Insight Panels
Insight Panels
Structured perspectives from operators and decision-makers.
Contributors
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Praveen Kumar Koppanati
QA Automation Lead
When AI is only recommending, decision authority usually stays with a human, but even that can be fuzzy unless you deliberately structure it. A recommendation can feel harmless until people begin to depend on it and then you realize the...
Rajesh Sura
Head of Data Engineering and BI, North America Stores, Amazon
AI can surface patterns faster than any team, rank options with remarkable precision, and execute within defined boundaries at scale. But recommending and deciding are fundamentally different acts, and that distinction is exactly where l...
Hemant Soni
Expert in Telecom, Media & Technology
When AI enters the decision loop, ownership does not disappear; it becomes more explicit. AI may recommend, predict, or even execute actions at machine speed, but accountability always remains human and organizational. The decision to tr...
Contributors
Praveen Kumar Koppanati
QA Automation Lead
“Honestly, what breaks first usually isn’t the model. It’s everything around it.” In a prototype, you’re working with clean data, a controlled setup and people who are willing to tolerate rough edges because it’s a demo. The moment you g...
Rajesh Sura
Head of Data Engineering and BI, North America Stores, Amazon
In a prototype, everything works because everything is controlled. The data is clean, the scenarios are curated, and the team running it already believes in it. Production is none of those things. What breaks first is rarely the model. I...
Contributors
Vivek Pandit
Frontier AI Lead - RL Environments
I believe we need to first understand what's the utility of evaluations. Evaluations as a tool for quantitative benchmarking and setting a common source of truth that people can agree on is really important to establish. This helps set a...
Praveen Kumar Koppanati
QA Automation Lead
When benchmarks stop reflecting reality, the first thing I remind myself is that benchmarks are not “wrong”, they’re just safe. They’re clean, stable and predictable. Production is none of those things. In the real world, data shifts, us...
Deepak Dasaratha Rao
Benchmarks stop being useful the moment they become “clean-room exams”: static data, stable labels, and a single notion of success. In production, you care about outcomes, risk, cost, and experience. Decision quality lift over baseline (...