In recent years, artificial intelligence has evolved from a theoretical concept into a practical tool that is rapidly transforming almost every industry. However, the most visible changes are currently happening in IT, the very space where these technologies are not only used, but also created.
If automation once meant simplifying individual tasks, today we are seeing a full-cycle transformation. From requirements analysis to code generation, testing, and even deployment, an increasing share of these activities can now be supported by AI. We are also witnessing the rise of AI agents capable of operating independently, making decisions, and executing tasks with minimal human involvement.
These changes are fundamentally reshaping how software development is organized.
Until now, development has largely been based on Agile methodologies – sprints lasting one or two weeks, story point estimation, regular planning, and retrospectives. All of this was designed to structure human work, ensure predictability, and deliver value incrementally.
However, this approach is built on one key assumption: that development speed is limited by human capacity. As AI begins to remove this limitation, the model itself starts to break down.
If a task that previously required two weeks can now be completed in one or two days, a fundamental question arises: do we still need sprints? What is the value of detailed two-week planning if execution happens significantly faster? And do story points still reflect reality when “effort” is no longer directly tied to output?
This is where the discussion becomes truly interesting, not from a technological perspective, but from a philosophical one.
We are no longer just talking about working faster. We are talking about how work organization and value creation models are changing.
As AI significantly accelerates coding, it does not eliminate its value, it shifts its role within the overall process. As a result, other activities become more important:
- defining the right problem and business objective
- understanding customer and user needs
- making architectural and design decisions
- validating the quality and security of results
The focus shifts from individual execution speed to the overall capacity of the team and organization.
Where development speed used to be the primary constraint, the bottleneck is now the ability to manage multiple initiatives effectively, make high-quality decisions, and maintain consistent quality across the delivery process.
This also changes how performance is measured:
- less focus on velocity
- more focus on capacity and throughput
- the ability to handle multiple initiatives simultaneously without compromising quality
In this context, Agile principles may take on a new interpretation. Instead of fixed sprints, we may see a shift toward continuous workflows. Instead of rigid planning, more flexible prioritization and more frequent validation.
Similarly, instead of story points, the key question becomes: what business impact does this work create, and how efficiently can we deliver it?
At the same time, the importance of domain expertise increases. The faster AI can generate solutions, the more critical it becomes to evaluate them:
- Is the solution correct?
- Does it fit the specific context?
- Is it secure, maintainable, and scalable?
In the future, competitive advantage will not come from simply using AI, but from combining it with strong domain expertise and rigorous validation.
AI does not replace software development companies, it enables them to operate at a different level, with greater capacity, broader impact, and higher added value.
What does this mean for clients?
- faster delivery without compromising quality
- the ability to develop multiple initiatives in parallel
- greater flexibility in a changing business environment
- a stronger focus on real business value, not just development output
At LTECH, we are already actively using AI in our development processes, not as an experiment, but as a practical tool that helps our clients achieve results faster and more intelligently.