A year ago, Tobias Lütke, CEO of Shopify, triggered a much bigger debate than a simple hiring policy. In a memo shared publicly in April 2025, he stated that teams would need to show why AI could not do the work before asking for more headcount or resources. He also described AI usage as a baseline expectation inside Shopify.
What made that moment so significant was not the rule itself. It was the shift in logic behind it.
For a long time, many organisations followed a familiar equation: more work meant more people. Rising workload justified additional headcount. Activity signalled need. And in many environments, being visibly busy still functioned as a proxy for contribution.
That logic is now under pressure: AI is forcing organisations to ask a different question. Not simply whether there is more work to do, but how that work should be done, by whom, and with what combination of technology and human capability. The Shopify memo made that shift unusually explicit, but it reflects a much broader pattern in the workplace.
From visible effort to visible value
One of the most consequential changes in the AI era is that effort is becoming less visible as a measure of contribution.
For years, many workplace cultures rewarded intensity almost by default. Long days, full calendars, constant responsiveness and visible busyness all signalled commitment. In some organisations, they even signalled importance.
But once AI enters the workflow, the equation starts to change. If a task that used to take five hours can now be completed in one, then effort alone becomes a less persuasive indicator of value. The focus shifts more quickly to output, speed, quality and judgement. That does not necessarily make work easier. It makes contribution more exposed.
And that is precisely why this moment matters so much for engagement.
Why this makes engagement more honest, but also more fragile
In one sense, this shift is healthy.
It creates a more honest conversation about what contribution actually looks like. It forces organisations to distinguish between real value creation and habits that merely look productive. It reduces the comfort of equating motion with impact.
But it also makes engagement more fragile, because people do not experience this shift in the same way.
Some employees accelerate. They experiment, adapt quickly and feel energised by the opportunity to work differently. Others experience a very different reality. They may feel exposed, uncertain or quietly threatened by a new standard they do not yet fully understand.
This is where the conversation becomes more interesting than the usual AI optimism versus AI fear debate.
The same workplace change can produce very different emotional and behavioural responses, depending on how people interpret what is happening and what they believe it means for their future.
The real issue is not technology, but interpretation
Most AI discussions still overfocus on tools.
What matters at organisational level, however, is interpretation. What do people think this shift is asking of them? Do they see it as an opportunity to focus on higher-value work, or as a signal that their current value is eroding? Do they understand how their role evolves, or do they simply feel that the bar has moved again?
These questions are not technical. They are deeply human. And they are exactly where leadership starts to matter more than technology itself.
Because when the old markers of contribution begin to disappear, people need help understanding what now counts, what still matters and how they are expected to adapt.
Leadership now has to redefine contribution
This is why the AI conversation is also a leadership conversation.
Once “being busy” is no longer enough, leaders can no longer rely on legacy assumptions about productivity, performance and engagement. They have to redefine contribution more clearly.
What kind of work should people still own?
What should AI handle?
What capabilities become more valuable, not less?
What does strong performance now look like in practice?
If leaders are vague on those questions, anxiety fills the gap. If they are clear, AI can become a catalyst for better work rather than a source of confusion and hidden pressure.
That is the real challenge. Not whether organisations adopt AI, but whether they are capable of guiding people through the meaning of that adoption.
One workplace, very different experiences
What I find most interesting is not the technology itself, but the divergence it reveals.
The same shift can energise one person and destabilise another. It can increase clarity for some and create self-doubt in others. It can sharpen performance in one team while weakening trust in another.
That is why engagement becomes more complex in an AI-shaped workplace. Not because engagement matters less, but because the conditions under which people experience value, pressure and recognition are changing so quickly.
This means organisations will need a more nuanced understanding of employee experience than before. They will have to look beyond adoption rates and productivity gains and pay closer attention to how people are making sense of the shift underneath.
Conclusion?
The most important consequence of AI at work may not be automation. It may be the end of a long-standing illusion: that busyness, on its own, is enough to prove contribution.
What replaces that illusion will shape not only performance, but also confidence, meaning and engagement inside organisations.