Alexia Pedersen: Upskilling the UK workforce for the AI revolution

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As the UK government rolls out its ambitious AI Opportunity Action Plan to enable greater implementation of new technologies to boost economic growth, it faces a critical challenge: ensuring every industry is prepared for this seismic shift. 

Interestingly, both UK employers and employees are aligned on the importance of education to bridge the gap between implementation and innovation.

According to our 2025 Technology Trends Report – with insights into the most popular technology topics consumed by our 2.8 million users – demand for AI-related learning materials surged in 2024, particularly courses relating to prompt engineering courses (+456%) and GenAI (+289%). There was also a notable increase in demand for AI-related skills like deep learning, natural language processing (NLP), and model monitoring. Despite this, many employees lack the expertise and skills to engage with complex AI systems, let alone innovate with them.

Organisations must lead by example. A company that actively integrates AI into its operations while upskilling employees will demonstrate its commitment to innovation and growth. This year, continuous learning is no longer optional; it’s a strategic imperative. Let’s look at how business leaders can build an effective continuous learning strategy to enable this transition.

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Continuous learning: a strategic imperative for AI readiness

Employers have a responsibility and pivotal role to play in preparing their workforce for disruptive technologies. Investment in continuous learning and development (L&D) will unlock the full potential of AI technologies while fostering loyalty and retention.

Our latest report identified practical training as a critical factor in workforce readiness. AI-related tasks, like model monitoring and data preprocessing, for example, require hands-on expertise that transcends theoretical knowledge. Take AI model monitoring – ensuring AI systems operate efficiently and ethically involves identifying biases, fine-tuning performance, and safeguarding against adversarial attacks. These tasks demand a practical understanding that traditional academic programmes can fail to deliver. Training centred around real-world applications and simulations, however, would enable employees to experiment, troubleshoot, and gain confidence in applying AI technologies.

Organisations should also look to integrate training directly into workflows. Platforms that allow staff to practice tasks, from building predictive models to automating routine processes in live environments, will ensure that training is immediately relevant. In turn, this will make it easier for both the employer and employee to see the value of their learning efforts. So, how can employers do so without disrupting workflow?

Harnessing learning, in the flow of work

Continuous learning involves more than just the occasional workshop or certification; it requires fostering a culture of curiosity and adaptability – empowering employees to take on new challenges, seek opportunities for growth and share their knowledge with others.

Yet, many employees simply do not have a large chunk of time readily available to devote to learning around their day-to-day responsibilities. With an increasing number of employees wanting the opportunity and tools to learn as they work, organisations must provide staff with a variety of learning modes.

To enable this, employers can harness ‘in the flow of work’ learning opportunities. This is a phrase coined by Josh Bersin to describe a paradigm in which employees learn something new, quickly apply it and return to their work in progress. It is different from traditional learning approaches like attending a seminar or conference. These traditional learning formats work well for a beginner or when learning something new, however many professionals know a lot already and prefer to dip in and find a quick answer to a question. This is a more impactful way to learn and takes significantly less time.

Instead, ‘in the flow of work’ learning provides employees with tools to quickly find contextually relevant answers to their questions at a time that suits their schedule. This simple concept is the next step in continuous learning – something that many businesses claim to value but few can demonstrate.

For best results, companies can offer ‘in the flow of work’ learning opportunities via an L&D partner, enabling staff across all levels and departments to access high-quality materials that are tailored to their unique learning style and objectives. Likewise, employees should prioritise their own L&D to make themselves an invaluable asset to their organisation during this period of technological disruption and economic uncertainty.

Bridging innovation and implementation

While the government’s AI Opportunity Action Plan offers a bold vision for the UK’s future, its success ultimately hinges on a workforce equipped to bring this vision to life. Above all, these ambitious plans underscore the importance of aligning workforce capabilities with technological advancements.

As the UK positions itself as a global leader in AI, investment in continuous learning will be crucial to drive growth, foster talent, and secure a future where technology and the workforce thrive simultaneously.

At the same time, empowering employees to access and apply contextually relevant resources and knowledge at their point of need – otherwise known as in the flow of work learning – will be critical to minimise disruption to workflow. Coupled with high-quality materials from a trusted L&D partner, this approach will equip employees to bridge the gap between innovation and implementation.

SVP International at 

Alexia leads the international team at O’Reilly and is responsible for creating and delivering value to our customers. With over 20 years’ experience in the technology sector, Alexia has a passion and interest in learning for the enterprise and brings extensive knowledge of working with large organisations to implement learning at scale to deliver successful outcomes.

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