Artificial intelligence companies are increasingly relying on usage data to align pricing with operational costs, improve product development and identify churn risks, as first reported by Security Affairs.
Many AI firms currently lack visibility into how their software is utilized, leading to pricing decisions based on assumptions rather than factual consumption metrics. Because AI products often involve variable costs such as compute power and model licensing, failing to track usage can result in expenses outpacing revenue. Research indicates that 48% of software buyers desire greater transparency regarding their usage, while 70% of industry participants identify rapid ecosystem changes as a primary security and business risk. To address these challenges, companies are implementing usage intelligence systems that collect telemetry data, including activation events and feature-level engagement. This information is processed through operational dashboards to provide actionable insights for sales and customer success teams. By integrating this data into pricing and product roadmaps, organizations can transition from static models to flexible, consumption-based structures. Ultimately, establishing a robust feedback loop between product consumption and business strategy allows firms to mitigate churn, optimize revenue and ensure long-term commercial sustainability in a competitive market.
Source: Security Affairs
