Skip to main content

Flexible Work Arrangements Will Be Essential Thanks to AI

I recently read an article focused on Richard Branson's views about flexible work arrangements, specifically the outdated idea of the 5-day work week (Richard Branson is the founder of Virgin Group).





He argues that business leaders should be embracing flexible work arrangements, such as reduced work weeks, flexible hours, and working from home.
"It's easier to attract to talent when you are open and flexible. It's not effective or productive to force them to behave in a conventional way." -Richard Branson

Although attracting top talent is critical, flexible work arrangements are going to be essential in the future, thanks to the ways that AI will transform daily work. Business leaders need to accept the fundamental shift in the nature of work that is occurring thanks to digital transformation. Which employee duties will be automated in the coming years? What will be left for employees to do with their time?

Is AI Eliminating the Need to Work?

Does this make employees unnecessary? Absolutely not. The remaining essential duties will become more important, and more valuable, than ever before.

Tasks that should be automated no longer differentiate your business. What will differentiate your business are the skills that only your employees can bring to the table.

People should be able to focus on the things that people do best: creativity, intuition, emotional awareness, and relationship building. As I mentioned in my post about AI in Medicine, automation gets rid of the tedious and dehumanizing parts of a job, leaving people to highlight their strengths.

Reconsidering the 40 hour Work Week

Creativity, intuition, and emotional awareness are all mentally taxing, which is why people cannot be expected to do these things to their fullest potential for 40 hours every week. A business could easily achieve the same amount of value from an employee whose flexible work arrangements included working only 32 hours per week, assuming the mundane parts of the job were eliminated.

The question then is: How does this change the employer/employee obligation structure? In the US the very structure of healthcare, retirement, and taxation is built on the concept of the 40 hour work week. Does the employer bear less responsibility if AI means that their employees have to work fewer hours per week?

There are never easy answers, and businesses will need to be more creative in how they compensate employees, all of whom will be expected to bring skills to the table that are currently considered necessary only for senior and leadership roles.

Working from Home

AI will likely eliminate most of the tasks we associate with in-office work, such as answering phones, customer support, ensuring machines rooms have not caught fire, etc. As businesses make AI a bigger part of their daily routines, business leaders need to take a look at the daily routines of their employees. Adding AI won't mean that there is less work to do. Rather, it means that the work which needs to be done is more difficult, more taxing on employees, and ultimately more valuable.

Being able to offer employees the ability to work from home as part of their flexible work arrangements means that employees can start their day earlier and less stressed, thanks to eliminating commute time. The result? A more effective employee who is ready to tackle the complex, creative work that cannot be outsourced to AI-enhanced systems.

Make Flexible Work Arrangements an Employee Benefit

In today's technology businesses, it can be difficult to convince top talent to stay or to entice talent to join your company. Many experienced technologists have their choice of employers, and have come to expect a host of various benefits beyond the basic group insurance and retirement. While it may not make sense to offer unlimited food, drinks, and Foosball, offering flexible work arrangements like reduced-length work weeks and working from home should be standard in the digitally transformed businesses of tomorrow.

Artificial Intelligence will eventually remove many of the mundane tasks from our work, and will be trusted to make decisions automatically. The real question is what businesses ask their employees to do after this transformation. Is it more important for your business that your employees do more, or that they do better?

Popular posts from this blog

Neural Network Dense Layers

Neural network dense layers (or fully connected layers) are the foundation of nearly all neural networks. If you look closely at almost any topology, somewhere there is a dense layer lurking. This post will cover the history behind dense layers, what they are used for, and how to use them by walking through the "Hello, World!" of neural networks: digit classification.

The Only Neural Network Layer You Will EVER Need

Neural networks can seem daunting, complicated, and impossible to explain. But in reality they are remarkably simple. In fact, they only ever require a single layer of neurons. In my previous post about the basics of neural networks , I talked about how neurons compute values. They take a set of inputs, multiply each input value by a weight, and sum the terms. An activation function is then applied to the sum of products, to yield the output value. That output value could be zero (i.e., did not activate), negative, or positive. In this post, we will go deeper down the rabbit hole. We will look at neuron layers, which layers are actually necessary for a network to function, and come to the stunning realization that all neural networks have only a single output. Organizing Neurons into Layers In most neural networks, we tend to organize neurons into layers. The reason for this comes from graph theory (as neural networks are little more than computational graphs). Each layer con

Neural Network Pooling Layers

Neural networks need to map inputs to outputs. It seems simple enough, but in most useful cases this means building a network with millions of parameters, which look at millions or billions of relationships hidden in the input data. Do we need all of these relationships? Is all of this information necessary? Probably not. That's where neural network pooling layers can help. In this post, we're going to dive into the deep end and learn how pooling layers can reduce the size of your network while producing highly accurate models.