The Future Of Performance Management: How AI Combats Workplace Bias
2 July 2021
Today, there’s somewhat of a crisis of confidence in the way that industry evaluates employee performance. One piece of recent research showed that only 6% of organisations think their performance management processes are worthwhile.
Many companies have undergone a move away from traditional, metric-based performance assessment in recent years. Sometimes this is because they have been found limiting. Sometimes because it was found that employers and managers are too easily inclined to simply ignore them, if their findings don’t line up with their personal “gut feeling” on who they like or dislike.
The fact is that as the nature of work has changed, the behaviour of workforces has shifted to match this. Organisations have become larger and more complex, and smaller operations are more likely to work with networks of partners. The efficiency or inefficiency of a worker in today’s knowledge economy is difficult to assess using traditional, metric-based assessments.
The problem is that nothing has so far been found to adequately replace them – but could the answer, once again, be provided by AI?
Bias in the workplace
Much of the difficulty in assessing performance has been put down to difficulties caused by workplace biases. These are well-documented, conscious or unconscious behaviours which can unfairly influence an assessment of an individual’s contribution to an organisation.
Race and gender are perhaps two of the most obvious sources of individual bias. Fortunately they are often quite easy to spot. Others, however, are more ephemeral, and it may not be so immediately obvious when they are taking place.
One is known as contrast bias, meaning an assessor is inclined to compare an individual’s performance to his peers, rather than to defined standards of achievement. Another is recency bias – where actions in the recent past are given more weight, perhaps unfairly, than actions which happened further back in time (but still within the period where performance is being assessed).
This is where AI can come in, as bias – along with fatigue and logical fallibility – is a human failing that machine intelligence doesn’t have to overcome.
Kris Duggan, founder and CEO of BetterWorks, which provides an analytical goal-setting and performance assessment platform for industry, believes the traditional annual performance review is behind the decline in usefulness of performance assessment. He puts it that an ongoing feedback process is part of the solution, and that intelligent, cognitive systems can help us do this.
He tells me “We think that if you can make collecting feedback much more frequent and agile, and more lightweight … and it’s open and collaborative … those things really do drive performance.
“What we found when meeting our customers was that a lot of pain of doing the process [annual performance reviews] – the reason why it’s not lightweight and it takes a lot of effort – is you have to come up with a list of people you’re going to ask … and there’s some gaming going on … maybe someone is only going to ask people who will say good things. The spirit of it makes sense, but the execution is really not that great.”
How AI can help
A good thing about AI is it won’t treat the job of performance reviews as something to do “when I’ve got time.” Unlike many human managers, it won’t put off assessments until the last minute – tell it you want an ongoing, 360-degree view of your workforce’s effectiveness and (in theory) that’s what you’ll get.
BetterWorks implementation of AI is powered by what it calls its “work graph”. This is a map of all the connections within a workforce – not just in terms of which employees’ jobs are intertwined, but also where goals and targets are shared.
Because AI-driven assessment can happen in real-time (with systems monitoring targets, quotas and how these are affected by people’s connections), incentives and praise for good performance can be dished out immediately. If targets are not being met or performance standards are slipping, then intervention can take place before the problem grows and becomes unmanageable.
AI is certain to have a dramatic effect on workforce management in the future. But enabling the drive towards an ongoing, organic assessment culture is a positive step that is already being taken today.
It’s certain that it will raise concerns, too – I can certainly foresee headlines being made once people begin to feel they are being “fired by machines”. These worries are justified – already a large number of jobs are scheduled to be replaced by AI. But currently, the potential for positive change – and a move away from tired procedures towards ongoing, real time assessment, means this is likely to be an area of interesting new development.
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