Experienced developers get the most out of AI
At Blis, we've seen that it's our most experienced engineers who get the most out of AI. They don't use it to outsource work, but to accelerate their thought process. Personally, I mainly use AI as a sparring partner, as a second opinion, or just to quickly generate a test scenario that I already had in mind.
I'm not using AI to get better — I'm using it to do what I'm already good at faster.
At the same time, you have to be careful that experience does not become a pitfall. Those who are too stuck in old patterns may see AI as a gimmick. An 'add-on' or, even worse, as something “the juniors can pick out”. On the other hand, it takes seniority for AI to work properly. After all, the real bottleneck with AI is not technology, but the judgment and context knowledge of the person who uses it.
Growth mindset
That's why mindset is at the heart of our AI training courses. We encourage a growth mindset: the belief that you can continuously develop yourself through practice. Anyone who thinks that AI is taking over their job or making it obsolete is frustrated. Anyone who thinks “I can learn something new here” is growing faster than ever.
That attitude makes the difference between teams that try AI once and teams that transform with it. Working AI-first requires curiosity, courage and the will to do things differently than you are used to. This is also reflected in how we train people at Blis: you can't stick to Level 1 or 2. If you want to keep growing, you have to learn to let go, dare to delegate to AI and stay critical of the output.
Our training courses are also designed for this. We teach teams not only to use tools, but also to think in AI workflows. For example, let an AI write a solution approach before you ask for code. Let the AI write tests first, check that they fail, and only then build the function. This is how you control the AI, instead of being guided by the AI.
Not a search engine but an intern
Many people use AI as a search engine. Like it's Google. But that is not the optimal use of AI. For me, it feels more like an intern: it can do a lot, but you have to give clear assignments, you have to control the work and sometimes you have to do it all over again. So we don't just train people on how to use tools, but especially how to make good prompts, set limits and control results.
And it goes beyond code. Our testers are now using AI to generate hundreds of tests and to automatically identify anomalies in log files. But here too: it only works with critical human supervision. It's the interplay between humans and AI that makes the difference. The AI accelerates work, people determine the direction.
How developers see themselves
This way of working also changes how developers see themselves. They become less executors and more designers of their own process. Instead of completing bundles of code, they think about what a solution should look like and how they can achieve it with the help of AI. This not only ensures better software and faster progress, but also greater job satisfaction and ownership.
We notice it in our projects: developers who work like this deliver faster, more consistently and with fewer bugs. They work test-driven, iteratively and above all: consciously. They know what to ask AI, and what they can do better themselves.
AI is not a substitute for good developers. But good developers who can work with AI are the future. And the faster you organize that, the greater your advantage.
Continue reading?
This is part 3 in a five-part series about AI-first working in software development. In the white paper 'The foundation of an AI-first company“you'll read about the framework we used to make Blis Digital AI-first and what we're currently using to make our customers AI-first.
Also read:
Blog 1: Why many software companies are missing the AI boat
Blog 2: The 4-level framework; how to promote AI readiness in your team
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