The Burnout Trap: Why "Saving Time" with AI Might Be Breaking Your Team

Hello H.A.I.R. Community,

I’ve been thinking about this a lot recently and, I’ve been asked the question a few time, now: “What’s the mental impact on automating the mundane tasks with AI?”

We have all heard the pitch. It has been the headline of every HR/Recruitment conference for the last two years: AI will handle the drudgery so your people can focus on the work that matters.

The promise is simple. If we use AI to automate the boring stuff - scheduling interviews, entering data, formatting reports - we liberate our teams. We assume that if we take away the "low value" admin, we will be left with a happier, more creative, and more strategic workforce.

It sounds perfect. But as HR leaders, we are starting to see a different reality emerge.

Instead of feeling liberated, many teams using high-frequency AI tools are reporting feeling more exhausted, not less. They are hitting a wall by 2:00 PM. They describe feeling "fried" or having "brain fog" that they can’t shake.

Why is this happening?

Because we made a fundamental mistake in how we view "boring" work. We thought it was waste. It turns out, it was rest.

The Hidden Value of "Boring" Work

Think about a standard pre-AI workday. It wasn’t eight hours of intense, strategic thinking. It was a mix. You might spend an hour solving a complex employee relations issue, but then you’d spend twenty minutes answering routine emails or filing documents.

We tend to look at those twenty minutes of admin as "lost productivity."

But biologically, your brain looks at those twenty minutes as a recovery cycle.

When you do simple, rote tasks, your brain shifts gears. It steps down from high-alert problem-solving and moves into a lower-energy state. You aren't sleeping, but you are recovering mental energy. These little pockets of "mindless" work act as interval breaks for your brain.

The Problem: We Are Removing the Breaks

Now, look at the workflow we are building with AI.

If an AI agent handles the scheduling, the inbox triage, and the data entry, what is left for the human?

Only the hard stuff.

We are creating days that are composed of 100% high-intensity critical thinking, conflict resolution, and complex decision-making. We have removed the valleys from the workday and left our people to hike a never-ending mountain peak.

I call this Work Densification.

We haven't necessarily increased the hours people are working, but we have drastically increased the intensity of those hours. A recruiter who used to do three interviews a day and two hours of admin is now expected to do six interviews a day with zero admin.

To a spreadsheet, that looks like doubled productivity. To a human nervous system, that looks like a fast track to burnout.

The 4-Hour Reality

Here is the uncomfortable truth we need to confront in workforce planning: Humans are not designed to do eight hours of deep, strategic thinking a day.

Most research suggests the limit for high-quality, intense focus is about four hours.

In the past, the "fluff" of the workday (the coffee breaks, the filing, the waiting for meetings to start) naturally filled up the rest of the time. But AI is stripping that fluff away.

If we expect our teams to fill the time saved by AI with more high-pressure work, we are asking them to do the impossible. We are running their engines at the redline, all day, every day.

So, What Should HR Leaders Do?

We cannot (and should not) stop using AI. The efficiency gains are real. But we need to change how we manage the human side of this transition.

Here are three simple, high-impact actions you can take to prevent "AI Burnout."

1. Redefine "Full Capacity"

We need to stop measuring work by hours and start measuring it by intensity. If a role has been automated so that it is now 100% complex strategy and 0% admin, you cannot expect someone to do that for 40 hours a week. It isn't sustainable.

• The Fix: Acknowledge that "deep work" roles might be full-time at 30 or 32 hours. If the output is higher quality, does it matter if they log off at 4:00 PM? Give the efficiency gains back to the employee as rest, not just more tasks.

2. Bring Back the "Buffer"

In the past, calendars had holes in them—travel time, setting up the room, etc. Now, scheduling tools slot meetings back-to-back with zero gaps.

• The Fix: Implement a company-wide policy for "short meetings." Make 30-minute meetings 25 minutes, and 60-minute meetings 50 minutes. Default the settings in Outlook or Google Calendar. Those 5-10 minutes are not "waste"; they are the neurological reset buttons your people need to survive the day.

