The overwork and productivity obsessions driving generative AI use

You can’t scroll Instagram or look at a pub menu without spotting the unmistakable signs of generative AI. The paint smears behind the text. The uniform ‘table’ of details and the copy-and-paste graphics. As much information as possible crammed into a rectangle, to the point where it is almost unintelligible.

Criticism aside, though, it’s easy to see why there is an increasing reliance on AI. The intensification of work, along with job insecurity, has left workers under pressure and struggling to keep up with their unrealistic workloads. Gone are the days of just fulfilling a role. Now, we’re expected to exceed our job descriptions to get ahead – or even keep our positions at all.

According to a survey of more than 16,000 professionals across 11 countries, nearly 45% said they were too busy to deliver meaningful results, while 44 per cent felt they were being asked to do more than they were capable of doing. A further 61% were now doing the work of more than one role.

“The easy answer is of course that people are overwhelmed,” says Victoria McLean, CEO & founder of the global career consultancy City CV and Hanover Talent Solutions. “Calendars are packed, teams are leaner, expectations continue to rise and everybody is looking for ways to get through more work in less time. Against that backdrop, it's hardly surprising that AI has been embraced so quickly.”

Read more: Why AI is making us treat people worse at work

Why is overworking so normal now?

There are several reasons why working excessive hours has become so normalised and much of it comes down to how our jobs are changing. Faced with rising costs, employers are choosing to spread the workload across existing teams rather than recruit more staff. People are expected to do more, often without the extra time or resources to go with it. Additionally, when it's no longer clear where your responsibilities begin and end, it's much easier for additional work to creep in.

Our job descriptions have also become increasingly blurred. Take someone who makes cakes for a living. Alongside baking, they're expected to manage the social media, handle digital marketing and organise deliveries. What was once one job has quietly become several. And, our desire for meaningful work can make it harder to set boundaries. Work is rarely just work, too. For many of us, what we do professionally is inherently tied to our sense of self-worth, so being busy and productive can feel like proof that we're capable and valuable.

All of these changes have coincided with another major shift – the waning power of trade unions. In 1980, union membership in the UK was 12.2 million. By 2020, that figure had dropped to about 6.6 million. With less bargaining power, less job stability and more expectations, it’s no wonder people are using AI to try to get through their heavy workloads.

Generative AI makes things quicker and easier. Or does it?

There’s an assumption that AI automatically saves time and allows us to be more productive. However, McLean says, this isn’t necessarily true – because we spend less time ‘creating’ and more time ‘fixing’. In fact, a recent analysis published by workplace productivity software company ActivTrak found that AI users work up to 90 minutes longer each day than non-users.

“Many people are still learning how to use it effectively, which often means multiple prompts, corrections, rewrites and refinements before the output is usable,” she says. “For some people – those that are happy to accept a first draft – AI can make content faster to produce. I'm much less certain it makes work faster overall.

“What saves you time upstream creates a major headache and extra work for the poor person downstream that is literally wading through pages of your AI slop,” she adds. “Sometimes I find myself wondering whether we're creating more value or simply creating more words.”

Multiple studies have highlighted an ‘AI productivity paradox’, where individual workers and companies rapidly adopt AI tools to boost productivity, yet workloads increase and review bottlenecks grow. In 2024, for example, a study by the The Upwork Research Institute found that 96% of c-suite leaders expect AI to boost worker productivity, but 77% of employees report AI has increased their workload, hampering productivity and contributing to burnout. 

And while businesses focus on ‘vanity’ metrics like login rates and prompt volumes, actual productivity – in terms of work being done – is stalling. According to research by Gartner, nearly one in five employees report zero time saved, and up to 52% to 77% report that AI has made their workload heavier or more stressful.

Read more: Why AI is making us treat people worse at work

What can you do if you want to cut back on using AI at work?

We’re growing increasingly aware of the problems associated with AI, from the environmental impact of increased electricity demand and water consumption, to the erosion of our critical thinking skills and cognitive decline. But, it’s easier said than done to say ‘no’ to using AI, especially if your employer wants you to use it.

“I don't think the answer is to reject AI completely – that ship has probably sailed,” says McLean. “Instead, I'd encourage people to think carefully about where they use it and where they don't. I'm very comfortable with AI helping somebody organise information, automate repetitive administrative tasks or summarise large volumes of content. I'm much less comfortable when people stop thinking critically, stop challenging assumptions or allow technology to replace expertise altogether.”

Ultimately, however, it’s up to employers to put clear AI guidelines in place. It’s easy for gen AI to become second nature, but unchecked use for all tasks leaves space for slop to creep in. Without clear guidance for usage, you end up with material that looks polished but is often riddled with small errors and ambiguous meaning, forcing colleagues to recheck, clarify and redo the work. It shows up in consulting reports, audit papers, legal filings, marketing materials, internal updates and more.

To avoid this, McLean says employees should be comfortable asking: Why are we using this? What problem are we trying to solve? Who is accountable if AI gets something wrong? Where does human oversight sit?

Perhaps more importantly, McLean adds, employers need to adjust their expectations of workers and focus more on quality over quantity. “Ultimately, I don't think AI is creating our obsession with productivity,” she says. “More that it is exposing how exhausted many people already are and how much work so many people already have on their plate. Can we blame them for looking for a shortcut?”

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