The rise of AI-native employees: why the next wave of adoption won’t come from the boardroom

When generative AI burst into public view in late 2022, many organisations began asking what it would mean for learning, leadership and work itself. For some, it was an interesting experiment. For others, it triggered a complete reset of everything people knew about work performance and daily lives. 

While organisations continue to plan pilots to prove value – and fail at it, according to the MIT report, over 90% of employees now use AI tools in some form, whether to draft ideas, organise notes or look for new ways to solve everyday problems. They have integrated them into their working routines without waiting for permission or policy. It’s an informal kind of progress where people find what works and carry on using it. As a result, there is a growing gap between how people work and how their employers think they work. 

Soon, that divide could widen even further if organisations don’t take this challenge seriously. 

The AI generation is coming 

Change is afoot. The UK government, for example, plans to train 7.5 million people in AI skills by 2030, embedding digital literacy from primary school to postgraduate study. Through £187M initiatives such as TechFirst, students will learn to use AI as naturally as previous generations learned smart calculators or internet browsers. 

Many of these future employees will arrive with years of experience using AI tools informally – some through coursework and others through personal use. By the time they enter the workforce, this fluency will be second nature. For employers, AI capability will no longer be a specialist skill. It will be part of how work gets done, just as email or search engines once were. We are witnessing the surge of AI-natives – a natural evolution of internet-natives. 

Learning by doing

For learning and development teams, this change is already visible. The traditional model of courses, modules and workshops is giving way to something more practical. AI allows people to learn through practice by simulating conversations, applying knowledge to realistic situations and receiving instant feedback. 

For instance, employees can engage with AI-driven simulations that take the role of a client, a patient or a colleague. The system measures how well they follow a defined process, assesses tone and accuracy and provides immediate feedback. It’s a simple idea, but imagine thousands of employees practising skills in parallel, at scale, with the kind of reflection and coaching that used to be available only face-to-face. 

These technologies make training more relevant. They offer people a safe space to experiment, to make mistakes and to learn by doing. When that feedback becomes part of daily work, learning turns from a formal exercise into a continuous process. 

The gap between use and structure 

Despite this progress, most organisations are still at the surface level of adoption. Many employees use free or personal accounts to access AI tools, often without understanding where their data goes or how their outputs might be stored. Leaders are aware of the practice but hesitant to formalise it without clear governance. 

The hesitation leaves value on the table. Individuals may become more efficient, but without shared guidance, the benefits are not seen straight away. Moreover, teams miss the chance to learn from each other’s experience, and companies lose visibility into how AI is actually being used across their business. 

Business leaders can implement a structured approach by helping every employee understand what tools are available, how to use them safely and when to avoid them. Next comes the team level, identifying which tools genuinely improve their work, whether in marketing, operations or customer success, and agreeing on standards for responsible use. 

Adapting to the pace of change 

The hardest part for most organisations isn’t the technology itself but the speed at which it evolves, and AI has been the biggest digital change so far. A model that feels essential today may be redundant within months, with larger providers constantly releasing updates, changing how tools behave and what they can do. As a result, policies written once a year simply can’t keep up. 

Staying relevant will depend on continuous review and adaptation by building a culture that learns as fast as the tools themselves. If organisations treat AI governance as a living process, they will manage this change far better than those who treat it as a one-off policy exercise. It’s not a question of waiting for the technology to stabilise. It won’t. A more realistic goal is to build internal agility – the ability to evaluate, test, and refine practices quickly. 

Continuous learning as an advantage 

The education system may produce AI-literate graduates, but their learning arc doesn’t end at graduation. Employers now need to extend it, offering ongoing opportunities to learn, adapt and apply new technologies responsibly. While historically technology adoption began at the top, with AI, that pattern has reversed, with many employees wondering why organisations are not keeping up. 

While leaders don’t need to be experts in prompt engineering or data science, they do need to understand what AI can and cannot do. They need to ask informed questions, assess risks and opportunities and help their teams experiment safely. Their role is to connect the energy of grassroots adoption with organisational purpose and to turn informal innovation into collective progress. 

This could be embedding learning into daily routines: short, relevant training programmes that are on-demand and directly connected to work. When organisations make learning habitual, they develop the resilience to keep up with constant change. 

The key is combining different kinds of expertise. In many workplaces, there are two groups: those fluent in AI tools and those with deep organisational knowledge. Both perspectives matter. The challenge is ensuring they can work together 

The next generation of employees will bring an entirely different relationship with technology. The expectations will be much higher than ever, with instant feedback, AI tools seamlessly integrated into all parts of a workplace and leadership that understands these tools. 

Whether that becomes an advantage or a source of tension depends on how organisations prepare now. So far, the MIT report clearly shows the gap is far from being bridged – and it’s something for leaders to take an urgent look at.