3. Don't Automate Connection

Just because AI can write a thank-you note to a candidate or a congratulations email to a team member, doesn't mean it should.

• The Fix: Identify the tasks that keep us human. Writing a personal note is a low-intensity task, but it provides high psychological value. It allows a moment of reflection. Encourage leaders to keep doing the "human admin" manually. It forces a slowdown that benefits the brain and the culture.

The Lesson

The goal of AI in HR shouldn't be to cram more work into the day. It should be to make the work we do more sustainable.

If we strip away all the "easy" work, we strip away the recovery time. As leaders, we need to protect our teams from the efficiency trap. Sometimes, the most productive thing your best employee can do is stare out the window for ten minutes.

Let’s make sure we leave room for that.

One Simple Next Step

Look at your team's calendar for next week.

Do you see solid blocks of colour with no white space? That is a warning sign. Challenge your direct reports to cancel one recurring meeting next week and replace it with a "Do Not Disturb" block. Tell them this isn't for deep work—it's for catching up, organising their desktop, or just breathing. Watch what happens to their energy levels.

Calling for Fair Visibility for All on LinkedIn

If you have been following my posts, you know we have been investigating whether the LinkedIn algorithm suppresses women’s voices. LinkedIn’s Head of Responsible AI recently published a blog post to "clarify" how the feed works.

I have analysed their response. Far from debunking the issue, it inadvertently confirms the exact mechanism of Proxy Bias I identified in my initial report.

Here is the breakdown of why their explanation fails the fairness test:

• The Denial: They spent most of the post denying they use "gender" as a variable. I agree. I never claimed the code said if gender == female. That would be Direct Discrimination. My argument has always been about Indirect Discrimination via proxies.

• The Admission: They explicitly listed the signals they do optimise for: "Position," "Industry," and "Activity."

• The Problem: Men are historically overrepresented in high-visibility industries (Tech/Finance) and senior roles. By optimising for these signals without a fairness constraint, the system systematically amplifies men. Furthermore, the "Activity" signal favours "agentic" (male-coded) linguistic patterns over "communal" ones.

In the UK, this is the textbook definition of Indirect Discrimination under the Equality Act 2010. In the EU, this is a Systemic Risk under the Digital Services Act (DSA).

The Action: Analysis is important, but action is essential. I am supporting the petition "Calling for Fair Visibility for All on LinkedIn," demanding an independent equity audit.Why Does This Happen? It's Not Just 'Algorithm Aversion'

Also, check out this: If a vendor promises their AI is "100% bias-free," they are lying to you. It is mathematically impossible to satisfy all definitions of fairness simultaneously. In this deep dive, I explore why HR leaders need to stop chasing the illusion of neutrality and start building "Bias-Managed" frameworks instead. Read the full article here

Upcoming: Where to Find Me

I will be speaking at several key events in December. I’d love to see you there.

📅 4th December: Job Board Predictions 2026 

I’m joining Alexander Chukovski, Martin Lenz, Cory Kapner, Radu Stoian and (of course) ͌Oras Al-Kubaisi to discuss how compliance and chatbots will interact in the modern recruiting space. Grab a spot

📅 9th December: AI in TA - The 2026 Landscape 

I’m joining a panel with Michael Blakley (Equitas), Khyati Sundaram (Applied), and Jamie Betts (Neurosight) to look at the future of recruitment tech. Save your spot

📅 10th December: Algorithmic Bias Deep Dive 

Hosted by the EWMD Network. This is a 90-minute deep dive into the LinkedIn algorithm changes and the economic impact on women. I’ll be speaking alongside Cindy Gallop, Jane Evans, and other incredible advocates. Register for the Deep Dive

Until next time,

Martyn

H.A.I.R. (AI in HR)

Putting the AI in HR. Safely.

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