Evaluation debt accumulates silently as evaluation frameworks fail to keep pace with changing products, data, and pipelines. It can break evaluation processes, disrupt production systems, and undermine user trust, which is critical for shipping AI products at scale. Models rarely block shipping by themselves; instead, evaluation frameworks and their limitations become the primary source of failure. Symptoms can appear as unreliable measurements, misaligned metrics, and evaluation gaps that do not show up in dashboards. Enterprise teams face challenges building evaluation frameworks that scale and evolve over time. Case studies from large personalization and search systems illustrate how evaluation maturity affects growth, retention, acquisition, and global personalization decisions.
"Very rarely do the models actually come in the way of shipping products that thrive. It's actually your evaluation frameworks that can break your products, break your pipelines, and actually touch that user trust, which is so critical for shipping AI products at scale."
"Today, I want to talk about something that's invisible to your dashboards, but deadly to your products, and that is evaluation debt. It accumulates silently and explodes spectacularly. We'll talk about what is evaluation debt, what are the symptoms of it."
"We'll talk about the challenges that enterprise systems, enterprise companies face in building evaluation frameworks, and that can scale and evolve. Then we'll talk about a couple of case studies that I'll bring from my experience building personalization systems across all these companies."
"Then we'll talk about what are the key takeaways, what can we do to assess where we are? What are our maturity models? How can we shape our adoption models going forward? Then wrap with some principles."
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