26, August 2026

Suprise Fakude

6 minutes read

Quick Summary

AI is changing workplace roles and skills faster than traditional training approaches can always keep up with. As organisations adopt generative and agentic AI, employees need more than basic tool training. They need AI fluency, critical thinking and practical experience applying AI to their work.

For L&D teams, this means moving beyond once-off training towards continuous skills development, role-specific learning and measuring whether employees can actually apply what they learn. Organisations also need to identify emerging skills gaps early and align AI training with workforce planning.

Ultimately, successful AI adoption depends on both technology and people. Organisations that invest in developing their workforce alongside AI will be better prepared to adapt as work continues to change.

AI adoption is accelerating, but workforce capability does not automatically grow at the same pace.

An organisation can introduce a new AI tool almost overnight. Employees, however, need time to understand where it fits into their work, how to use it effectively and when its output should be questioned. As AI moves from simple productivity tools towards automation and AI agents, that learning gap becomes increasingly important.

For Learning and Development (L&D) teams, this creates a new challenge. Training can no longer only respond to the skills employees need today. It also needs to anticipate how roles, workflows and skills are likely to change next.

The question is becoming less about whether organisations should train employees on AI and more about whether they can develop their people quickly enough to keep pace with it.

The AI Skills Gap Is Already Emerging

AI adoption is no longer limited to technology teams. Marketing professionals are using generative AI to support research and content development. HR teams can use it for recruitment and workforce planning, while finance and customer service teams are finding new ways to incorporate AI into everyday processes.

As these tools spread across departments, the skills required to use them are changing too. The World Economic Forum’s Future of Jobs Report 2025 found that nearly 40% of the skills required on the job are expected to change by 2030. AI and big data are among the fastest-growing skills, but human capabilities such as analytical thinking, resilience and leadership also remain important.

This presents employers with a simple but significant challenge. New technology can be introduced relatively quickly. Developing the people who know how to use it effectively takes longer. When those two processes happen at different speeds, organisations risk investing in technology without building the capability required to get the most from it.

AI Training Cannot Be a Once-Off Event

Corporate training has traditionally followed a fairly predictable process. A skills need is identified, employees attend training and the organisation records that training as completed. AI makes that model more difficult.

An employee who learnt the basics of generative AI may understand prompting and how to use a chatbot. But workplace AI is already progressing towards systems that can automate workflows, work across applications and complete sequences of tasks. The skills employees need will therefore continue changing alongside the technology. Rather than treating AI as a course employees complete once, organisations need to think about AI capability as something that develops continuously. Employees need foundational knowledge, but they also need opportunities to experiment, apply what they learn and update their skills as their roles change. That is the difference between simply providing AI training and developing AI fluency.

Are Employees Learning AI or Just Using It?

Another challenge is that employees are not necessarily waiting for formal training before adopting AI. Many may already be experimenting with tools such as ChatGPT, Microsoft Copilot or Gemini to research information, prepare documents, analyse data or complete routine tasks.

This can create productivity gains, but knowing how to use an AI tool does not automatically mean someone knows how to use it well. Employees need to understand how to verify AI-generated information, protect sensitive information, recognise limitations and determine when human oversight is necessary. For L&D teams, the question therefore should not only be:“Have our employees received AI training?”

It should also be: “Can our employees use AI effectively and responsibly in their work?” The difference between those two questions is important because one measures training activity while the other measures capability.

What Does an AI-Ready Workforce Need to Learn?

AI literacy begins with understanding what the technology can and cannot do. Employees need to know how to communicate effectively with AI systems, evaluate the information they produce and identify appropriate opportunities to use them. But technical capability is only one part of workforce readiness.

As AI handles more routine execution, skills such as critical thinking, problem-solving, communication and professional judgement become increasingly relevant. Employees need to know when an AI-generated answer is useful, when it requires further investigation and when it should not be used at all. This becomes particularly important as organisations begin exploring agentic AI. Instead of asking an AI tool to perform one isolated task, employees may increasingly work with systems capable of completing entire sequences of activities.

The employee’s role then starts shifting from simply doing the task towards directing, evaluating and improving work completed with AI. This connects to another important question for employers: AI can automate the task, but can it make the judgement? As organisations automate more work, developing people who can evaluate AI output becomes just as important as deploying the technology itself.

L&D Needs to Move Closer to the Work

If employees are expected to use AI in their jobs, learning needs to connect with the work they actually perform. Generic AI training can create awareness, but an HR professional, marketer and finance employee will not necessarily use AI in the same way.

A marketing team might need to learn how AI can support research, content development and campaign analysis. HR professionals may need to understand how AI can assist with workforce information while maintaining appropriate human judgement. Managers may need to learn how to evaluate AI-generated recommendations and decide which decisions should remain under human control.

The technology may be similar, but the application is different. Role-specific learning allows employees to understand not only how AI works, but how it can help them solve real workplace problems. This is where L&D can move beyond delivering courses and begin supporting actual business transformation.

Final Thoughts

AI is moving quickly, but the real race is not between employees and artificial intelligence.It is between the speed of technological change and the speed at which organisations can develop their people.

Businesses can invest in AI platforms, automation and digital infrastructure, but technology alone does not create transformation. Employees still need to understand how to use those tools, question their outputs, apply them to real problems and make sound decisions.

For L&D leaders, the question is therefore becoming increasingly urgent: Is your workforce learning fast enough to keep up with AI? The organisations best prepared for the next stage of AI may not simply be those with the most advanced technology. They may be the ones whose people are capable of learning, adapting and applying that technology as quickly as it changes.

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