5. The Empirical Argument

Honesty is required about what can and cannot be proven. No one has longitudinal data on spec-driven frameworks because they are too new. But the empirical case can be built from adjacent evidence.

First, the data on Scrum's actual effectiveness is weaker than most people realize. Industry surveys consistently show that organizations struggle with Scrum adoption, that the most common "agile" implementation is really "Scrum ceremonies bolted onto waterfall thinking," and that the correlation between Scrum adoption and delivery outcomes is modest at best. Dandori does not need to prove it is better than ideal Scrum. It needs to prove it is better than Scrum as actually practiced, which is a much lower bar.

Second, the Kanban and flow-based evidence supports continuous flow over batched delivery for teams with high variability in task duration. That describes both AI-augmented work and traditional development: some tasks take hours, some take weeks, and forcing them into uniform sprint containers creates waste. The queueing theory behind this is well-established in operations research, even if it is underappreciated in software methodology discussions.

Third, organizations that track their own AI-assisted delivery metrics can generate internal evidence. When feature delivery time compresses from weeks to days for AI-augmented work, while other features still require traditional human implementation timelines, the question becomes not "should we change?" but "how do we coordinate a team that operates at two different speeds?" Scrum has no answer for that. Dandori does.