AI and DevOps: Tools, Challenges, and the Road Ahead – Expert Insights
June 3, 2025
Rafal Polanski: First of all, how are you today, Łukasz?
Łukasz Ciechanowski: Despite my day being quite busy, as we have a demo in the project I am currently working on, I feel quite well today. Thank you for asking.
RP: Great. I wanted to ask you about the projects and about how in the projects especially in DevOps area of the tools which are very popular now AI tools can help you and are they also able to simplify the work of a DevOps engineer in the in this kind of projects or no or are there may be a problem. What is your opinion?
ŁC: In my opinion, AI tools/support shouldn’t be a problem except the situations where some kind of client’s policies disallow them to use. Everything that may help you or may boost your work shouldn’t be treated as an issue. And if we are speaking about how AI tools are helping DevOps in regular professional life, I would say the given help is very similar to other technical areas, like development.
First of all, AI is a source of information needed to solve problems. Two or three years ago, if you had an issue to be addressed first, what you did as an engineer, you went to StackOverflow, raised the question, and tried to find similar problems from the people who had already solved them, as you don’t want to invent the wheel again. Right now, it has changed. AI is the source of the answer, so you are not going to StackOverflow anymore and just directly ask an AI assistant to serve you. Additionally, there is a market push to be involved in AI and consume its goods as much as possible, therefore, the tools providers try to introduce AI capabilities whenever they can. So basically, if you are using some DevOps tools, you are using AI capabilities just like that. Still, some of the AI features are available like experimental options, for instance, in case of GitLab CI/CD there are features which allow you to automatically generate the pipelines, and there are some like autocompletion features which are embedded and present just like that. Of course, they had existed before, but right now they are much smarter, and the suggested outcome is similar to the human being’s outcome.
RP: And if you look at challenges, and with that people report there, I have an impression that many DevOps engineers can be afraid of using AI, and not only DevOps engineers. IT specialists may be afraid of AI. Should they be afraid?
ŁC: Well, this is a difficult question…, but in my opinion, they shouldn’t. Anyway, it is something that they will not avoid. AI is everywhere now. AI can boost our work, so it can’t be ignored. If we want to be competitive in delivering solutions, we need to use it, otherwise, those that benefit from AI will be faster, will be better, and will deliver more sophisticated stuff. In my case, the main fear should be that if I will not be a forerunner, I will be behind. On the other hand, I can understand this fear … if you are hearing news every day with the message that some of the companies are firing people, giving as an explanation the AI expansion … your fear as a working person, having a family and obligations, may grow. On the other hand, let’s say honestly: as for now, AI is not replacing humans. Of course, a statement that AI is just a tool would also be hurtful, as this is a bigger topic / bigger initiative, but in the end, you are deciding what you will consume and what you will use, what you will adopt and what you will decline.
In the past, we also had some tech-trends which brought similar fear to the customers or tech-people like for instance, agile initiative or cloud expansion. When agile was introduced, I heard, so often from the clients, that with an agile approach, they would not get anything valuable, and the software house companies would just burn their money. Nowadays, most projects are realised in this way because the market/customers are educated enough to understand that the biggest problem and the biggest cost is if someone develops something for you that you don’t need. The other example is the cloud transitions. Before it massively happens, I have heard many times that this will backfire on us, and when this approach becomes established, everyone will run away from it. And I never noticed it, at least not on a bigger scale
RP: I also think that many of these fears are the result of the media or and kind of even maybe frenzy, and the approach that you advocate, meaning use the opportunity for you and the tools for you to help you be ahead of the curve, I think is very smart. So I appreciate your answer. Since the new initiatives you mentioned happened, the IT people were even busier, so this fear seems to be unjustified.
Coming back to the DevOps field. How do you see the current challenges that DevOps faces?
ŁC: I believe that more or less the challenges are similar to the past, but the main differentiator is the scale and the complexity. I think that in simple terms, we can say that right now, everything basic already exists. I know that this is not fully true, but in most cases clients want to implement more and more sophisticated things as an extension of what they already have, technically realized as a distributed services, scaling horizontally and all of that brings different scale of the problems and enforce some approaches, especially in DevOPS area like for instance CI/CD processes are right now mandatory, smart monitoring is mandatory, vulnerabilities detection on code analysis level and runtime level are mandatory because maintenance effort and stability should remain the same. Also, securing such systems is more difficult because of the software complexity, 3rd parties libraries’ massive presence, and more channels of support, like web or mobile, etc. So it means that the factor of problems has grown, and you need to have the right tools to solve them and thus an area where AI can help. Speed of producing all needed DevOps components in a faster way is important, and it has a direct influence on the delivery cost, which, as I mentioned earlier, must be incurred and must be considered already at the stage of deciding whether to start a given activity or not. So for instance if CI/CD processes and their foundation/skeleton could be intelligently generated, why not benefit from that? .. Or if using tools supporting AI in monitoring will bring important features like smart behavior analysis or smart predictive analysis, which we never had before on that level, why not to have them as security attacks are more and more intelligent? ( .. unfortunately, also thanks to AI).
If you need to implement something specific for your situation, build a unique script, why not use AI to assist you in that or even generate some needed parts/components for you, which later on you will just tweak or integrate?
