Coding with AI changes teamwork and the agile process
Developer teams are changing their structures and ways of working through the use of AI. They need to reposition themselves, says Facundo Giuliani.
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AI assistants are changing the job profile of developers, with pure coding becoming less important while conceptual skills gain significance. This has implications for the work of individual coders, as well as for the team and the agile process. heise developer speaks with Facundo Giuliani, Team Lead for Solutions Engineering at Storyblok, about the future of developer teams in the AI era.
How does the increased use of coding assistants affect the structure of development teams?
Development teams are increasingly moving towards hybrid competence profiles where pure programming is only one part of the added value. Conceptual thinking, problem contextualization, and integration know-how will be just as crucial as writing code. This often leads to cross-functional teams where developers, designers, and product owners collaborate earlier and more closely. Companies will increasingly organize their teams around problem areas and desired outcomes, rather than just code delivery.
What does that mean in practice?
Teams will be built around specific customer or business challenges. For example, to optimize onboarding conversion or shorten content publishing time, rather than being responsible for a specific codebase. Developers, designers, analysts, and AI specialists will work together from the start, defining the problem, testing hypotheses, and iterating quickly. Success will no longer be measured by story points or code commits, but by real impact criteria such as usage rates, performance improvements, or reduced manual work.
There are speculations that AI will worsen job prospects for young developers. Will the age structure in teams change in the future?
Entry-level roles will not disappear due to AI, but the importance of entry-level jobs will shift: New entrants will increasingly engage with more complex tasks with AI support, rather than primarily executing or generating code. This can even make entering the tech field more accessible, as the barrier to experimentation decreases, allowing for more diverse entry opportunities. Within teams, there will likely be a balanced mix of experienced professionals setting the strategic direction and younger talents who can learn faster and achieve results with the help of AI.
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This also improves opportunities for career changers?
Absolutely! AI assistants and low-code tools lower the technical entry barrier, enabling people with design, content, or data backgrounds to meaningfully participate in software projects. The focus on problem-solving, creativity, and communication opens up new avenues for career changers to create added value, even without deep programming knowledge from the outset.
How will teams work in the AI future?
Teams will increasingly take on the orchestration of systems rather than coding line by line themselves. They will curate and validate AI-generated components. The daily work will combine human judgment with automated tests, security scans, and AI assistant-powered continuous delivery pipelines. Developers will focus more on architectural decisions, quality assurance, and cross-platform integrations to ensure reliable results from machine-generated code.
Will new roles also emerge in development teams?
We are already seeing new hybrid roles emerge within product and platform teams – for example, the role of Prompt Engineer or System Orchestrator. These positions combine human intention with machine execution and shape how different agents, APIs, and content systems interact with each other.
Does the widespread use of AI assistants also have an impact on the agile process?
AI assistants shorten the iteration cycle, significantly accelerating planning, backlog maintenance, and prototyping. Stand-ups and retrospectives will increasingly focus less on the status of individual tasks and more on validating assumptions and steering AI results. Agile processes will evolve to prioritize experimentation, metrics, and continuous validation, rather than just sprint throughput.
What considerations should teams make now to remain future-proof?
Teams should invest more in competence development around architecture, integration, and orchestration, as these form the basis for long-term success. Establishing and testing standards, as well as robust observability, are crucial for integrating AI safely and at scale. Most importantly, however, teams should foster a culture of flexibility where developers are encouraged to learn new tools, collaborate across disciplines, and view AI as a partner rather than a replacement.
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