Prompting is often misunderstood as a technical skill, which leaves many capable, high-achieving professionals sitting on the sidelines of the AI shift because they believe they are “not technical enough” to take part.
They hear phrases like prompt engineering and assume this belongs to a different species of worker: the coder, the systems architect, the twenty-something digital native who seems to have emerged from the womb holding a laptop. That assumption is costing businesses far more than they realise, because it keeps perfectly capable leaders from using AI where it can create real value.
You do not need to be technical to use AI effectively. Research from MIT Sloan, published in January 2026, found that the best AI users were not software engineers — they were clear communicators. The skill is closer to briefing and delegation than coding: define the task, the audience, the desired outcome, and the constraints. For most senior leaders, AI prompting is a management discipline, not a technical one.
The truth is simpler and far more useful: using AI well is usually less about programming and more about communication, delegation and judgement. In other words, it looks much more like management than machine engineering.
“Prompting is not a technical party trick. For most leaders, it is simply delegation, briefing, and quality control in a new environment.”— Larysa Hale, Expert Circle
Do you need to be technical to use AI?
People tend to fall into one of two traps.
The first group thinks prompting is trivial. They treat AI like a search engine, type a quick question, and expect a polished, commercially useful answer to appear on demand. The second group goes the other way and assumes prompting is too technical, too specialised, or too advanced for anyone outside IT. Oddly enough, both groups are making the same mistake: they misunderstand what good AI use actually involves.
For most leaders, prompting is not best understood as coding. It is best understood as directing work.
Think about how a managing director briefs a capable senior associate. You do not simply say, “Sort this out,” then disappear. You explain the context, define the desired outcome, set the boundaries, outline the risks and make it clear what excellent looks like. If the work comes back confused, generic or misaligned, it is often because the briefing was weak. AI behaves much the same way.
What skills do you need to use AI effectively?
The research increasingly points in the same direction: better outcomes do not come only from better models. They also come from better user behaviour.
MIT Sloan highlighted this in January 2026, reporting that only about half of the performance gains from moving to a more advanced AI model came from the model itself. The other half came from how users adapted their prompts. The researchers noted that participants from a wide range of jobs, education levels, and age groups — including those without technical backgrounds — were able to make the most of the new model’s capabilities.
Andrew Ng, Stanford adjunct professor and founder of DeepLearning.AI, makes a similar point through his Generative AI for Everyone course, which is explicitly aimed at helping non-technical professionals understand what generative AI can and cannot do and how to apply it in day-to-day work.
The organisational barrier, meanwhile, is often not raw technical difficulty at all. Hays reports that 60% of employees feel their employers are not adequately preparing them for AI implementation, pointing to a training and support gap rather than a purely technical one. Confidence rises when people are shown how to use AI inside a structured working environment, not when they are merely told to “go experiment with it.”
“If you can lead a team, define an outcome, and give useful feedback, you already have the foundations for using AI well.”— Larysa Hale, Expert Circle
How non-technical leaders should approach AI: three shifts
The first shift is mental. Stop thinking of prompting as a technical trick and start thinking of it as professional briefing. When you sit down with AI, define the audience, the task, the commercial goal and the constraints as clearly as you would with a direct report.
The second shift is to embrace the feedback loop. The first output is rarely the final one. That is not a defect; it is how serious work usually happens. Just as you would push a team member to sharpen an argument, improve the logic or remove weak sections, you need to guide AI with the same discipline.
The third shift is to hold the professional standard yourself. AI can multiply effort, but it still needs a director. If the output is average, that is often because the input, the framing or the follow-up was average.
This matters because many businesses are still framing AI adoption as though it were primarily a technology challenge. In reality, for many firms, it is a management challenge first.
Our briefings and programme are designed for senior leaders who want to make that shift: from “I’m not technical” to “I know how to lead this properly.” Because the firms that get the most value from AI will not necessarily be the ones with the most engineers. They will be the ones with leaders who understand that good prompting is, at heart, good management.
Larysa Hale is the founder of Expert Circle and creator of the AI-Driven Marketing Growth Programme, a structured series of briefings and masterclasses for managing directors and senior leaders in professional services. She has spent over 15 years helping founders, marketing directors and business leaders build commercially grounded growth strategies.
