“Understanding ourselves, navigating the world around us, making sense of others, and recognising the impact we have on them. Because who are we, really, if not shaped by one another?“
Happy Wednesday, everyone. I’m Shaida Darian, a Business and Organisational Psychologist. I’m fascinated by three questions: Why does this happen? What can we do about it? And when should we act? Join me down the rabbit hole. With any luck, we’ll find Wonderland.
This week in workplace whiplash 🌀
The More Employees Use AI, the More They Fear It
Gallup finds frequent AI users are more than twice as likely to fear their jobs will disappear within five years than less frequent users. The twist: supportive management substantially reduces that gap. Apparently, familiarity does not always breed confidence; sometimes the people using AI most can see its implications most clearly.
Read the story — Gallup, 9 September 2026Channel 4 Cuts 28% of Its Workforce to Become “Simpler”
Channel 4 plans to remove around 340 roles, representing 28% of its workforce, while reducing management layers and duplication to create a leaner operating model. The aim is faster decision-making and lower non-programming costs. The unresolved question is what happens to decision quality when simplification also means substantially fewer people carrying the work.
Read the announcement — Channel 4, 9 September 2026Employment Reform Comes With a Management Bill
New CIPD data on zero- and low-hours reforms finds almost half of employers expect implementation to be difficult, while around two-thirds anticipate higher HR and management time and cost. Stronger worker protections may improve predictability, but legislation still has to be translated into everyday decisions by managers already working inside increasingly complex employment systems.
Read the research — CIPD, August 2026
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Thursday Reflections
As I look through the latest workforce news, I notice the exact same pattern. Another corporate announcement drops, packed with terms like "transformation," "restructuring," and "preparing for the AI era." And almost instantly, the online narrative collapses into four simple words: AI is taking jobs.
It’s a clean story. But every time I see it, I find myself pausing.
I’m Shaida Darian. As a Business & Organisational Psychologist, my work is all about looking behind closed doors at how decisions actually get made. And the reality is far messier. AI doesn't make decisions; it creates capacity. If a new tool saves your team 100 hours a month, what happens next isn't automatic. Do you trim headcount? Take on new projects? Give people some breathing room? Raise the bar on quality?
Technology changes what is possible. Leadership still chooses what happens next.
Which brings me to the question: when an organisation says AI caused the job cuts, did the technology determine the outcome, or did it simply change the options available to leadership?
The organisational psychology lens
When complex organisational decisions have several plausible causes, we tend to simplify them. Psychologists describe this through attribution and sensemaking: we construct explanations that make uncertain or uncomfortable events easier to understand. In organisations, those explanations also shape where responsibility appears to sit.
A redundancy programme rarely has one cause. AI adoption may coincide with weaker demand, previous overhiring, margin pressure, restructuring, changing skills requirements and investment in new technology. Yet “AI-driven” compresses those interacting pressures into a single, intuitively coherent explanation. That does not necessarily mean leaders are being deceptive. Under causal ambiguity, even decision-makers may understand a complex restructuring through the most salient story available to them.
The evidence reflects that complexity. Direct substitution is real, particularly where work consists of discrete, repeatable tasks. Duolingo, for example, reduced some contractor work as generative AI took on parts of content production. But broader evidence points to a more mixed labour-market effect. Harvard Business School research finds declining demand in highly automatable work alongside increased demand for roles involving more complex human-AI collaboration. Firm-level research also shows that heavy AI adoption does not automatically translate into lower overall employment.
This is why the language matters. Calling a redundancy “AI-driven” can make a managerial decision sound like a technological outcome.
AI may change the options available to an organisation. It does not decide which option leadership chooses.
What this means for leaders
Understanding attribution leads to a practical boundary: leaders should not treat workforce outcomes as technologically inevitable. AI changes what an organisation can do; leadership determines what happens to the capacity it creates.
Principle 1: Separate capability from decision
Before describing redundancies as AI-driven, establish what the technology has actually replaced. Map the tasks being automated, measure the capacity created, and determine whether the role itself has diminished or simply changed. Confusing task automation with role substitution risks removing the human judgement, context and exception-handling that remain after routine work disappears.
Principle 2: Decide where the productivity dividend goes
AI efficiency creates a productivity dividend, but its distribution is a strategic choice. Time saved can become lower headcount, greater output, workload relief, better service, redeployment or investment in growth. Where demand can expand, productivity gains may support employment rather than reduce it. The technology creates the surplus; leadership decides how that surplus is used.
Principle 3: Communicate causality accurately
When restructuring occurs, explain the full mix of drivers. Separate direct task substitution from capital reallocation, capability shifts, cost pressure and broader restructuring. Using “AI transformation” as a catch-all explanation may make a complex decision easier to communicate, but it also risks obscuring where managerial choice begins. Clear causal accounting is not merely a communications issue; it is part of maintaining credibility and trust.
Final thoughts
“AI-driven” does not mean inevitable. AI may create the capacity to do more with less, but leadership still decides whether that capacity becomes redundancies, redeployment, growth, or better service.
The critical question is not simply whether AI can replace human work.
It is who decides what happens to the productivity it creates.
How's the depth of today's edition?
Until next week,
Shaida
P.S. If AI suddenly made your team 20% more productive, what would you want leadership to do with that extra capacity?
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