Brian Le
2

Designing 
Trust & Confidence 



 

AI recommendations only become useful if administrators are willing to act on them. Because these systems directly affect production workloads, every recommendation needed to be understandable, transparent, and easy to validate before it could be trusted.
How do you convince a conservative, highly technical admin to trust AI?
Seeing is Believing
Every recommendation could be inspected before it was enabled. Simulations compared predicted and historical behavior, while detailed runtime options explained exactly what AI proposed changing. The goal was to keep users in control throughout their adoption process.

Earning Confidence
Performance metrics made it possible to compare historical and AI-enabled runs over time. Improvements became visible through familiar operational data, giving administrators another way to evaluate AI as they expanded adoption across their environments.

Seeing is believing: Compare predicted and historical workload patterns before enabling AI, allowing administrators to evaluate recommendations using familiar operational data.



Trust before automation: AI exposed the reasoning behind each recommendation so administrators could validate individual tuning decisions before adopting automated optimization.



Confidence through evidence: Performance metrics compared historical and AI-enabled runs, helping administrators understand how recommendations affected CPU time, memory usage, and other runtime characteristics.