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What Most Companies Get Wrong About Monetizing AI Features

تكنولوجيا
Forbes
2026/06/02 - 10:30 501 مشاهدة
InnovationWhat Most Companies Get Wrong About Monetizing AI FeaturesByPranav Lal,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 02, 2026, 06:30am EDTPranav Lal is an enterprise technology leader who has architected and scaled GTM systems for many high-growth pre-IPO to IPO SaaS companies. gettyEvery product leader I have spoken with in the last year is wrestling with the same question: We are shipping AI features that cost real money to run. How do we charge for them?The conversation almost always starts in the wrong place. Product and marketing start a working session, debate per-seat vs. usage-based vs. outcome-based pricing and arrive at a decision. The pricing page gets updated. Finance updates the forecast. Six months later, the same companies are quietly walking back their AI pricing, granting custom exceptions and absorbing margin erosion they did not forecast.The pattern is now visible in the data. In Revenera's 2026 Monetization Monitor, a survey of 501 product leaders, 70% of those offering AI features said delivery costs were eroding profitability, and more than a third cited uncertainty around pricing AI features as a direct blocker to aligning price and value. The postmortem usually blames the market, but in my experience having worked with multiple SaaS companies during their growth and public market transitions, the market is rarely the problem. Pricing was treated as a marketing decision when it was actually a systems decision, and the systems were never built to support what the pricing page promised.​When a company moves from per-seat to consumption-based pricing for an AI feature, it is not just changing a SKU. It is committing to a metering infrastructure that did not exist before. Every AI invocation now needs to be counted, attributed to a customer, aggregated in near-real time, surfaced t...
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