AI coding assistants are often described as productivity tools, but that framing understates their impact. In practice, they behave much more like a new kind of team member. One that works continuously, scales instantly, and never tires. The closest analogy is not a better IDE, but an endlessly available junior developer.
This comparison matters because it exposes a tension in how teams plan work.
Sprint planning has traditionally assumed relatively stable capacity. Teams estimate based on known skills, predictable availability and historical velocity. AI disrupts this by injecting variable, on-demand capability into the system. Tasks that once took days can sometimes be completed in hours. Others expand unexpectedly as generated code reveals hidden complexity.
The result is not simply faster delivery. It is less predictable delivery.
Teams quickly discover that old estimation techniques struggle to cope. Velocity becomes volatile. Comparisons to previous sprints lose meaning. Some work accelerates dramatically, while integration, testing and architectural alignment become the new bottlenecks. Planning conversations shift from effort to risk.
This forces a more honest discussion about what sprint planning is actually for. If the goal is to lock down commitments, AI makes that harder. If the goal is to create shared intent and identify constraints, AI makes it more important. The focus moves away from precise estimates and towards understanding where human judgement is still required.
There is also a behavioural impact worth noticing. When AI can generate large volumes of code quickly, the temptation is to pull more work into the sprint. This can create the illusion of progress while increasing downstream cost. Reviewing, understanding and maintaining generated code still requires human attention, and that attention is finite.
Delivery leaders who navigate this well tend to treat AI capacity as conditional rather than guaranteed. They plan around learning rather than output, allowing space for discovery and correction. They also invest more heavily in architectural clarity, recognising that AI amplifies both good and bad design decisions.
Nagrom’s experience in this space suggests that the teams who thrive are those willing to rethink planning as a continuous activity rather than a fixed ceremony. AI does not eliminate the need for discipline. It changes where that discipline is applied.
Sprint planning is not disappearing. It is being reshaped around a new reality, one where capability is abundant but coherence is not. The teams that adapt will deliver faster without losing control. The ones that do not will struggle to understand why speed feels harder to manage than slowness ever was.
