still learning about appreciative inquiry (and about most everything else) and I’m sure that I’ll write more about it in the future. In the meantime, if you have examples of appreciative inquiry used in an organization, please let me know.
Seven Leadership Styles
Alchemist
I’ve often thought of leadership as the ability to motivate people to work together toward a common goal. My definition is not far from Dwight Eisenhower’s quote: “Leadership is getting people to do something because they want to do it.”
Of course, there are different tactics to use in different situations. I study rhetoric – the art and science of persuasion – because I find it very helpful in convincing people that they want to do something. I also like to lay out goals and make them clear as possible. I’m not much of a yeller but I can understand (intellectually at least) how yelling might be a good leadership tactic in some situations – like an emergency.
I hadn’t thought much beyond that, so it was good to rediscover a 2005 Harvard Business Review article titled “Seven Transformations of Leadership”. The authors, David Rooke and William Torbert, identify seven “action logics” that can help us understand what kind of leader we already are.
I’ll summarize the seven here – using Rooke and Torbert’s terminology – partially because I think they’re important but also because I want to refer back to them in future posts.
Opportunists – “characterized by mistrust, egocentrism, and manipulativeness”, their goal is to win in any way possible. Only 5% of leaders are deemed to be opportunists (thankfully).
Diplomats – “seeks to please higher-level colleagues while avoiding conflict.” They rarely rock the boat and comprise about 12% of leaders.
Experts – “try to exercise control by perfecting their knowledge … watertight thinking is extremely important.” Experts comprise the largest single group of leaders, about 38%.
Achievers — “create a positive work environment and focus … on deliverables, the downside is that their style often inhibits thinking outside the box.” About 30% of leaders are achievers.
Individualists – often seen as “wild cards” with “unique and unconventional ways of operating”. Yet they also “contribute unique practical value” and “communicate well with people who have other action logics.” They make up about 10% of leaders.
Strategists – “focus on organizational constraints and perceptions, which they treat as discussable and transformable.” They are “highly effective change agents” but account for only 4% of leaders.
Alchemists – are able to “renew or reinvent themselves in historically significant ways.” They have “an extraordinary capacity to deal simultaneously with many situations at multiple levels.” Rooke and Torbert identify Nelson Mandela as an exemplar of the alchemist, which may help explain why only 1% of leaders fit the category.
Should We Work?
What? I still have to work?
Which of these two government policies is most appropriate for the next 50 years?
- Strive to reduce unemployment; aim for 4% unemployment.
- Eliminate the need to work; aim for 100% unemployment.
It’s a question we’ll need to wrestle with soon. It appears that we’re at the beginning of another great wave of job destruction. The last wave, starting roughly in 1980, eliminated or outsourced blue collar and clerical jobs. We used to have secretaries; now we have word processing software. We used to have factory workers; now we have robots.
The next wave will eliminate white collar jobs. This will happen in two ways:
Type 1 – through advanced communications and software support, a small number of “augmented knowledge workers” can do the work of thousands of traditional knowledge workers.
Type 2 – machines and systems will become smart enough to replace many knowledge workers.
I’ll illustrate with two examples from my life.
Type 1: The MOOCs. Massive Online Open Courses find very talented professors and augment them. With video, web, and online testing support, these professors can literally reach thousands of students. They give great lectures. (You should watch them). Why do we need other professors to cover the same material? A few professors can replace thousands. By the way, this will also accelerate the dominance of English.
Type 2: Automated Essay Grading. I’m rather proud of my ability to read essays and make useful comments that help students think more clearly and communicate more effectively. So what? Within the next few years, we’ll see software systems that can do almost as good a job as I can. OK … maybe they could do it even better since they never get tired. I’ve always thought that this would be a difficult task to automate because it’s “fuzzy”. But computers are mastering fuzzy logic even as we speak.
Much of what we call “knowledge work” is actually easier to automate than essay grading. Any process based on rules is fairly easy to computerize. Deciding which stocks to buy or sell is a good example. It’s just a set of rules. So today, “quants” and high-speed computers dominate much of our stock trading.
Diagnosing an illness may be another good example. Today, as many as 15% of diagnoses made by humans are wrong. But diagnosis is just a rules-based process. Surely, a computer can do better.
Within the next three decades, we may well reach a point where nobody needs to work. So what will we do? Good question. Perhaps we should ask a computer.
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