When you read or listen to anything about generative AI and its impact on jobs, it’s often a story of job losses. Massive waves of job losses as more and more tasks are given over to machines.

But the truth is more complicated – and more optimistic – than that. Because, rather than job displacement, the biggest impact of generative AI is likely to be job augmentation. In this article, we’ll look at what that means in practice and help you uncover how your own job might be impacted.

Generative AI Is Accelerating Automation

There is no doubt about it: generative AI tools like ChatGPT are accelerating the wider trend for AI-driven automation. A 2023 McKinsey report found that, by 2030, activities that add up to 30 percent of hours currently worked across the US economy could be automated. That’s a pretty big chunk, and yet, some jobs will be more affected than others.

If we look at Indeed’s 2023 AI At Work report, the findings suggest that roughly 20 percent of jobs could be considered “highly exposed” to generative AI automation – meaning the technology is considered good or excellent at 80 percent or more of all skills for that job. At the other end of the scale, 34 percent of jobs were “low or minimally” exposed – but even that means that generative AI is good or excellent at up to 50 percent of the work.

In other words, almost every job will be impacted by generative AI to some extent. Some jobs will cease to exist, but many more will be augmented or altered by AI tools. And, of course, new jobs will be created, just as they have before in previous waves of transformation.

At The “Highly Exposed” End Of The Scale

Customer service representative is a job that faces one of the highest risks of being displaced thanks to generative AI. Many customer service tasks are already automated through chatbots – and this will only increase. ChatGPT, for example, is far more impressive than your average customer service chatbot and is getting better all the time. Looking ahead, generative AI chatbots will routinely talk to customers and help them solve their problems.

But even in this highly exposed field, it doesn’t mean everyone who works in customer service is on a one-way route to redundancy. While many tasks within customer service can be automated, there will still be scenarios that require the human touch. Complex issues, sensitive topics, or situations requiring empathy and judgment will always be better handled by humans. Most likely, then, the role of customer service representative will evolve, with a smaller group of humans working alongside AI tools. In this scenario, humans will oversee AI operations and handle the more complex interactions.

At The Other End Of The Scale: Job Augmentation

What exactly do we mean by “job augmentation”? Augmentation means that rather than human workers being replaced by generative AI systems, job roles will adapt to incorporate generative AI. Indeed, many jobs – including knowledge work and creative roles – will increasingly integrate generative AI tools.

For the most part, this will mean automating certain tasks that are repeatable and straightforward – the sort of work that doesn’t really require the human touch. This allows people to focus their time and effort on aspects that require uniquely human skills.

Teaching, for example, is a role that will always need the human touch. A good teacher’s ability to inspire young minds and really connect with students is special. But can teachers use generative AI to help streamline lesson planning? Absolutely. Similarly, doctors can use generative AI tools to interpret medical images or generate medical notes more quickly, freeing up more time for patient care. Lawyers can use generative AI to automate certain research and contract analysis tasks. And software developers can use generative AI to automate parts of the code creation and testing process.

So What About Your Job?

I’d advise anyone to take a good, long look at their job to see which parts of the role are exposed to automation and which require the human touch. A good starting point is to ask yourself questions like:

· How does my work add value?

· Which of my tasks can be automated?

· And, importantly, how can I use generative AI to add greater value/deliver a better service/do a better job?

That last question is crucial. Because this process isn’t just about assessing your level of risk – it’s about finding new ways to do your job better and easier by tapping into generative AI tools.

And if you find that your job is highly exposed to automation, ask yourself, how would you like to add value in the world? To put it another way, what would you rather be doing? In her book Next! The Power of Reinvention in Life and Work, Joanne Lipman sets out a four-step reinvention road map that helps people transition to a new way of working and/or living:

· Search: this stage is about identifying the information and experiences that will inform your transition. For example, this could be a side hustle, a hobby, or a topic that you’re particularly passionate about.

· Struggle: This stage is where you’re starting to disconnect from your previous identity, but perhaps you don’t yet know where you’re going. We often overlook this stage in favor of the dramatic “I quit my job, and now I run yoga classes in the woods” stories. But the truth is, this stage takes time. And it’s a struggle, hence the name.

· Stop: This is the thing that finally stops you in your tracks – such as a change in circumstances, or something like a global pandemic, or a decision to quit your job.

· Solution: And in this final stage, the transition is complete.

If there’s one thing I want people to take away from this article, it’s that generative AI isn’t something to fear. Yes, it will bring about job displacement for some people. But it also has the potential to make work better.