It's not about the technology
How does your work process look like nowadays? How do you solve implementation tasks? How do you research? How do you plan and organize your work?
These are the questions that stuck to my mind recently. Take a look at LinkedIn in general, read the posts, scroll the feed a bit. You will see the insistent idea that if you don't use AI in your daily work, in each and every task, you are hopelessly outdated. Then close the LinkedIn page, get back to your ABAP 7.40 system where inline declarations and CDS views are "the new black," and think for ages how to incorporate AI into your code :-)
Just a joke, but probably if you work with ABAP more than 5 years you will feel the pain, sorry for that :-)
And the core thing is not in the ways of applying AI, not in all those holy wars around it. The idea is strictly in that feeling that you are outdated. Do you feel it? Do you think it is new and came just with AI? Do you remember RAP tutorials, 7.5 syntax, OData, CDS flooding the social media in the same way?
AI is just an example. The amount of posts around AI is so enormous that you start thinking that everyone is already an expert and uses it everywhere, unlike you, more than you, better than you. Everyone builds "multi-agent systems" and "AI-native pipelines," whatever it means. Literally, a lot of people, with ABAP.
But what I actually see, once I look past the feed, is something different. When something doesn't work on the first try, the instinct is often not to dig further — not to read the code again, not to google it, not even to push an AI chat for a better answer. AI gets written off as unreliable, and it feels simpler to ask someone else or fall back on an approach you already know, than to sit with the problem a bit longer.
And this isn't limited to junior engineers — it shows up in fairly experienced ones too. Thankfully, it's not that many people. There are plenty who do go deeper, who keep asking why something is built this way and not another, and I wish more people picked up that habit. Either way, it takes your own judgment and analysis to make sense of it.
But what's really important in observing others' approaches to work is what conclusion you come to, what you borrow for yourself, and what you consider getting rid of.
Few points to think about:
When you have an issue in the code, you will more likely:
In what order? What do you think is a right or wrong way of working, researching, solving problems?
These are the questions just to trigger thinking process, nothing more. And they are not about the right technology, not about your relevance, not about a particular feature you know or don't. They are about your core principles and attitude, your ability to think, analyze and solve problems.
Do you see room for improvement for yourself, or are you an expert?
These are the questions that stuck to my mind recently. Take a look at LinkedIn in general, read the posts, scroll the feed a bit. You will see the insistent idea that if you don't use AI in your daily work, in each and every task, you are hopelessly outdated. Then close the LinkedIn page, get back to your ABAP 7.40 system where inline declarations and CDS views are "the new black," and think for ages how to incorporate AI into your code :-)
Just a joke, but probably if you work with ABAP more than 5 years you will feel the pain, sorry for that :-)
And the core thing is not in the ways of applying AI, not in all those holy wars around it. The idea is strictly in that feeling that you are outdated. Do you feel it? Do you think it is new and came just with AI? Do you remember RAP tutorials, 7.5 syntax, OData, CDS flooding the social media in the same way?
AI is just an example. The amount of posts around AI is so enormous that you start thinking that everyone is already an expert and uses it everywhere, unlike you, more than you, better than you. Everyone builds "multi-agent systems" and "AI-native pipelines," whatever it means. Literally, a lot of people, with ABAP.
But what I actually see, once I look past the feed, is something different. When something doesn't work on the first try, the instinct is often not to dig further — not to read the code again, not to google it, not even to push an AI chat for a better answer. AI gets written off as unreliable, and it feels simpler to ask someone else or fall back on an approach you already know, than to sit with the problem a bit longer.
And this isn't limited to junior engineers — it shows up in fairly experienced ones too. Thankfully, it's not that many people. There are plenty who do go deeper, who keep asking why something is built this way and not another, and I wish more people picked up that habit. Either way, it takes your own judgment and analysis to make sense of it.
But what's really important in observing others' approaches to work is what conclusion you come to, what you borrow for yourself, and what you consider getting rid of.
Few points to think about:
When you have an issue in the code, you will more likely:
- try to read the code, analyze it by yourself, debug
- ask a more experienced colleague what's wrong
- google the issue? ask any AI chat
- research how this should be called according to the documentation
- say it doesn't work
- search for an example of implementation in the system
- ask someone how they did it
- try to implement it in the same way other approaches work (they have to be alike, aren't they)
- research the overall idea of the approach/framework/API/technology, etc. (Google/AI/documentation)
- answer that this is not applicable and implement the well-known approach instead
- read a couple of posts about it on social media
- google it
- ask colleagues if anyone has used it and whether it's really relevant to your field
- do some research to understand the core concepts and the reason the term appeared
- read something from time to time if you find anything really interesting
- analyse the implementation implications, try to make an MVP to try it out
- add your own point
In what order? What do you think is a right or wrong way of working, researching, solving problems?
These are the questions just to trigger thinking process, nothing more. And they are not about the right technology, not about your relevance, not about a particular feature you know or don't. They are about your core principles and attitude, your ability to think, analyze and solve problems.
Do you see room for improvement for yourself, or are you an expert?
