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Most organizations invest in experimentation, expecting better decisions, faster learning, and higher ROI. Most don’t get it—not because their analysis is wrong, but because their delivery systems can’t keep up.
A best-in-class experimentation platform running on a monthly deployment cycle still limits teams to ~12 experiments per year. With a typical win rate, that results in only a handful of meaningful outcomes. The constraint isn’t insight—it’s throughput.
This talk reframes experimentation as a delivery problem. Traditional experimentation tools sit on top of a velocity ceiling they cannot raise, while most unrealized ROI sits beneath that ceiling—in manual approvals, fragmented tooling, and brittle release processes.
Drawing on patterns from regulated industries, we’ll explore how leading organizations are breaking through: moving to high-frequency, reliable delivery by standardizing workflows, embedding governance into pipelines, and adopting AI-assisted delivery models.
The takeaway is simple: when delivery velocity increases, every downstream metric improves—from experimentation throughput to reliability and cost efficiency.
If your experimentation ROI has plateaued, the problem isn’t your analysis.
It’s your ceiling.
Key takeaways
- Increasing release velocity unlocks compounding gains across learning, reliability, and cost
- Experimentation ROI is capped by delivery velocity—not analytics capability
- Most hidden value sits inside the delivery pipeline, not the experimentation layer
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