Could AI make the 3.5-day workweek a reality?
Nearly every industry has begun to adopt AI at some level. Studies reveal that, on average, employees are saving 1.75 hours each day through AI—that’s more than a full day of work saved every week.
Artificial intelligence (AI) is fast becoming a permanent fixture in the modern work landscape. It is especially true since the advent of large language models (LLM)-powered tools, which broadcast the productivity boost of generative AI (Gen AI), leading many companies to adopt AI wholeheartedly.
Nearly every industry has begun to adopt AI at some level. Studies reveal that, on average, employees are saving 1.75 hours each day through AI—that’s more than a full day of work saved every week.
As the efficiency of new AI systems begins to improve, that number will likely increase. Some sales teams claim they’re using AI to save up to four hours of work per day by eliminating a lot of the manual, time-consuming aspects of chasing leads. In other words, sales teams are eliminating up to half of their normal workday by using AI, half of it.
All of this recently prompted Jamie Dimon, CEO of JPMorgan Chase, in an interview with Bloomberg Television, to predict that the use of AI could lead to a 3.5-day workweek.
Employees are already expecting more control over their schedules and prioritising more flexible work environments. So, it's entirely possible that workers will eventually achieve a work environment that provides a livable wage at somewhat less than the standard five days a week.
On the other hand, with the understaffing that many companies are facing at the moment, it doesn’t seem likely that employers will give in to a shorter workweek any time soon.
Instead, they’re more likely to enjoy the productivity gains that AI provides. In the short term, it seems more likely that companies will be expecting employees to use AI to work smarter rather than working fewer hours.
Using AI responsibly
Before your company can enjoy the productivity gains of AI, you need to ensure you’re adopting new technologies safely. For example, you might be worried about privacy issues if workers put any kind of sensitive information into commercial systems with LLMs. Or you might be concerned about legal liability if some information you provide with the help of AI happens to prove false.
To avoid those kinds of situations, your first step should be to create a framework for the responsible use of AI—a comprehensive policy of do’s and don’ts. For example, employees should always check any facts that AI comes up with, and they should know not to input sensitive company information.
These rules are a good start to ensuring that all employees are using AI in a way that maintains a consistent quality of output while ensuring safety and legal compliance.
Using AI to the fullest
Once you’ve established the foundation of responsible AI, here are a couple of things you can do to ensure you’re using the technology to its fullest. I would start with standardising workflows and personalising AI systems to your company’s needs.
This involves creating uniform procedures and guidelines for how employees interact with and utilise AI for their tasks. For example, creating content plans or client presentations could mean establishing templates for prompts, developing training resources, and implementing a standard review process to ensure that AI-assisted content aligns with company standards.
Next, you should personalise your internal AI systems for your workers and clients—tailoring AI tools to meet the specific needs of employees according to your industry and clientele.
Take for example an ecommerce company that specialises in outdoor and fitness equipment. The company might develop an AI system to track seasonal purchasing trends in sales of hiking gear in the spring and summer months.
It could also create a system to measure customer engagement with specific product categories, like running shoes or camping equipment. The company could train the AI system on purchasing and engagement data from previous years to make it highly specific and specialised to its needs.
The same company could create an AI system for the finance team to take seasonal sales numbers and forecast budget allocations for inventory purchases during high-demand periods.
It could also monitor the amount paid for marketing campaigns compared to the return on investment for each product category, and also detect anomalies in payment transactions, like sudden spikes in shipping expenses or major changes in supplier costs.
By contrast, at a healthtech company, which provides subscription-based telemedicine services, the company might want to track things like patient engagement metrics with different telehealth services, patient retention rates, and platform sign-ups.
The important thing is that you’re going beyond the generic informational output that an LLM-powered system might produce and ensuring that the results are highly specific and usable.
So, about that 3.5-day workweek...
It seems that we’re not going to see any move to a 3.5-day work week, at least not until companies have a chance to realise all the insights and productivity gains that AI can bring to their daily operations.
For now, AI will simply let us do more in the five days we have. While that may change in the future, until it does, using AI responsibly to streamline daily processes will likely become the norm.
Aditya Nair is the Head of Developer Fulfillment at Turing Enterprises Inc.
Edited by Suman Singh
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)
