AI Training Expert Reveals Key Lessons for Mastering Artificial Intelligence
Training teams to use artificial intelligence at work has provided me with a front-row seat to a new kind of professional divide. Some individuals hand everything over to the machine and stop thinking entirely, while others refuse to touch it at all. However, there is a third group that learns to work with AI critically, treating it like a bright, enthusiastic intern that requires management and support to perform optimally.
The Critical Factor: Curiosity Over Technical Ability
The difference between these groups is rarely technical ability; it is curiosity. A willingness to experiment, make mistakes, and figure out what AI is genuinely good at drives success. Most people fail with AI because they do not understand what it actually is. The individuals I have worked with tend to swing between extremes: treating AI as an all-knowing oracle or dismissing it entirely after one error.
Current AI has as much in common with the human brain as a bird has with an A380 aircraft. Both can fly, but that is where the similarity ends. Large Language Models simply predict words based on patterns in their training data. This is why they can produce fluent prose about well-covered topics but will confidently fabricate information when on unfamiliar ground. Once users grasp this, their approach shifts to providing clear goals and proper context. When someone tells me everything they get from AI is rubbish, it almost always turns out they are receiving generic answers to generic prompts.
Treating AI as a Skill, Not a Shortcut
The biggest predictor of success is not technical ability but whether someone treats AI as a skill to be learned rather than a magic box that either works or does not. The people best at using it are those who experiment daily and reflect on how to achieve better results next time. The goal is to get the machines to work for us, not to think for us, which means using AI in a proactive, critical, and engaged manner.
AI needs direction, feedback, and correction, just like people do. The skills required to use AI are ones many people already possess: communication and delegation. Similar to managing an intern, you would not hand them a project and disappear. You would break it down, check in regularly, and course-correct as needed. The same principle applies to AI. And just like with an intern, as their manager, you are ultimately responsible for what they produce. That is what 'human in the loop' truly means: it is your job to keep the AI on track and ensure the output meets standards.
Avoiding Pitfalls: Judgment and Data Security
You should not outsource your judgment to AI or provide it with sensitive data. A few months ago, a manager at a small retail chain proudly showed me an HR dashboard he had coded using AI. Unfortunately, he had imported sensitive information without considering the risks of data leaks or necessary policies. I sent him straight to IT.
The risks extend beyond security. AI systems are trained on data created by humans and reflect our collective biases. You should avoid asking AI to make high-level subjective judgment calls, such as whether to put a candidate through to an interview, which could be prone to bias. Instead, focus on factual evaluations, like determining if a candidate has the right number of years of experience.
The Inevitable Impact of AI
Ignoring AI will not stop its impact. The environmental, ethical, and social effects of AI are significant and growing. In a recent session for an environmental charity, one director was torn between the ability to do more as an organization and the moral costs, such as the carbon impact of running AI systems. However, AI is not going away. It is far better to have AI-literate citizens who can demand that it is built in a responsible and democratic way. AI is not a train waiting for us to board; it is already mid-journey. The only question is who gets to steer.
The Rapid Pace of AI Evolution
The pace of AI's evolution leaves no room for slow decisions. Today's version of AI is the worst it will ever be, and it is 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 debug code, now I create entire applications in hours with just a few prompts. Many developers laughed last year when Anthropic's CEO predicted that 90% of code would soon be written by AI. Today, many admit he was not far off.
Unlike past technological revolutions, 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's induction to become Edison's power plant. Today, the gap between breakthroughs and global adoption is a few months. We do not have the luxury of a decade-long debate; we must build our social and democratic response as fast as technology advances, or risk being governed by tools we do not yet understand.
The people who will shape how AI changes the world do not have to be the technologists who build these systems. They can be those willing to experiment and 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.
