AI companies increasingly rely on usage data to align pricing with costs, improve products, identify churn risks, and drive revenue through informed decision-making. The piece frames a paradox: AI firms extract intelligence from data yet often lack visibility into how their own software is used. With compute, energy, and third‑party model costs scaling alongside adoption, pricing and packaging must reflect actual consumption; otherwise revenue and margins can diverge from reality.
IDC is cited showing 48% of software buyers want better visibility into usage, while 70% rank rapid change in the AI ecosystem as a top security risk, underscoring the need for reliable usage intelligence to keep models and business models aligned.
Usage intelligence comprises telemetry at the instrumentation layer (activation events, entitlements, feature usage, active users, time-to-value), the operational layer (dashboards in CRM, health scores), the enforcement layer (contracts, rights for consumption-based pricing), and a feedback loop back to pricing, product, and customer success.
The article offers practical examples: shifting from perpetual licensing to tiered subscriptions guided by feature engagement, or trial models tied to executions rather than time, enabled by usage data. It argues that accurate usage data enables three concrete outcomes—preventing churn through early engagement, identifying upsell opportunities via utilisation patterns, and guiding the product roadmap with evidence rather than assumptions. Privacy considerations are noted, advocating aggregated rather than personal data where possible under GDPR or the EU AI Act.
Organisationally, the piece stresses that usage data must reach pricing, product, sales, customer success, and finance teams to realise value. Building a cross‑functional system around monetisation is essential for long‑term growth in a fast‑moving AI market.