Progressive Trajectory-Aware Calibration for Human–AI Handoff in Sales Conversations
Year: 2026
Type: article
Abstract: AI assistants in sales conversations predict conversion probabilities at each turn, but miscalibrated confidence can undermine human–AI handoff decisions. We propose Progressive Trajectory-Aware Calibration (PTAC), a lightweight post-hoc method that adjusts temperature using conversation progress and observed probability trajectories. PTAC requires fewer than 5,000 parameters. On e-commerce data with independently verified outcomes, PTAC reduced turn-stratified calibration error by over 80% at 14% purchase prevalence and by 9% relative to static scaling at the natural 6% prevalence, matching adaptive scaling. It reduced error by 63–78% on call-center data with model-generated labels and by 13% on synthetic dialogues. PTAC improved handoff error rates at fixed human workloads and reduced misplaced reliance at operational thresholds; trust outcomes were simulated and matched-trust performance was comparable. Causal-progress ablation supported deployment-time improvements on call-center and synthetic data; e-commerce gains depended on non-causal turn position.
Source: Figshare
Authors: Ahmet Arda Çelik, Kerem Gencer
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225610598
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