Training teams to use AI at work has given me a front-row seat to a new kind of professional divide.<\/p>\n
Some people hand everything over to the machine and stop thinking. Others won\u2019t touch it at all.<\/p>\n
But there\u2019s a third group. They learn to work with AI critically, treat it like a bright, enthusiastic intern that needs to be managed and supported to do their best work.<\/p>\n
The difference? It\u2019s rarely technical ability. It\u2019s curiosity. A willingness to experiment, get things wrong, and figure out what AI is actually good at.<\/p>\n
Here\u2019s what I\u2019ve learned so far.<\/p>\n
Most people fail with AI because they don\u2019t understand what it actually is<\/p>\n
The people I\u2019ve worked with tend to swing between extremes: treating AI as an all-knowing oracle or dismissing it entirely after one mistake.<\/p>\n
Current AI has as much in common with the human brain as a bird has with an A380. Both can fly, but that\u2019s where the similarity ends. Large Language Models simply predict words based on patterns in their training data. It\u2019s why they can produce fluent prose about well-covered topics, but will confidently make things up when they\u2019re on unfamiliar ground.<\/p>\n
Once users understand this, their approach changes to providing it with clear goals and proper context. When someone tells me everything they get from AI is rubbish, it almost always turns out they\u2019re getting generic answers to generic prompts.<\/p>\n
The people who get the best results treat AI as a skill, not a shortcut<\/p>\n
The biggest predictor of success isn\u2019t technical ability. It\u2019s whether someone treats AI as a skill to be learned rather than a magic box that either works or doesn\u2019t. The people best at using it are the ones who experiment daily and reflect on how to get better results next time. The goal is to get the machines to work for us, not to think for us – that means using it in a proactive, critical and engaged way.<\/p>\n
AI needs direction, feedback and correction \u2013 just like people do<\/p>\n
The skills needed to use AI are ones many people already have: communication and delegation. Just as with that intern, you wouldn\u2019t hand them a project and disappear. You\u2019d break it down, check in regularly, and course-correct as needed. The same applies to AI.<\/p>\n
And just like with an intern, as their manager you\u2019re ultimately responsible for what they produce. That\u2019s what \u2018human in the loop\u2019 really means: it\u2019s your job to keep the AI on track and make sure the output is up to scratch.<\/p>\n
You shouldn\u2019t outsource your judgment to AI \u2013 or give it sensitive data<\/p>\n
A few months ago, a manager at a small retail chain was proudly showing me the HR dashboard he had coded using AI. Unfortunately, he had also imported sensitive information without thinking about what would happen if that data leaked or any policies he needed to follow. I sent him straight to IT.<\/p>\n
But the risks go beyond security. AI systems are trained on data created by humans and reflective of our collective biases. You should avoid asking AI to make high-level subjective judgement calls such as \u201cshould we put this candidate through to interview\u201d that could be prone to bias. Focus instead on factual evaluations, for example \u201cdoes this candidate have the right number of years of experience\u201d.<\/p>\n
Ignoring AI won\u2019t stop its impact<\/p>\n
The environmental, ethical and social impact of AI is significant and growing. In a recent session for an environmental charity, one director was torn between the ability to do more as an organisation and the moral costs of doing so, such as the carbon impact of running AI systems. But AI is not going away. It\u2019s far better to have AI-literate citizens, able to demand that it\u2019s built in a responsible and democratic way. AI is not a train waiting for us to board; it\u2019s already mid-journey. The only question is who gets to steer.<\/p>\n
The pace of AI\u2019s evolution leaves no room for slow decisions<\/p>\n
Today\u2019s version of AI is the worst it will ever be and it\u2019s improving faster than most people realize. Tasks that were impossible a year ago are now routine. Where once I spent long nights hunched over a keyboard trying to figure out why my code wouldn\u2019t run the way it was supposed to, now I create whole applications in a matter of hours with nothing more than a few prompts. Many developers laughed last year when Anthropic\u2019s CEO said 90% of code would soon be written by AI. Today many admit he wasn\u2019t far off.<\/p>\n
Unlike the technological revolutions of the past, this one is moving faster than our ability to adapt. It took a century from the steam engine to the locomotive, and fifty years for Faraday\u2019s induction to become Edison\u2019s power plant. Today, the gap between breakthroughs and global adoption is a few months. We don\u2019t have the luxury of a decade-long debate; we must build our social and democratic response as fast as technology, or risk being governed by tools we don\u2019t yet understand.<\/p>\n
The people who will shape how AI changes the world don\u2019t have to be the technologists who build these systems. They can be the ones who are willing to experiment, to take both capabilities and risks seriously. We all have a responsibility not just to understand AI ourselves, but to push our employers, communities and governments to use it in ways that ensure no one gets left behind.<\/p>\n