On optimisation

12 February 2024

A social media account recently posted a video from a coffee shop that is apparently using an AI to track and measure the activity of its employees. On the video, the AI has identified each employee and the number of cups of coffee they have served. It also has a time stamp next to each customer, indicating how long they’ve been in the shop.

A vision of the future? It’s no secret there’s a mad scramble at the moment for business to find ways to utilise AI technology. According to PwC’s Global Artificial Intelligence Study, AI might potentially contribute $15.7 trillion to the global economy by 2030. About $6.6 trillion of that, it says, will be due to ‘labour productivity improvements’, where AI might be used to ‘augment’ the productivity of the labour force and automate many tasks and roles.

Indeed, the sort of tracking available in that café has long been available in less visible industries. It’s not hard to see how this sort of technology might appeal to employers who want to find an easy way to drive workers to do more during their shifts.

However, I think that word ‘easy’ is part of the problem. The tracking in that video might show an employer which of their staff spends the most time making coffee, but it doesn’t help an employer understand which of their staff is the most valuable. In order to do that one would need an app that tracked not only how many tasks an employee got done, but also how many people left satisfied after interacting with them, whether other staff wanted to work a shift with them, whether suppliers were happy to deal with them, and likely many other small ways they contribute to the environment they work in.

The freedom to be fully ourselves - and to freely form relationships with our fellow workers, managers and others we encounter in our work - is an essential part of our ‘optimisation’.

Other industries are already grappling with these questions. The last decade or so has seen sporting clubs turn to data analytics in order to find ways to ‘augment’ team performances. The ‘Moneyball’ phenomena started a race for teams to find new ways to analyse performances in order to find players that offer teams the ‘best value’. It has led to some innovation in sports: basketball, for example, has transformed as teams realise that their best chance of winning comes via a greater return

AUSTRALIA

On optimisation

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