England’s new policy on AI tools in education has highlighted gaps that most organizations quietly ignore: the importance of mastering AI, critical thinking and careful analysis of its results.
Despite rapid advances in AI, training resources on appropriate, safe, and effective use of AI are still extremely limited.
As an example, although rapid engineering plays a crucial role in enabling modern AI users, there is still a lack of education around this essential skill.
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Senior Vice President of Data and AI at NinjaOne.
Without a good understanding of the fundamentals and blindly relying on AI capabilities alone, users can produce work – or results that work but lack judgment, nuance and quality. Ultimately, this puts their organization, their customers, or even their own credibility at risk.
In order to fully realize the benefits of AI in 2026, users must better discern where and how to apply it correctly – combining thoughtful use of the technology with regular training and enablement to achieve optimal results that do not compromise reputation or security.
Defeating common myths about AI
In the organizations I have worked with, two myths about AI continue to arise.
The first: use AI everywhere. Find ways to implement it at scale, invest in it deeply, and let the results speak for themselves.
AI is like a slow burn: it requires targeted investment and conservation to maintain progress. If you let it burn too intensely, it could destroy entire operations. We are not yet at the point where we can fully trust autonomous AI with our most sensitive business documents and decision-making.
The best use cases always keep a human in the loop, who can detect when the AI isn’t doing things quite right. In IT, where availability 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 type of edge cases that absolutely require human intervention.
Second myth: AI is a universal solution. AI is far from a single use case. Its application is incredibly wide and varied. For organizations to get the most out of AI, start with clear goals and small use cases. From there, train teams, add guardrails and validation processes, and collect feedback. Then repeat.
AI is not just a solution, it is a skill. It requires hands-on practice, strengthening, and a willingness to adapt in order to generate maximum victories. Organizations that build true adoption will treat AI skills like any other job. They will create space to experiment, learn from others, and improve through real work.
The upcoming split
LinkedIn data shows that AI literacy has increased 177% since 2023. But even though people are using AI everywhere, education and understanding have not kept pace.
Over the next five years, we will see a clearer divide 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 check-box exercise that everyone has to do.
The other group will develop real capabilities, from contextual engineering of prompts to outcome validation frameworks and responsible use protocols that align with their goals. This gap will not only show up in efficiency measures.
This will manifest itself in quality, trust, customer experience and the performance of entire teams. This will be evident in more advanced AI use cases and accelerated decision-making.
Large organizations have already embraced this change and are working to stay ahead of the enablement curve. Walmart launches AI upskilling programs with OpenAI. Accenture has publicly confirmed that it could fire any employee who cannot improve their skills.
AI education has moved from an optional initiative to basic workforce planning.
What this moment really means
Mastery of AI is the bare minimum. Organizations that value rapid engineering, critical thinking about outcomes, and intelligent collaboration with AI tools will attract both top talent and partners in the future.
Keeping up with current dynamics requires a flexible and pragmatic approach, as organizations will need to combine thoughtful adoption with regular training. The next generation will not treat AI as something new or intimidating.
They will treat it like Wi-Fi: expected, invisible and essential. They will enter the job market ready. The real question is whether the job market they will enter will be ready to welcome them.
Now is the time to close the gap, not because AI is exciting, but because the organizations that thrive will be those that find creative ways to use AI to augment existing human capabilities – increasing productivity, streamlining processes, and allowing individuals to grow in their roles (and therefore grow the business accordingly).
The AI gap that no one talks about (or people don’t talk about enough) remains enablement – and ultimately, it’s not due to faulty technology, but rather a lack of investment in people.
Organizations that act now, investing in both AI and the tools to fuel progress, will set the standard that all others will eventually follow.
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