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The Workforce Lens I dig into the future of work — DEI, AI, workforce trends — and share insights HR leaders actually need, minus the corporate fluff.

A promotion can take someone away from the work they are good atA promotion can take someone away from the work they are...
16/10/2026

A promotion can take someone away from the work they are good at

A promotion can take someone away from the work they are good at—and put them in charge of other people.

That is not always a bad move. But the skills that make someone a strong individual contributor do not automatically make them a strong manager. One study of sales workers found that higher sales performers were more likely to be promoted, while stronger pre-promotion sales performance was associated with lower performance from their new teams.

The article looks at what that means for how organisations choose managers, and why the job itself can be hard to do well when admin and urgent problems crowd out coaching.

Have you seen promotion treated as the only way to move forward?

https://zurl.co/VxqCJ

A look at who actually gets promoted into the role, and why that might explain more than another survey on generational attitudes

Everyone loves complaining about Gen Z not wanting to be managers. Lazy. Entitled. Cannot handle responsibility. You hav...
11/10/2026

Everyone loves complaining about Gen Z not wanting to be managers. Lazy. Entitled. Cannot handle responsibility. You have heard it all before.

Turns out there might be a much better reason, and it has nothing to do with laziness.

A study looked at over fifty thousand salespeople and found something wild: the people most likely to get promoted into management were often the ones least likely to actually be good at it. Being great at your own job kept winning people the promotion. The thing that actually made someone a good manager afterwards barely mattered at all when it came to who got picked.

So maybe it is not that this generation does not want to lead. Maybe they are just the first ones watching this happen up close, with somewhere else to go, and deciding it is not worth signing up for.
I wrote a whole piece digging into this for anyone who wants the full story (and the numbers behind it). Link in the comments if you are curious.

https://zurl.co/B3jF3

A look at who actually gets promoted into the role, and why that might explain more than another survey on generational attitudes

What responsible use looks likeResponsible AI use is not just about choosing a good system.It is also about making sure ...
02/10/2026

What responsible use looks like
Responsible AI use is not just about choosing a good system.

It is also about making sure someone can question the result, understand the route that produced it, and stop the process when the stakes are too high for automatic handling.
That sounds obvious. In practice, organisations often rely on a job title, a policy document, or a final approval box and assume the work is done.

The real test is more concrete. Can the person responsible see enough, know enough, and act early enough to make a difference?

That is the question running through my article on AI agents and accountability.

Read it here: https://zurl.co/wK28R

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

The practical test for accountabilityHere is a simple test.If something goes wrong, can the person named as responsible ...
28/09/2026

The practical test for accountability

Here is a simple test.
If something goes wrong, can the person named as responsible explain what the system was told, what it saw, what it did, and where they could have stopped it?

If not, the organisation may have assigned responsibility without assigning real control.
That distinction is easy to miss when a process looks neat on paper. An org chart can be clear while the decision path underneath it is not.

My article explores why this matters for AI systems, but the question applies more widely. Any organisation that delegates a consequential decision needs a record that people can actually use.

Read it here: https://zurl.co/WOxfi

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

Labels change how carefully we lookA small change in language can alter how people behave.If a piece of work comes from ...
25/09/2026

Labels change how carefully we look

A small change in language can alter how people behave.
If a piece of work comes from a tool, we tend to inspect it as something we are using. If it comes from an “employee”, we may start to treat it as someone else’s contribution, with someone else carrying part of the responsibility.

That is why the study discussed in my article matters. Managers caught fewer errors when the same work was framed as coming from an AI employee rather than a tool.
The concern is not the word itself. It is the shift in attention that can follow it.
When an organisation gives an AI a human role, it needs to be very clear about which human duties have not moved anywhere.

Read it here: https://zurl.co/4Cn5F

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

A name on the chart is not controlPutting an AI agent on an org chart may help people understand where it sits in a proc...
21/09/2026

A name on the chart is not control

Putting an AI agent on an org chart may help people understand where it sits in a process.

It may also create a false sense that someone has given it a proper role and someone else has proper control over it.
Those are different things.

A real role comes with authority, limits, supervision, and a way to challenge decisions. If the person listed as responsible cannot see the agent’s instructions or stop its actions, the chart is giving us a description without much control behind it.

The article asks what organisations are really saying when they call an AI a worker, an employee, or a boss.

Read it here: https://zurl.co/QBRUt

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

This problem did not start with robotsOrganisations have always found it difficult to assign responsibility when a decis...
18/09/2026

This problem did not start with robots

Organisations have always found it difficult to assign responsibility when a decision is spread across departments, committees, and layers of approval.

AI makes the difficulty more visible because the chain can include systems that pass work to one another without anyone having a complete view of the process.
The basic human problem remains the same: people are expected to answer for decisions they may not have been able to see or change.

That is why the article looks back at corporate law as well as forward to AI regulation. The history is not a side note. It shows what happens when an accountability model is applied to a structure it was not designed for.

Read it here: https://zurl.co/bznQT

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

Suppose the decision cannot be explainedAn AI system rejects a loan.The employee who reviews the case did not build the ...
14/09/2026

Suppose the decision cannot be explained
An AI system rejects a loan.

The employee who reviews the case did not build the system. The manager who approved its use did not see the final recommendation. Somewhere in the process, another system altered the information that came through.
The customer asks why the decision was made.
Who can answer?

That is the part of AI accountability that interests me most. Not only who gets blamed when something goes wrong, but whether anyone can still explain what happened in a way that is honest and useful.

The new article follows that question through law, corporate governance, and the way organisations are beginning to describe AI at work.

Read it here: https://zurl.co/Tgokq

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

Every AI liability law so far does the same thing: it decides who to blame without deciding what blame actually requires...
13/09/2026

Every AI liability law so far does the same thing: it decides who to blame without deciding what blame actually requires.

California blocks one legal defence. The EU treats AI like a product. Singapore says outright that AI agents are not legal persons. Different tools, same move underneath, push responsibility onto a human by default, because the harder question is not answered yet.

Traced that same gap back further than AI. Decades ago, corporate law hit the identical wall, on people, not machines, and a major fraud case collapsed for basically the same reason an AI agent escapes accountability today.

➡️ In my latest piece:
👉 Why the standard "AI cannot suffer, so it cannot be accountable" argument is weaker than it looks
👉 The counterargument some legal scholars now take seriously, that AI may eventually need something like legal personhood
👉 What a real study found happens to human vigilance the moment work gets relabelled "AI employee" instead of "tool"
👉 The old corporate law precedent showing this gap is not new, and was never really about AI at all
👉 Why the piece ends without resolving its own argument, and what that leaves every organisation still needing to work out

https://zurl.co/CVoxP

New liability rules assign responsibility for AI failures while leaving the harder question unresolved

Do not make endurance the business modelSome managers appear to thrive in player-coach roles because they are carrying a...
04/09/2026

Do not make endurance the business model

Some managers appear to thrive in player-coach roles because they are carrying an unreasonable load with remarkable discipline.

That can conceal the design problem.

The work gets done. The meetings happen. The team receives enough attention to avoid an obvious breakdown. Meanwhile, the manager’s own recovery time, thinking time, and capacity for difficult conversations disappear.

A role should not be judged sustainable simply because a strong person has managed to survive it.

The better test is whether the arrangement leaves enough room for quality technical work and quality management without requiring constant personal sacrifice.

https://zurl.co/WJHMe

Why dual career ladders, ownership roles, and player coach managers work in theory

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