Across industries, AI has moved from next to now. Once spoken about as part of future strategy, it is already being implemented into everyday work, plugged in at every level and at speed.
But while the uptake of AI has been fast and furious, the human side of the change is not always getting the same attention. Organisations are investing in (and experimenting with) tools, licences, training and governance, but spending far less time helping people understand how their work is changing, where their judgement and expertise matters most, and how their skills can be redeployed to create mutual value – for the organisation and it’s people.
That is where AI transformation succeeds or stalls. Not at the point a tool is switched on, but in whether people feel clear, safe, incentivised and confident enough to use it well to serve a valued outcome. AI cannot simply be imposed on a workforce; people need to understand it, shape its application and see where they fit within it.
When organisations give proper attention to the human conditions around AI, it can become a powerful driver of value. That means looking beyond technical readiness and asking what needs to be true for people, teams and leaders to adopt AI well: do people trust the purpose of the change, do they understand the future they are moving towards, do they feel safe enough to experiment and challenge, and do they still see where their own value lies?
When those conditions are in place, early productivity gains are far more likely to hold. When they are not, confidence drops, pockets of experimentation stall, adoption becomes cautious or performative, and even the most advanced technology risks becoming another expensive transformation that fails to deliver.
The Era of “Un-working”
AI isn’t just automating tasks; it is rewriting what “good work” is and how it is done. It demands that professionals “unwork”, discarding the habits, rhythms, and structures that anchor their professional value.
This shift is profound because work is much more than a transaction of pay-for-performance. Decades of motivation research suggest that work is the primary arena where we satisfy our essential psychological needs. Work fulfils us across three key ways:
- Agency & Control: The ability to direct one’s own actions and make meaningful choices.
- Mastery & Competence: The deep satisfaction of being “good at something” and refining a craft over time.
- Meaning & Belonging: The conviction that one’s efforts contribute to something significant. Being a vital part of something bigger, connected to a tribe of like-minded peers.
These needs aren’t just “nice-to-haves” that make a job enjoyable; they are the bedrock of human motivation, psychological well-being, and thriving.
That is why the AI transition is so profound. If AI is introduced in ways that constrain agency, deskill professionals, isolate individuals or diminish the visibility of human contribution, it risks undermining the very qualities that make work meaningful for people.
For example, an analyst who loves digging into data to identify new market trends across insight reports may feel their worth or job enjoyment is diminished if it feels like a tool synthesises that data in seconds. The opportunity is not simply to remove the task, but to redesign the role around higher-value judgement, such as interpreting implications of the trends and helping the organisation act on what the data shows.
This matters for teams as much as individuals. AI can disrupt team trust and psychological safety by inadvertently disrupting the collaborative dynamics necessary for high performance. When it’s unclear how much human oversight has been applied to AI output, teams face a human-AI oversight paradox: members may relax their own sense of accountability, assuming the “all-knowing” tool is correct. This lack of transparency regarding effort and contribution weakens coordination, fuels resentment, and ultimately degrades the team’s collective learning velocity.
If we undo how people need to work without understanding the human needs of work, we risk undermining the things that make people want to contribute. But if AI is introduced with the grain of human motivation, it can make work more meaningful, not less.
The Second-Mover Advantage: Learning from early adopters
When I talk to clients, there can be a heightened sense of competitive pressure in the room when we discuss AI. Leaders can feel they are falling behind competitors, with this urgency driving a race to be “AI-first,” but moving fast is rarely the same as moving well.
Organisations that rush straight to mass adoption can suffer from workforce “brain fry”: an initial spike in efficiency followed by a drop-off as teams hit cognitive overload, decision fatigue and anxiety about what AI means for their future value. This happens because when people feel overwhelmed or professionally threatened, they do not innovate; they entrench in old ways of working.
This is where the “second-mover advantage” comes in. By prioritising the cultural readiness before scaling, organisations avoid the hidden risks – compliance breaches and trust breakdowns – that can hinder first-movers. Going slow to go fast offers strategic protection by building the confidence needed to eventually scale rapidly.
From Reduction to Redeployment
We need to be honest: if the only metric for AI success is cost reduction, we are facing a long-term risk of short-termism. If organisations stop hiring junior talent because “AI can do it,” they save money today while quietly hollowing out their future leadership pipeline.
Basic tasks – the very things AI handles best – are the essential apprenticeship grounds where junior employees build the judgment and expertise needed to lead in ten years’ time. Remove this learning ground without redesigning professional development, and organisations will face a “missing middle” of leadership by the next decade.
The strongest businesses are moving beyond efficiency to redeploy talent and unlock growth. IKEA offers a useful example: when their “Billie” chatbot began handling 53% of customer inquiries, they didn’t just bank the savings. Instead, they retrained 8,500 co-workers into roles like remote interior design, helping generate an additional €1 billion in revenue.
The Leadership Roadmap
For the C-suite, the mandate is clear: AI transformation needs a behavioural change strategy, not just a technology roadmap. Leaders should ask five questions before scaling.
- What value are we creating beyond efficiency?
- What human needs will this change affect?
- What behaviours and incentives need to shift?
- What invisible risks are we tracking?
- How are we protecting the next generation of expertise?
AI is not only a tool; it is a mirror reflecting what an organisation values and trusts. Handled well, it can make work more creative, purposeful, and human. The organisations that get this right will be those that understand the human conditions needed for AI investment to become lasting value.
