AI is quietly rewriting the deal between employers and employees, and L&D sits right at the centre of it. From mindset shifts to self-directed learning, what does this mean in practice? Yomi Tejumola explores how AI is reshaping responsibility, capability and trust at work, and what L&D should do next
British businesses spent £53bn on training in 2024. At face value this sounds like a substantial figure, until you realise it’s down nearly one-fifth from 2011 according to government data. Per employee, the decline is even starker. At £1,700 per head, this is a 29.5% drop over the same period, with companies slowly backing away from investing in learning and being locked into a ‘what if we train them and they leave’ spiral.
“The companies that thrive may not necessarily have the most automation, but they will have the most imagination and willingness to empower their people”
It seems that learning is being viewed less as a growth lever and more as an expense to minimise. Too many organisations operate from fear – of losing people they’ve invested in, wasting resources and seeing their employees take their new skills elsewhere.
This needs to be flipped on its head. Instead, we should be asking: what if we don’t train them, and they stay?
The most effective organisations take a pragmatic view of talent. They understand that people won’t stay forever, nor should that be the goal. In fact, when someone grows beyond their role, it’s usually a sign that the environment is working. And when they move on, the skills, mindset, standards and network they developed travel with them into their next chapter. In many cases, they become some of the organisation’s strongest advocates, often returning as partners, collaborators or sometimes even clients because they’veexperienced the culture first-hand and trust the quality of work.
This is the return a lot of companies are missing out on. Learning and development yields far beyond employee retention – if approached in the right way, it can set off long-term network effects, drive innovation and build long-term brand equity.
The capability-mindset connection
AI is adding another dynamic. The news that tech consultancy Accenture plans to lay off employees who cannot be retrained for AI-assisted roles underlines the fact that roles are changing faster than ever.
However, retraining is as much about mindset as it is capability. Most people can learn new skills –we’ve all done it before, formally or informally. What holds them back is belief; thinking along the lines of ‘this isn’t relevant to me’, ‘I’m not technical enough’, or ‘I don’t have time’. These ideas harden into resistance and, once they do, curiosity fades.
Creating psychological safety is crucial. People need to see learning modelled with peers experimenting, mistakes being treated as progress, and tangible wins being celebrated across the organisation. Then comes capability and the tools, space and time to build skills.
And capability can spark belief just as much as belief sparks capability. For those drowning in work, we start with efficiency by showing how AI removes repetitive tasks. Once someone automates something or co-writes with AI, there’s a shift, and that small win creates capacity, which in turn opens the mind. When mindset and capability grow together, transformation sticks.
Employees taking responsibility
At the same time, the onus is shifting between employer and employee. Rather than accept that the company owns development, it is down to employees to take the initiative and seize ownership of their learning and progression themselves, with the company acting as an enabler. The days of waiting for managers to send links to training courses are behind us.
Self-direction looks like curiosity in motion. We see people using AI both for work productivity and in their personal lives: creating bedtime stories with their kids as characters, managing information overload, sending love letters to spouses, building mini apps for daily workflow problems. When learning feels personal and relevant, professional experimentation follows.
The fastest learners treat AI as a sandbox – dabble, break things, see what works and what doesn’t. Futureproofing is about growing confidence in your ability to learn and unlearn quickly. Doing it collectively is also important. We mix people from HR, marketing, data science and finance intentionally because everyone brings different perspectives, and those nuances shape how we all see and use AI.
Management’s role: Facilitate, don’t dictate
Employers need to create conditions that make self-directed learning possible, but you can’t force people to do it. Specifically, management’s role falls into five areas:
- Facilitate: carve out time and space for exploration – you can’t value learning while filling every hour with delivery.
- Encourage: make AI experimentation central, not optional.
- Model: use AI yourself, share your learning curve and missteps. When leadership learns alongside junior staff, it signals a shared journey, not a hierarchy.
- Equip: provide tools, access, support networks to your people.
- Protect progression over perfection: celebrate wins loudly, share failures openly. Both prove learning is happening.
AI adoption is an evolution. The most successful people right now take ownership, experiment, learn in public and build their own playbooks. They aren’t waiting for their employer to give them the time, tools and tasks required to embrace this technology and use it as a platform to further their careers.
In the short term, this shift will widen skills gaps, which is inevitable in any disruption. But in the longer term, AI will act as a leveller by breaking geographic barriers, democratising knowledge access and personalising learning.
The companies that thrive may not necessarily have the most automation, but they will have the most imagination and willingness to empower their people, whether they decide to stay or not.
Yomi Tejumola is the Founder and CEO of Algomarketing
