The price is wrong: AI cost calculation has to consider task completion rates, not just token costs
AI cost calculations must shift from focusing solely on token prices to measuring task completion rates, because cheap models that fail to finish tasks end up more expensive overal
By The Register
AI cost calculations must shift from focusing solely on token prices to measuring task completion rates, because cheap models that fail to finish tasks end up more expensive overall. The core argument is that low token costs can be misleading if the AI does not successfully complete the assigned job, forcing repeated attempts or human intervention that drive up the true expense.
Industry analysis shows that 2026 is marking a transition where support AI is being priced like labour rather than software, with vendors charging per resolved task or conversation instead of just per token. Current market rates for an AI-resolved ticket range from about $0.49 to $2.00, with $0.99 emerging as a reference price for fully resolved conversations without human help.
Experts recommend tracking metrics such as task completion rate, error recovery rate and token efficiency ratio to determine whether an agent deployment is cost-viable at enterprise scale. Output-only evaluation is insufficient because it misses execution-level failures that break production deployments, meaning trace-level analysis is essential for accurate cost assessment.
Businesses should log the cost of every LLM call including input and output tokens, then aggregate these per conversation to get a true picture of resolution costs rather than just token spend. The most effective approach involves running hybrid systems where AI handles initial resolution and humans escalate complex cases, which can reduce cost-per-resolution by around 71% while maintaining customer satisfaction.
Organizations are advised to measure a baseline before starting, run a focused pilot and only scale once the numbers prove the ROI is real rather than merely deferred savings.