AI Is Forcing a Reset of Who Drives Workplace Development

By Yomi Tejumola,  Founder and CEO at  Algomarketing.

With employers cutting training budgets while simultaneously demanding AI-ready skills, Yomi Tejumola, founder and CEO of Algomarketing, argues that the balance of power is shifting - and employees will increasingly need to drive their own learning to stay ahead.

Late September this year, Accenture CEO Julie Sweet outlined plans to cut staff who are unable to reskill on AI - a stance shaped by the firm seeing 7% revenue growth driven by rising client demand for AI skills. “Every CEO, board and the C-suite recognise that advanced AI is critical to the future,” she told CNBC; a stark reminder that in the AI age, if you’re not learning, you’re at risk of being left behind. 

While Accenture plans to invest heavily in upskilling its “reinventors” as part of this strategy, many UK businesses seem to be moving in the opposite direction. In fact, if we look at recent government figures, employee training spend has been steadily declining for more than a decade - the total invested in all training by organisations was £53 billion last year, an 18.5% decrease from 2011. This was equivalent to £1,700 per employee, an even sharper decline of 29.5% over the same timeframe. 

It all points to a country firmly in the grips of a “what if we train them and they leave” employer mindset crisis, where somewhere along the line, learning shifted from a growth lever to a cost centre. And it’s driven by fear - fear of losing people they’ve invested in and fear of sunk cost.

Together, these two dynamics - employers pulling back on training and organisations demanding AI-ready skills - lead to one conclusion: the balance of responsibility between employers and employees is shifting when it comes to L&D.

How to thrive as learning responsibility resets

In order to successfully address this shift in balance, it will take effort from both the employee and employer. Workers will need to take learning and career progression into their own hands; experiment with AI, demonstrate creativity, essentially build their own playbooks and future-proof themselves. Management teams, meanwhile, must respond by encouraging AI adoption and replacing fears of losing people or money on L&D with a new mindset: “What if we don’t train them, and they stay?”

AI is forcing a reset from what used to be “the company owns development” to “the employee owns development, with the company enabling it” and here’s how to make it work from both perspectives:

Advice for employees: 

• Dabble and experiment: The fastest learners are the ones who treat AI as a sandbox, not a syllabus or a box to tick. Try things. Break things. See what the tech can do and what it can’t. The best thing anyone can do right now is play. Start small, get things wrong, share what you learn. Over time, people shift from using AI as an assistant to seeing it as an amplifier, freeing up time, expanding creativity, and opening new possibilities. Future-proofing isn’t about mastering every new tool; it’s about building confidence in your ability to learn and unlearn quickly.
• Strengthen your learning mindset: The conversation about retraining isn’t purely about capability, it’s also about mindset. Most people are capable of learning new skills, but what often holds them back is a lack of belief that they can. Thoughts like “this isn’t relevant to me”, “I’m not technical enough” or “I don’t have the time” will all too often turn into resistance. Once that mindset is set in stone, curiosity fades and capability never gets a chance to grow.
• Be more self-directed: Employees must recognise that their growth is exactly that - their growth. It’s theirs to own and the days of waiting for your manager to send a training link are gone. Being self-directed looks like curiosity in motion: trying a new AI tool, sharing your learnings and applying it to your own world. People are already using AI to simplify or add colour to their personal lives. That might be creating bedtime stories with their kids as central characters, staying on top of school newsletters, managing information overload or building mini apps to fix daily workflow kinks. But the point is this - when learning feels relevant and personal, experimentation follows naturally.
• Don’t do it alone: I’m very much of the opinion that you go further when you go together and that experimenting in community makes the journey more colourful. Everyone brings their own lens, their own way of thinking, solving, creating, and it’s those nuances that shape how we all see and use AI.

Advice for employers:

• Facilitate: Create the conditions for curiosity - carve out time and space for people to explore, learn and play. You can’t claim to value learning while filling every hour with delivery. With this in mind, provide both structured and unstructured opportunities to experiment, giving employees room to learn in ways that suit them. It’s also a great idea to intentionally mix teams across functions -  HR and people, marketing, data science, finance, client services, field marketers etc. - to stretch perspectives and expand how AI is applied. 
• Encourage: Set the direction and the moonshot. Make it clear that AI experimentation isn’t a side project, it’s part of how the organisation grows. Start with practical wins: show how AI can save time or simplify tasks to spark confidence. Then look to celebrate successes and highlight the benefits of AI in both professional and personal contexts to keep momentum.
• Model: Don’t just talk about AI; use it. Share your own learning curve, experiments, and missteps. When leaders learn alongside interns and analysts, it shows that this isn’t a hierarchy of capability, it’s a shared journey. Learning in public helps build psychological safety and encourages employees to experiment themselves.
• Equip: Give people the tools, access, and support networks they need: AI Ambassadors, workshops, certifications, and open learning spaces. Offer multiple on-ramps for learning: formal training, peer mentoring, buddy systems, and rewards for experimentation. All of this will help support employees in building both capability and confidence to drive their own growth.
• Protect: Focus on progression, not perfection. Every “AI win” is celebrated, and every “AI fail” is shared just as openly. Retraining isn’t just skill-building; it’s belief-building. Capability can spark belief just as much as belief sparks capability. When mindset and capability grow together, that’s when transformation actually sticks. As an example, if your employee is overwhelmed by workload, start with the efficiency lever, showing how AI can save time or remove repetitive tasks. Once someone automates a task or co-writes an idea with AI, there’s a mental unlock - “oh, I can do this”. That’s the moment the mindset shifts. That first small win opens up capacity, and capacity opens the mind. 

Balancing change, choice, and human potential

While the principles of facilitating, encouraging, modelling, equipping and protecting learning are all solid, it’s important to add that employers can’t drag employees into the future - they have to want to go. I understand the pragmatism here; roles are changing faster than ever, and not everyone was hired in an AI-native world. The goal, however, should be to take as many people along for the journey as possible. And remember, the true return on investment in L&D isn’t just retention - it’s the long-term value created through skills, innovation and stronger networks.

Nonetheless, after all these efforts, if someone remains resistant, organisations - like we’ve seen with Accenture - will need to make hard calls. For me, this is completely warranted, as long as these decisions aren’t used to disguise bias or exclusion.

In the short-term, this shift and way of thinking from companies may well widen the skills gap. That’s inevitable in any wave of disruption. But in the long-term, AI will act as a great leveller. It’s already breaking down geographic barriers, democratising access to knowledge, making learning more personalised, and skills more transferable. For me, I see AI as an assistant for creativity and human potential. The companies that thrive won’t be the ones with the most automation, they’ll be the ones with the most imagination.

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