England’s new policy on AI tools in education has spotlighted gaps that most organizations are quietly ignoring: the importance of AI literacy, critical thinking and careful analysis of its results.
Despite AI’s rapid advancements, training resources on proper, secure, and efficient AI use are still extremely limited.
As an example, while prompt engineering plays a crucial role in enabling modern AI users, there’s still a lack of education around this critical skill.
Article continues belowSVP of Data and AI at NinjaOne.
Without a strong understanding of the fundamentals, and with a blind reliance on AI capabilities alone, users may churn out workslop – or outputs that function but lack judgment, nuance, and quality. Ultimately, it is putting their organization, customers, or even their own credibility in jeopardy.
In order to fully reap the benefits of AI in 2026, users must get better at discerning where and how to properly apply it – pairing thoughtful use of the technology with regular education and enablement to achieve optimal outcomes that don’t compromise reputation or security.
Beating common AI myths
Across the organizations 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 organizations 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 organizations 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.
The coming split
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 organizations are already embracing this shift and racing to stay ahead of the enablement curve. Walmart is launching AI upskilling programs 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.
What this moment really means
AI literacy is the bare minimum. Organizations that place value on prompt engineering, critical thinking about outputs, and intelligent collaboration with AI tools will attract both top talent and partners in the future.
Acting on the current momentum requires a flexible and pragmatic approach, as organizations will need to pair thoughtful adoption with regular education. 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 walk into the workforce ready. The real question is whether the workforce they enter will be ready for them.
This is the time to close the gap, not because AI is exciting, but because the organizations that thrive will be the ones that find creative ways to use AI to augment existing human capabilities – boosting productivity, streamlining processes, and allowing people to grow in their roles (thus growing business as a result).
The AI gap that no one’s talking about (or people aren’t talking about enough) remains enablement – and at the end of that day, that’s not due to faulty technology, that comes down to a lack of investment in people.
The organizations that act now, investing in both AI and enablement to fuel progress, will set the standard everyone else will eventually follow.
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