Happy Wednesday, everyone. I'm Frank Richardson, an organisational psychologist observing the workplace with curiosity and care. Each week, I share insights to help HR leaders better understand the people behind the processes and build cultures where both individuals and organisations can thrive.
This week in workplace whiplash 🌀
This week reminds us that technology may be changing work, but it hasn't made it any simpler:
🤖 Employees are spending almost a full workday 'botsitting' AI
A new report found employees spend an average of 6.4 hours every week supervising AI, correcting errors and turning AI-generated outputs into something they're comfortable submitting. Even more concerning, almost one in three employees admitted they've submitted work they couldn't fully explain if someone asked how they produced it.
👉HR Dive💰Trump Accounts arrive, but employers are waiting
The new Trump Accounts officially launched this month, giving employers the ability to make tax-advantaged contributions towards savings accounts for employees' children. While many children have already been enrolled, HR experts say most employers are holding back until payroll systems, vendors and government guidance catch up.
👉HCA Mag⚖️ Amazon sued over alleged FMLA leave errors
Amazon is facing legal action after an employee alleged he was dismissed because HR systems incorrectly recorded his approved FMLA leave, despite repeated attempts to have the records corrected. The case is a timely reminder that when leave management relies on multiple systems and processes, administrative errors can quickly become legal risks.
👉HR Dive
Which brings us to today's article. As AI becomes embedded in our workplaces, are we thinking about capability in the right way?
🤝 In partnership with Leapsome
Most HR teams manage talent strategy with their worst data, scattered across ATS, HRIS, and review tools that never talk to each other. Leapsome connects the whole employee journey, from first interview through reviews, engagement, and compensation, into one people data foundation. That depth is what lets AI raise the impact of every employee, not just clear admin. Over 2000 companies already run their talent strategy on Leapsome. AI that raises your talent density, built on the deepest people data foundation in HR.
See the Leapsome HR Platform in action at leapsome.com/talent-density
If you've spent any time around HR lately, you've probably heard the words 'skills mapping' enough times to consider it a personality trait. It's the corporate equivalent of a fire drill. A new technology arrives and everyone rushes to update the competency framework, identify the capability gaps and launch a few learning pathways. AI followed exactly the same script, filed neatly under "AI literacy."
But that approach rests on one important assumption: that AI is simply another capability to add to the framework.
A recently published paper in the Academy of Management argues that one of the biggest AI challenges organisations face is helping people know when to trust AI, when to question it and when to ignore it altogether.
That points to a different way of thinking about capability altogether. One where AI works less like a new skill on the list, and more like a shift in how the existing ones need to interact.
Has AI changed your capability framework yet?
🧠The behavioural science lens
Behavioural science shows us that as our environment changes, the capabilities that create value change with it:
Using AI well is really about calibrated trust: The Academy of Management paper reframes the core AI skill as judgement: knowing how much weight to give an AI output before acting on it. Getting that calibration right takes scepticism, experience and a good feel for when something's off, built up over time rather than taught in a single training session.
Our brains naturally get this wrong: Behavioural scientists have identified two predictable traps. Automation bias leads us to over-rely on technology simply because it appears competent, while algorithm aversion causes us to swing too far in the opposite direction after seeing AI make a single obvious mistake. Both distort good judgement, just in different directions. The goal isn't to trust AI more or less, but to trust it appropriately.
AI capability is really a combination of human capabilities: The World Economic Forum predicts growing demand for skills such as analytical thinking, curiosity, resilience, and leadership over the next few years. AI places greater weight on these capabilities because they determine how effectively people work alongside the technology. Critical thinking helps us spot weak reasoning. Ethical judgement helps us recognise unintended consequences. Curiosity encourages us to test AI's assumptions instead of accepting them at face value.
🚀What this means for leaders
Here's what that shift looks like in practice.:
Stop treating AI as a standalone capability: Someone can write brilliant prompts and still make poor decisions because they accept every AI recommendation without question. Equally, someone with excellent judgement but little confidence using AI is unlikely to get the best from it. AI capability emerges from the way critical thinking, curiosity, ethics, communication and judgement work together.
Rethink your skills architecture: Capability frameworks tend to grow over time, with every new challenge becoming another competency to assess. AI offers an opportunity to step back and ask a different question. Rather than adding another row to the spreadsheet, ask which existing capabilities have become significantly more important because people are now working alongside AI?
Reward human judgement: AI can generate options, identify patterns and produce remarkably convincing answers. But responsibility, competing values and contextual judgement remain deeply human capabilities. Those capabilities have always mattered. AI simply makes them easier to overlook and more valuable when they're present.
💬 Final thoughts
We're spending a lot of time asking what new AI skills our people need to develop but a more important question is whether our capability frameworks still reflect what expertise actually looks like.
If AI is changing the value of the skills we already have, then skills mapping becomes something very different. The challenge isn't squeezing another competency into an already crowded framework. It's recognising how judgement, curiosity, ethics, critical thinking and decision-making have become the capabilities that tie everything else together.
How's the depth of today's edition?
If something here speaks to you, I’d love to hear it.
Until next week,
Frank
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