The most recent review of a century’s worth of research on selection methods, found that tests of general mental ability  are the best available predictors of job performance, especially when paired with an integrity test. Yet, assessing candidates’ and employees’ potential presents significant challenges. People Metrics Are Hard to Get Right. For all the promise these techniques hold, it’s difficult to measure something as complex as a person for several reasons.

Multiple valid and reliable personality tests have been carefully calibrated to measure one or more character traits. These traditional psychological assessments are usually designed to help figure out whether a person is more or less intelligent or extraverted than their peers, for example. In other words, the assessments are meant to capture differences between people. 

But several studies have found that, during a two-week period, there can be even more variation within one individual’s personality than there is from person to person.

People change, and not always when you expect them to. The allure of aptitude, intelligence, and personality tests is that they purport to tell us something stable and enduring about who people are and what they are capable of. Test makers (usually) go to great lengths to make sure people who take the test more than once get a similar score the second time around. 

Yet compelling evidence suggests that people can learn how to learn, sometimes in ways we didn’t anticipate. We can also shift our personalities in one direction or another (at least to some degree, though not always without cost) for both near-term benefits and longer-term goals. 

Interestingly, one recent study with more than 13,000 participants found that people tend to become more conscientious right before getting a new job, which is conveniently around the time a hiring manager would be trying to figure out how hard they would work if they landed the role.

The nature of the task can matter more than the nature of the person. Most of us have heard the theory that we each have a preferred learning style, and the more we can use the one that fits, the more we’ll remember. Unfortunately, virtually no evidence supports that theory. That doesn’t mean that all approaches to studying are equally effective, it’s just that the strategy that works best often depends more on the task than on the person. 

Similarly, different parts of our personalities can serve different types of goals. We act extraverted when we want to connect with others or seize an opportunity, and we become disciplined when we want to get something done or avoid mistakes.

Given the findings mentioned above about how much people’s behaviour can change from one situation to the next, it might seem paradoxical to even try to find something enduring about a person’s character. 

But just because personality is dynamic does not mean it is not definable or not worth exploring. Some researchers have proposed using if-then questionnaires to detect nuanced patterns in each person’s personality profile, although such techniques have yet to be well-tested in the workplace.

A better approach might be to take repeated measures from the same employees over time. That is often easier said than done, given the challenges many organisations face in getting employees to fill out even a single survey. If the participation problem can be overcome, however, repeated measures can lead to insights about what people are like in general and the ways in which they vary that one time surveys simply can’t generate.

Adapted by “Can We Really Test People for Potential?” By Reb Rebele – MIT Sloan Management Review 201903 – Spring 2019 

Even if your job is high paying, it is wise to ask yourself whether it is common and repetitive enough to be done by a machine. If you conclude that it is, maybe it’s time to look for or create less commoditised work!

For many organisations today, the next big driver of job commoditisation is automation driven by smart machines.

Simply put, if a job is viewed as a commodity, it won’t be long before it is automated. 

Thomas H. Davenport’s research on automation through artificial intelligence (AI) or cognitive technologies suggests that, if a job with fairly routine duties can be outsourced, many of the tasks typically performed by the worker can probably be automated, even by relatively dumb technologies like robotic process automation. 

As an example, financial services is ripe for automation, given that many activities are relatively structured and there is relatively little product differentiation. Given this, there are both low-value and high-value commodity jobs in the financial industry and some of the lower-value ones, such as bank tellers, have been disappearing for a while, even if slowly. According to Hedge Fund Research Inc. in Chicago, Illinois some of the high-value jobs are being commoditised as well.

Sophisticated algorithms have begun to replace financial traders and hedge fund managers and about one-third of hedge fund assets are managed in that way. The result is that a robo-adviser, a machine that recommends investments to customers, has begun to replace human financial advisers. 

As customers turn to mutual funds, exchange-traded funds (ETFs), and other passive investments, it’s relatively easy to determine an appropriate portfolio for consumers, rebalance it for asset allocation preferences, and harvest tax losses, all with little or no human intervention.

Since most financial markets are digital, machines can easily determine which investments perform best. Intuition and personal experience in picking investments count for little. So, even traditionally well-paid financial jobs are becoming commoditised and, as a consequence, automated.  Despite this, automation often takes longer than we expect because organisational inertia can be high, because all jobs are slightly different from one another, and because people find ways to differentiate themselves.

So, how can we protect our work from becoming a commoditised job?

Start to focus on the most human aspects of your tasks, those that are most difficult to automate. In the financial world, for example, this often involves understanding human beings and the poor financial decisions they frequently make.  One of Thomas H. Davenport’s students defined this as “financial psychiatry,” but a more academically respectable name is “behavioural finance.” 

Financial advisers who understand behavioural finance can focus not on selecting investments for their clients, but on talking them off the cliff when they’re ready to “sell everything” after a market decline, or other careless financial decisions.

Or they can address the tricky problem of reconciling the differing risk tolerances of husbands and wives. Tackling such complex and emotional issues is not likely to be taken over by machines anytime soon!

Paradoxically, workers in finance and other industries that have high paying, specialised jobs can also make themselves less commoditised by helping with the commoditisation process. If they’re good at structuring decisions and understanding how those are represented in computers, they will have jobs for quite a while.

They’ll be able to monitor machine-based decisions, pick up the ball when the machines drop it (because of missing data, for example) and perhaps even improve machines’ decision making over time. It’s very difficult to predict how quickly jobs of various types will become commodities and how quickly the humans who perform them will be replaced by machines. 

Jobs are comprised of a set of tasks, only some of which are usually automated. 

Automation often takes longer than we expect because organisational inertia can be high, because all jobs are slightly different from one another, and because people find ways to differentiate themselves. 

But no matter what your field, it pays to ask yourself whether your job is common and repetitive enough to be done by a machine. If you conclude that it is, it’s time to look for or create less commoditised work.

Adapted by an article written by Thomas H. Davenport President’s Distinguished Professor of Information Technology and Management at Babson College in Babson Park, Massachusetts, as well as a fellow at the MIT Initiative on the Digital Economy – MIT Sloan Management Review 201804

Managers are often advised to seek feedback on their performance and reviews from team members.

But researchers found instead that sharing critical managerial feedback had greater positive effects on team dynamics.

During their studies, researchers divided over 100 team leaders into four groups:

• Leaders in the first group were told to ask team members for feedback on their performance
• Leaders in the second group were told to discuss development areas from their own performance reviews
• Leaders in the third group did both 
• Leaders in the fourth group did neither

After a year, teams whose leaders had shared negative feedback about themselves reported significant improvement.

Leaders initially felt anxious and employees were sceptical and largely remained quiet. But as leaders continued to share, vulnerability was normalised, allowing feelings of safety to grow.

When leaders asked for feedback, by contrast, employees tended to speak up but leaders sometimes reacted

defensively, because they felt judged and were not ready to make a public commitment to vulnerability. 

The feedback often concerned things that were not very useful or controllable for leaders. So for these teams, employees went on saying less and less to their leaders, while the leaders became more and more unresponsive.

Researchers concluded that leaders who asked their team for performance feedback actually compromised team dynamics, while sharing feedback with the team helped employees to concentrate on issues that were really important.

Adapted from HBR 202202 – “Taking Your Team Behind the Curtain: The Effects of Leader Feedback-Sharing and Feedback-Seeking on Team Psychological Safety,” by Constantinos G.V.