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A human boss v/s the machine as a boss
In the world of increasing usage of automation, artificial intelligence and robotics, it is the primary concern of the working professionals to report to the human bosses or algorithmic/machine bosses.
In the world of increasing usage of automation, artificial intelligence and robotics, it is the primary concern of the working professionals to report to the human bosses or algorithmic/machine bosses.
Primarily, the boss plays a significant role in shaping up the career of an employee, as a mentor and a guide. So this raises an essential question as to whether an employee should report to a human boss or the machine boss?
The current industry practice is that every employee should report to their immediate human boss. This boss recruits an employee, assigns annual targets, also assess the performance appraisal of an employee to award bonuses, increments and promotions.
At the same time, human bosses help in grooming employees' career by sharing their own experiences. Further, they understand employee concerns and issues during times of emergencies. Of course, not all of these bosses are alike.
They can be broadly categorised as takers or givers. Some human bosses play the role of a taker and exploit their employees. The giver type of bosses shares their learnings and experience to develop an employees career growth.
Also, employees often convey about unfeasible targets by their bosses. An employee doubts the performance appraisal and promotions based on the mercy of his human boss.
Managing human bosses is a skill. The employees who know this skill tend to exploit their boss to the maximum. Hence, there are both pros and cons of working and reporting to the human boss.
We are now in the era of the gig economy, and everyone is monitored by his boss round the clock. The smartphone is playing the role of a boss for many.
Artificial Intelligence algorithms changed the way of managing things traditionally. Also, the entire structure of the organisation is changing now. In the past, if someone wanted to set up a company, he needed to hire office space and employees.
But now, having a great idea is enough. One can think of Swiggy, Zomato, Uber, Ola, etc., how many office spaces do they have? Is it possible to accommodate all their employees/partners by taking one physical office space? Not possible at all. Then how such companies are managed?
As we know, earlier the companies used to have supervisors, floor managers, etc., to supervise, to rectify the problems of employees and guide them, but now they are all guided and managed by the algorithms.
Think of Uber, Ola, Zomato, and others which monitor these employees; algorithms are their managers and bosses. The algorithm will determine how much each driver will get paid -- also, it monitors their performance levels based on the booking acceptance and rejection rates.
Such tools are helping to shape employees to perform the job better. So, in this era, employees are reporting and working under the machine bosses.
The importance of machines or algorithmic bosses by the corporates to find the right talent on the web across the globe is increasing. Moreover, such machines start hiring, setting the targets and evaluating performance appraisals.
The employees are always on toes meeting the goals to get a good rating by the machine bosses. Since the machine bosses are automated, the work environment has one motto: perform or perish.
Further, machine bosses do not show sympathy, humanity and empathy.
Similarly, employees do not get any support, career guidance and mentoring from an algorithmic boss.
The machine boss sends reminders 24x7 to meet the required targets under any circumstances. An Ola or Uber driver is highly concerned about feedback and
ratings. If they fail to get a threshold rating, then it can lead to "no bookings" for days together.
Of course, the primary advantage of working under a machine boss gives more freedom and flexibility of time to the employee. At the same time, an employee cannot share, discuss the job-related issues with machines.
Hence, it will be tough to handle machine bosses as they are programmed in comparison to the human bosses.
The market is thinking that automation will remove entry-level jobs. However, the current scenario looks like the so-called mid-managerial and white collar jobs are also more vulnerable due to automation.
Of course, many significant factors are present; when the boss is an algorithm, he will not be biased, will be more transparent, nepotism issues will not arise. However, a significant role will be missing: mentoring or career guidance.
There was a term called Paypal mafia, i.e. if you take most of the successful startups in Silicon Valley, a majority of them were founded by PayPal workers. Companies have to encourage entrepreneurial activity, which will not be possible by an algorithmic boss.
Now, after several series of development in management since decades, we are in the Algorithm Management era. This term was coined in the year 2015 by the academics of Carnegie Mellon University Human-Computer Interaction Institute.
Those who are using this algorithm management say that it creates employment, better and cheaper service, and more transparency in the labour market. Unlike human bosses, algorithmic bosses perform micromanagement.
A significant problem in algorithmic boss arises due to ethical issues. Many experts have understood the risk in this change. A human boss can empathise with the situation of his employees whereas algorithm boss cannot.
The algorithmic boss would monitor at the grass root level. If an employee does not perform at the desired level, he may lose certain benefits.
In initial days everyone felt that an algorithm boss was better than a human, but soon people realised the other way. Yes, we do accept the advantages in it, but still, it cannot replace the human boss.
To some extent, human bosses are better than algorithm ones though they cannot be as much efficient as them.
Finally, the would-be employees will find life difficult with machines as bosses. Also, the machine bosses are learning about humans faster than vice-versa.
(The first author, M Chandra Shekar is an Assistant Professor in the Institute of Public Enterprise, Hyderabad and the second author, R Kumaran is an Associate Credit Manager with IDFC First Bank, Chennai)
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