The Future Workforce Dilemma: Should Robots Be Held To A Higher Standard Than Employees?

They never show up late and they can work 24/7. They don’t take lunch breaks, sick days or require paid leave. They don’t leave coffee cup rings on the desk, or complain about the air conditioning. So, if they’re less of a HR headache, should robots be held to a higher standard than their human counterparts?

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Defining Standards

What do we mean by ‘higher standards’? Merriam Webster defines a standard as ‘something set up and established by authority as a rule for the measure of quantity, weight, extent, value, or quality’. The established measure of quantity is usually determined by the average performance over a period of time, or a published set of metrics for a specific task, as determined by a respected body.

For example, Company X may have determined that on average, over the last twelve month period, a financial analyst can produce 65 accounting entries per month – defining the standard. So, if a robot is subsequently created to produce accounting entries each month for a year, and averages 100 accounting entries, do these results make the robot superior to the employee?

Balancing Quality and Quantity

A robot is programmed with the logic of the majority of use cases – in this example, accounting entries. Could the robot be programmed for 100 percent of the cases? Absolutely, but is the programming specific to something that happens once a quarter or year, for example, justifiable from a cost perspective? That is something for the organisation to decide.

In our example case, the robot produced 69 percent more entries than the human. This raises three issues:

  • From a purely results driven perspective, the robot did a much better job
  • The exceptions that the robot was not programmed for fell to the human to resolve
  • The robot only had to access the entry once, whereas the human had to access more often to amend any errors

When we consider the weight element of a standard, these issues bring to light a common misconception about robots – that they can only carry out simple, transactional tasks. In reality, robots thrive on complexity. If we take a specific, complex entry and program a robot to complete it side by side with a human, the robot wins every time.

An average accounting entry may take twenty-five minutes for a human to complete. This example defines the extent, or level of effort required to complete the entry. Robots don’t get distracted, and they don’t have to go back to the entry to check it for errors.

How can we measure value or quality?

Measuring value or quality is important because it is not always monetised when developing a business case for robots. Accounting entries made by humans may have been revisited a number of times before they passed inspection and have been subsequently approved and posted. Further down the line still, the approver may have overlooked some of the human errors, and any incorrect entry will have slipped through the net.

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On the other hand, the robot’s entry was right first time, so will pass entry and be posted without query. Fast forward to a quarterly or early audit of entries either by an internal compliance team or external auditors: As they review, they may catch the errors made by the reviewer for the human entries, but there are none to catch from the robot. In fact, when compliance or external auditors encounter robotic entries, just like systematic entries, they spend less time scrutinising them because they know the human error is absent. This actually equates to not only a minimised risk of financial error, but more importantly, reduced audit costs.

So, should robots be held to a higher standard because of the higher value they derive with their output? The caveat is that humans will always be required within an operation because the robot must be programmed for changes, and they need to handle the one-off transactions the robot has not been equipped to handle. It is not so much a question of competition between humans and robots, as it is collaboration.

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CIF Presents TWF – Professor Sue Black


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