Just as the internet and social media produced their own native generations, in a few years we will begin to see the first wave of true AI natives enter the workforce. These will be people who have not known a world without AI and have grown up using AI tools instinctively in education and everyday life.
These people are now entering the workforce. Employees may view this as either exciting or disruptive – or maybe a bit of both – depending on their own AI readiness and adoption timelines. These new employees, many of whom are likely to take up grad and entry-level jobs, will not need to be persuaded that AI can help them do their jobs faster and more efficiently. They will expect the technology to be everywhere and view its absence with frustration and incredulity.
Meanwhile, many organisations are still in early or experimental phases. They may be using AI to take meeting notes or automate routine communications. Those further ahead of the curve are piloting more advanced tools, drafting governance policies and exploring where automation can safely remove friction from reporting and analysis. The approach is thoughtful and measured – understandably so, given AI’s proximity to sensitive data, compliance and forecasting.
Experimentation vs normality
But AI natives will not see this technology as experimental. To them, it’s just normal – like using a search engine rather than visiting a library to carry out research. Where established professionals may integrate AI into existing workflows, AI natives are more likely to structure work around it from the outset. Prompt-based thinking is not a specialist skill for them; it’s simply how they start tasks:
- Clearly define the objective
- Set constraints
- Iterate quickly
- Refine outputs
That shift in starting point creates a clear expectation gap between AI natives and organisational processes and infrastructure. The question inside these organisations then moves from “Should we use AI here?” to “Why aren’t we using AI here?” That’s where friction can build, and people start to take sides.
Skills transfer and mentoring
This has practical implications beyond mindset. In accounting and finance, for example, early-career development has traditionally been built on learning, repetition, and mastery. Processing invoices, reconciling accounts and compiling reports may be routine, but they build pattern recognition and commercial instinct. They also teach sector-specific ethics and diligence. Just as importantly, they place juniors alongside experienced leaders who can explain why something looks wrong, not just tell them that it is.
As automation becomes more capable, much of that transactional layer will shrink. AI can categorise data, flag inconsistencies, and generate draft reports in seconds. That improves efficiency, but it also alters the training ground. If these foundational tasks are handled by systems, who do future finance leaders learn from, and how? And what skills will they be developing as they’re mentored?
The role itself may evolve towards interpretation, modelling and forward-looking analysis. That could be a positive shift – one the industry has long called for. But it will not simply happen by default. Organisations, and to an extent educational institutions, will need to deliberately design learning, ensuring professionals understand the logic behind the systems they supervise.
The risk isn’t that AI removes expertise, but that expertise is assumed rather than cultivated. It’s an issue that extends beyond finance and accounting, with implications for the wider UK economy.
Removing the temptation of shadow AI
There’s also the risk of informal workarounds – or shadow AI. If official AI use is unclear or restricted, some employees may turn to unsanctioned tools to bridge the gap. This is more likely due to old habits than because of defiance or nefarious inclinations. However, as organisations learned during the rise of mobile and cloud technologies, shadow adoption can create governance blind spots. In the worst cases, sensitive data can be exposed, audit trails can be lost, and accountability can become blurred.
Ideally, organisations should find ways to channel rather than suppress AI native behaviour. Structured guardrails, clear oversight and visible confidence thresholds can create a balanced environment. In practice, that could mean introducing systems that flag levels of certainty, require validation before outputs are finalised, or make underlying assumptions transparent to the user – and their line manager. Governance must be embedded within the workflow itself, not layered on afterwards as a compliance check or box-ticking exercise.
Such frameworks allow cautious users to build trust gradually, while enabling AI native professionals to work efficiently within defined boundaries. AI becomes embedded, but not unchecked, and human judgement remains central. Over time, this kind of structured adoption will evolve from a purely technical decision into a factor in talent retention. Organisations that can confidently say “AI is part of how we work, and here is how we control it” are more likely to attract and retain professionals who expect intelligent systems as part of their everyday toolkit.
Conclusions
Looking ahead, the long-term implications are significant. As AI natives begin leading teams, expectations around productivity and expertise will shift. Productivity will be measured more by orchestration – how effectively systems are configured, supervised and interpreted – and less by manual throughput. Expertise will also centre on understanding where automation adds value and introduces risk, and where human judgement remains indispensable.
Leadership itself will evolve, too. Finance leaders, for example, will be expected to understand the numbers. But they’ll also have to understand the systems that produce them. That means knowing how models are trained, how outputs are validated, and where bias or error might enter the process. The ability to interrogate AI will become as important as the ability to interpret financial statements.
This transition won’t happen overnight. But it is coming. AI natives won’t ask whether the technology should be part of their workflow; they will assume that it already is. So, the question for leaders is simple: are your processes, governance structures and training pathways ready for that shift?
Charis Thomas
