Everyone is talking about AI right now. We’re trying out new tools, learning how features work and trying to keep pace with developments. But far fewer people spend time on the foundations: understanding how we need to think and communicate to get AI to do what we actually want.
For me, real knowledge isn’t about which tool you use. It’s about how clearly you can articulate what you want to build, which problem you want to solve and what a good result looks like.
From writing code to setting direction
When I started exploring AI and automation nearly ten years ago, programming was a major advantage. Often, it was essential to making progress.
My first automated chatbot required coding, learning and a lot of patience. When I built a solution with a limited scope using Alexa to automate functions in a hotel room and answer specific questions about the local area, I had to program how it should behave and use the knowledge I gave it. To bring my ideas to life, I needed to be able to translate them into code.
Things are different today.
Understanding programming is still an advantage, but you don’t need to be able to write code yourself to start building with AI. AI can do an increasingly large share of the coding. Your job then becomes describing what needs to be built, setting the parameters and assessing whether the result works. More important is knowing and defining what data you’re working with, or need.
The new programming language is, as I see it, clarity and precision, with the goal in sight.
Those who can articulate their goals, explain the context and give precise instructions will have a major advantage when working with AI. It’s also about being able to ask follow-up questions, spot misunderstandings and adjust the direction along the way. Not so different from being a leader, is it?
An ABC for building with AI
I think of it as an ABC.
A stands for aim: What do you want to achieve, and why? What output do you need?
B stands for brief: What should the solution do, for whom and within what parameters?
C stands for control: How do you determine whether the result works and meets your needs?
You don’t need a master’s degree in computer science to get started. Nor do you need to spend years learning programming before you can bring your first idea to life.
I firmly believe that three weeks of focused practice in being clear can take you a long way. Three weeks spent practising how to articulate clear requests, break down problems, test solutions and understand how AI responds to your instructions. That won’t make you an expert in everything, but it can give you a strong foundation to build on.
I also believe the programmer’s role will change fundamentally. The more coding AI can do, the more important it becomes to ask what human expertise is needed around it. An understanding of logic, systems, dependencies and quality will still be crucial. Someone has to be able to provide the right guidelines and take responsibility for what gets built.
Build experience as the technology develops
AI is developing rapidly, and the shifts are significant. If you wait for development to pause before you start learning, you could be waiting a long time while others build experience. And don’t be afraid when the systems change. In the past two weeks alone, Gemini has changed fundamentally, Claude has gained new features and ChatGPT has become more capable. Things move quickly, and you need to keep up.
I’m convinced that people who start early and turn their curiosity into practical knowledge have a lot to gain. Every attempt is a chance to understand more: what works, what needs improving and where AI can genuinely benefit your organisation. Start by solving everyday problems, bringing some structure to something or trying to bring that app idea you’ve had for ages to life. Dare to ask your AI assistant, and take it from there.
That’s why you need to take responsibility for your digital maturity. By actively developing your knowledge and capabilities, you can create a digital advantage that delivers value for you, your business and the people around you. It starts with curiosity.
AI can be used for so much more than generating text, summarising analyses and creating images. It can help you build services, connect workflows, automate recurring tasks and test ideas that previously required far greater resources.
With clear goals and people who can turn possibilities into practice, AI can change the playing field. But the opportunity to build more doesn’t make direction and responsibility any less important. Quite the opposite. For me, that’s why AI isn’t a tech issue – it’s a leadership issue.
And it starts with being able to explain what you want to achieve. Create clarity through leadership. Be human, think data.