Speaking further about challenges, for DevOps, and I think for the others as well, working in “distributed human environments”, where you have several development streams which at some point are connected within the project or program, the knowledge sharing is also a big challenge. In many cases, you can’t be in every team demo or spend too much time to understand where the other team’s focus is having the demo summary generated by AI or documentation created with AI help may simplify your professional tech-live a lot
RP: Thanks a lot. So, you guessed me and you answered two questions in one row because I was also willing to ask you on this how the AI tools are specifically addressing those challenges, but you in fact answered that within. So, as there are probably more than one and that there are more coming, I mean the AI tools in the DevOps area. Would you be able to tell us what your favourite one is or the one that you most often use?
ŁC: Honestly speaking, I don’t have any favourite one and I don’t use just a single tool for solving all of the problems. DevOps areas and responsibilities are very wide and depend on the type of problem you are using a wide set of tools. Nowadays, in many cases, DevOps is operating on the container level and, of course, to some level, we (DevOps) don’t care if the system is written in Java, Python, or any other language. Creating a good container is also challenging, so if you are using a good AI agent/assistant, you will not forget about any good rules that should be followed, even if you are not very experienced in this. What I had mentioned is a development AI support example, but for DevOps, AI is even more helpful in runtime. If you are using a cloud infrastructure, some of the AI capabilities are enforced by the service you selected. For instance, if you are using some kind of API gateways or load balancers from the cloud, most likely you will use AI capabilities offered with them, like traffic anomaly detection, etc.
Back to the question, which tools exactly we are using as DevOps, I will answer a little bit around :)… Since DevOps often supports developers, they’re usually also using tools dictated by the development team (e.g., GitLab, Azure), being in the majority and usually having established preferences :). This also enforces some specific AI capabilities that are a part of those tools. I also don’t want to promote any specific tool
RP: Okay, I understand that you don’t want to promote one, but I would like to maybe try a little bit more push, and I know that you work in a Microsoft environment, and you are one of the very few people who also work in an AWS environment. And so if you would name the most popular tools that you meet used by both DevOps and programmers separately for Microsoft and separately for Azure, that you have to work with?
ŁC: In the case of DevOps, I can only repeat my previous opinion that we as DevOps don’t develop so much artifacts as developers mostly doing some “dirty work” scripts which AI web assistant is usually enough, container creation rules where AI agent could be helpful but it is not mandatory if you have experience or, pipeline where repository type enforce AI capabilities etc. Maybe I will change my mind when I have more experience with Copilot4devops, AI Assistant for Azure Devops, that we started to play with, but it is too early to share any opinions
If we talk about the runtime part and Azure or AWS, as I mentioned, all of the services offered by them slowly bring some AI flavour. Some obvious AI use cases are for security enhancements and traffic analysis / bad traffic blocking exists in APi gateway, firewall, etc. And all cloud providers are tracking their competitors so they really have similar capabilities, and it is hard to say which is better or more advanced.
In case of development perspective, I can only tell a few good words about Azure Cognitive Search, known as Azure AI, which was well received by the development team and introduced a lot of cool improvements in the project we are working on, which deals with processing, categorising, and cataloging documents of various types
RP: So the environment, both on the DevOps side, but specifically the fascination with AI, creates a lot of new names, a lot of new ideas, and a lot of new hopes for how AI will evolve and help in different areas in the next couple of years. What is your view on the next five, ten years in terms of the AI role? Is it going to grow? How is it going to change?
ŁC: It will grow further as any new initiative :). All software providers, like cloud providers and tool creators, will try to find new use cases where AI could be used.
All existing obvious AI use cases for developers, like some code analysis AI capabilities, code simplification or discovering vulnerabilities capabilities, will be improved as AI will bring new models, new engines, and bigger data sets. There will be more AI in enterprise software for regular users. I see myself that, for instance, LinkedIn is continuously introducing more and more sophisticated search engines offering better profile matching based on more sophisticated criteria, and I’m sure AI has a role here. It’s hard to predict our role in that game, but I still believe that AI will not fully replace us, and engineers still will be present in the management part, in the integration part, and in the verification part.
RP: Okay. And last but not least, I mean, you have invested quite some time in your skill development to become an expert, and you are our CTO. You are also helping younger colleagues develop themselves, and very often you share your knowledge with our customers. If you had a young person in the DevOps area and starting to use the AI tools, what expert advice would you give them? Where should they go to learn? What should they avoid? In other words, what are to do and not to do for a beginner AI DevOps
ŁC: Yeah. As with everything, the process of learning new stuff is painful, and there is no magic recipe for how to pass it. In my case, the best was always learning by doing. That may sound naive, but my advice is that for every problem that young DevOps encounter, try to solve it most efficiently, and this efficient way is with AI assistance. If they need to write a pipeline, they should generate it, if they need to write a Dockerfile, they should use a dedicated AI boosting tool, etc. In many cases, this approach can be opposite to how the older engineers will do that, as they have more experience, more working examples from the previous projects, so they will likely base it on them, and start with them, rather than ask AI for help. Usually, in the projects, younger people are supported by more experienced colleagues who do a code review, and such a mixed approach can bring unexpected results. Young people who will be naturally “integrated” with AI from the very beginning can bring freshness to the project approach and thus gain the recognition of people with an already established position and knowledge.. and I probably do not have to emphasize that all this can only have a positive impact on the quality of cooperation and thus the quality of service delivery
RP: Thank you very much. It is nice to listen to you sharing your knowledge. Thank you for today. We will actually have other interviews like that to get deeper into interesting topics. So this is the first one. I hope we will get some interest from people. Thanks a lot for today.
ŁC: You are welcome. Thanks.