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4 day work weeks = 35% more revenue. But the real takeaway: deliverables > hours worked.
If you missed it, 4 Day Week Global ran a study of 61 UK companies who moved to a 4 day, 32 hour work week. Paid the same. The employee results weren’t a surprise to anyone: better sleep, less stress, better mental health, less resignations, etc.
👉What shocked everyone: revenue rose 35% at these companies (on average.)
And 56 of those companies said “F it, we’re keeping this going forever.”
So…how?
Without looking at the management model of each company in the study, there’s 2 logical conclusions:
1. Working 32 hours at 100% is better than working 40 hours at 50%.
(Note: made up percentages for illustration purposes.)
We all know what it’s like to fire on all cylinders. And to mail it in because we’re burned out. Productivity wise, you get more done in less time when you’re sharp and focused.
2. Any rational manager who is cutting hours but still needs to meet their goal is going to make deliverables the focus. Not the hours spent at the desk.
The real question is: with the modern work world is hyper focused on results and growth, why are hours worked even a concern for salaried employees to begin with? What Jedi mind trick did previous generations play on us?
Look at freelance workers. You can pay for hours worked. Or you can pay a flat rate for deliverables.
Most businesses like paying flat rates for projects. Costs are predictable. And they only really care about outcomes. As long as the work is done well and on time, no one cares for second how many hours went into it. Or when.
The salary world really is an ongoing slate of deliverables at a fixed cost, with the benefit of more predictability and stability. (Jury’s out on the last one tbh.)
It just “feels” like hours should be tracked, too. Because that’s how we’ve always done it.
Granted, there’s some limitations here. Professional services businesses can’t 4 days until their clients do. And no, I didn’t miss that the org conducting at it is called “4 Day Week Global” – there’s a motivation for curve fitting data if I’ve ever seen one.
👉I’ll promise you one thing: if the business magnates of 1900’s had data showing they’d be richer by working people less, they absolutely would have.
Partner at Hirewell. #3 Ranked Sarcastic Commenter on LinkedIn.
Over the last year, hiring teams have started seeing a wave of new job titles pop up across tech, sales, and operations.
Some are legitimate new roles.
Others are existing jobs with a slightly different name.
And many of them have one thing in common: AI is suddenly part of the job description.
From Go-to-Market Engineers to AI Specialists, companies are experimenting with new roles as they figure out how automation and AI fit into their teams.
But most of these positions aren’t entirely new. They’re evolutions of existing roles.
One role that’s gaining traction is the Go-to-Market Engineer.
Depending on who you ask, it’s either:
In practice, it’s a bit of both.
We recently worked on a role called an Outbound & Go-to-Market Specialist. Instead of traditional RevOps work like reporting and CRM management, the focus was on:
The goal wasn’t just managing sales data. It was accelerating pipeline generation through automation.
In other words, the role was designed to help SDRs and AEs move faster.
One trend is becoming clear: companies aren’t replacing entire departments with AI.
Instead, they’re changing how existing roles operate.
Sales teams still need pipeline.
Marketing teams still need content.
Engineering teams still need to build software.
The difference is that employers now expect candidates to use AI tools as part of the workflow.
That’s why we’re seeing so many job titles that start with “AI.” But that may not last forever.
Right now, AI still feels new enough that companies highlight it in job titles.
But eventually, AI will likely become a baseline expectation, not a specialty.
Think about it like cloud technology or data analytics.
At first, companies hired “cloud specialists.” Now most engineers are expected to understand cloud infrastructure.
The same shift will likely happen with AI.
Instead of hiring “AI-enabled marketers” or “AI engineers,” companies will simply expect employees to know how to work with AI tools.
One challenge with these emerging roles is simple: there aren’t many candidates with real experience yet.
Many of these positions didn’t exist two years ago.
In one recent search, we started looking for a candidate locally in Chicago. Eventually we expanded nationwide because the pool of people with relevant experience was extremely limited.
This is a common issue with emerging roles:
That gap will likely persist for the next few years.
Another noticeable shift is that roles are becoming more hybrid.
Instead of hiring for narrow responsibilities, companies are combining multiple functions into one position.
For example:
Forward Deployed Engineers
A model popularized by Palantir, these engineers:
That used to involve several roles: product managers, engineers, and solution architects.
Now, AI tools allow one person to cover more ground.
Similar changes are happening in other functions as well:
Automation removes repetitive tasks, leaving more strategic work behind.
For employers, the takeaway is straightforward.
Job descriptions need to evolve alongside technology.
Instead of focusing only on traditional experience, hiring managers should consider:
Because in many cases, the perfect candidate with the exact title simply doesn’t exist yet.
We’re currently in a transitional phase in hiring.
AI is changing how work gets done, which means job titles, responsibilities, and expectations are shifting quickly.
But most of these “new” roles aren’t entirely new professions.
They’re existing jobs adapting to new technology.
And as companies continue experimenting with AI, the titles may keep changing.
The work itself, however, is likely to look familiar.