The latency-accuracy tradeoff describes the operational balance between response speed and output quality in real-time AI assistance. Faster responses typically use smaller models, fewer context tokens, or fewer verification steps, while higher-accuracy responses often require more computation, more context, or additional checks that add delay.
In a contact center, this matters because agents and customers experience delays immediately on live calls. If assistance arrives too late, agents ignore it and handle the call without it; if it arrives quickly but is wrong, it can create compliance risk, rework, longer handle times, or customer confusion.
Leaders manage this tradeoff by matching latency targets to the task: time-critical prompts (next-best question, empathy cue, interruption handling) need very low delay, while less time-sensitive tasks (post-call summaries, QA scoring, deeper knowledge retrieval) can tolerate higher latency to improve accuracy.
