Artificial intelligence has, in recent years, 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 talking about full-cycle transformation. From requirements analysis to code generation, testing, and even deployment, more and more of these steps can now be performed with the help of AI. We are also seeing the rise of AI agents capable of operating independently, making decisions, and executing tasks with minimal human involvement.
These changes are significantly impacting how we organize software development work.
Until now, software development has largely been based on Agile methodologies. Sprints lasting one or two weeks, story point estimation, regular planning, and retrospectives, all designed to structure human work, ensure predictability, and deliver value incrementally. However, this approach is built on a key assumption: that development speed is limited by human capacity. As AI begins to take over this limitation, the entire model starts to crack.
If a task that previously took two weeks can now theoretically be completed in one or two days, a logical question arises: do we still need sprints? What is the value of planning work in detail for two weeks if execution happens significantly faster? And do story points still reflect reality when “effort” is no longer directly tied to output?
The answer is not a simple “yes” or “no.”
If AI can significantly accelerate code development, sprints as a core Agile unit do not completely lose their relevance, but their role begins to change. Their original purpose structuring work around human capacity limits and ensuring predictable delivery, becomes less relevant in an environment where execution speed can vary greatly and increase dramatically.
In smaller or more flexible teams, sprints are increasingly being replaced by continuous workflows and dynamic prioritization. In larger organizations, however, they will likely remain as a structural framework that supports coordination, predictability, and governance.
At the same time, during this transition phase, it remains important to measure time and output in order to compare what was possible before and what is possible with AI clearly demonstrating the business impact.
This is where things become truly interesting, not from a technological perspective, but from an organizational and even 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 coding becomes significantly faster with AI, its value does not disappear, it shifts 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, it is now becoming the ability to effectively manage multiple initiatives, make high-quality decisions, and ensure consistent quality across the entire 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 increasingly see continuous workflows. Instead of rigid planning, more flexible prioritization and more frequent validation. 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 higher 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
- focus on real business value, not just development output
So what is the short conclusion?
AI is fundamentally changing the pace of software development, and with it, the principles of how work is organized. Sprints are not disappearing, but their role is evolving. They are no longer the only or universal solution for all teams. In more flexible environments, they are being replaced by continuous workflows, while in larger organizations they remain a structural mechanism for coordination and control.
At the same time, during this transition, measuring productivity remains critical to clearly demonstrate the value created by AI.
The biggest shift is not just in speed, it is in the focus on higher-level thinking, decision quality, and business value creation.
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 smarter.