In England, recent policy around artificial intelligence in education highlights that many organisations aren’t prioritizing AI literacy and analysis of AI-driven outputs.
Although AI tools continue to advance at pace, training programmes around effective and secure use of AI are still few and far between. Take prompt engineering as an example. Well-written prompts are crucial for getting the most out of AI, but many employees lack the training and resources to prompt effectively.
This is one reason why we’re seeing the proliferation of AI ‘workslop’ – high-volume, low-quality outputs that lack judgement, nuance, and fail to deliver any meaningful value for organisations. At best, ‘workslop’ creates more problems than it solves for employees. At worst, it can place the organisation’s customers and its reputation at risk.
To realise the full benefits of AI, employees need to be discerning about where and how to apply it. A considered approach should be supported by regular education and enablement. With all this in place, organisations can improve efficiency and speed with AI, without sacrificing their reputation or security.
Reality check: the two most common AI myths
Across the organisations I’ve worked with, there are two AI myths that continue to show up.
The first: Use AI everywhere. Find ways to implement it widely, invest in it deeply, and let the returns speak for themselves.
AI is like a slow burn – it takes targeted investment and curation to keep progress alight. If you let it burn too brightly, it could destroy entire operations. We’re not yet at a place where we can fully trust autonomous AI with our most sensitive business materials and decision making. The best use cases always keep a human in the loop, who can spot when AI doesn’t quite get it right. In IT, where uptime and security are mission-critical, we rely on experienced professionals to make judgment calls. With the right training, they can safely automate repetitive tasks and free up more time for the kind of edge cases that absolutely require human intervention.
Myth two: AI is one size fits all. AI is far from a singular use case. Its application is incredibly broad and varied. For organisations to make the most of AI, start with clear objectives and small use cases. From there, train teams, add guardrails and validation processes, and collect feedback. Then iterate.
AI isn’t just a solution, it’s a skill. It requires hands-on practice, reinforcement, and a willingness to adapt in order to drive maximum wins. The organisations that build genuine adoption will treat AI skills like any other craft. They’ll create a space to experiment, learn from others, and improve through real work.
AI education isn’t keeping pace with AI deployment
LinkedIn data shows AI literacy has surged by 177% since 2023. But even with people using AI everywhere, education and understanding haven’t kept pace.
Over the next five years, we will see a clearer divide emerge in how business leaders approach AI education and enablement. One group will treat AI skills like Microsoft Office in the 90s, keeping it as a checkbox exercise that everyone must do.
The other group will develop real capability, from contextual prompt engineering to output validation frameworks and responsible-use protocols that match their goals. This divide won’t just show up in efficiency metrics. It will show up in quality, trust, customer experience, and the performance of entire teams. It will be evident in more advanced AI use cases, and expedited decision-making.
Major organisations are already embracing this shift and racing to stay ahead of the enablement curve. Walmart is launching AI upskilling programmes with OpenAI. Accenture has publicly confirmed that it may exit any employees who cannot be upskilled. AI education has evolved from an optional initiative into core workforce planning.
The future of AI depends on enablement
AI literacy is now a baseline expectation. Organisations that focus on developing skills around prompt engineering and critical thinking around outputs will attract and retain top talent and partners in years to come.
Capitalising on the current momentum requires a flexible and practical approach, with both thoughtful adoption and regular education. The next generation of talent won’t be daunted by AI. They will enter the workplace ready. The question is: will the workforce they enter be equipped for them?
It’s time to close the AI education gap. Organisations that thrive in future will be those who invest in their employees with AI training and enablement. They’ll see greater ROI on their AI investments as a result, and their employees will be more engaged and motivated.
