In 2026, the AI monetization conversation has shifted quickly from one concern to the next. Early in the year, fears that AI agents would erode software seats helped trigger a selloff of software stocks dubbed the “SaaSpocalypse.” Since then, attention has turned to a different disruption, as AI leaders including Anthropic, OpenAI, and GitHub lean heavily on usage-based pricing to better align revenue with the cost of delivering AI.
It would be easy to conclude that software monetization is shifting wholesale from seats and subscriptions to tokens, credits, and consumption. But look across a broader cross-section of the software industry, and a different picture emerges.
Most software companies aren’t AI-native businesses designing pricing from scratch. They have existing products, customers, contracts, and pricing structures. They’re adding AI to portfolios that may already span dozens of products and multiple monetization models. And they have to balance the economics of AI with something customers still value enormously: predictability.
That tension came through clearly in our new Revenera Monetization Monitor: Software and AI Pricing Models and Strategies 2027 report of 415 product and technology leaders at established software producers and intelligent device manufacturers.
Nearly all are adding AI capabilities to at least some products. The way they’re monetizing those capabilities, though, is considerably more pragmatic than the headlines might suggest.
AI features are widespread, but adoption is lagging
AI capabilities have gone mainstream. The share of software producers incorporating AI into their products jumped from 80% last year to 96% today. Getting AI into the product, however, is only the first step.
The widespread use of chatbots can create the impression that everyone is using AI. That isn’t yet true inside established software products. Almost half of respondents say that adoption of their AI features is limited or low.
For many vendors, that makes the next phase less about launching more AI and more about learning from the AI they have already launched.
Revenera
Seat loss is limited so far, but pricing pressure is real
One of the biggest fears surrounding AI agents is that companies will need fewer software subscriptions, or “seats,” as agents take over work previously done by people. So far, the impact appears relatively small. Only 7% of respondents say they’re experiencing seat loss due to AI.
That doesn’t mean AI isn’t affecting software pricing. Nearly half of software producers report downward pricing pressure related to AI. Some buyers are using the possibility of building their own AI-powered alternatives as leverage in negotiations. At the same time, growing AI budgets are forcing customers to scrutinize what they spend elsewhere in their software portfolios.
The near-term challenge may not be that AI eliminates the seat. It may be that AI changes what customers are willing to pay for it.
Revenera
Demand for predictable costs is influencing AI pricing
Over the past year, stories of AI bill shock have become increasingly common. Consumption-based pricing may closely reflect underlying AI costs, but customers don’t necessarily want a bill that changes dramatically from month to month.
That trade-off shows up clearly in the research. Software producers are almost three times more likely to prioritize cost predictability for customers than maximizing near-term revenue. And that preference is reflected in how companies are pricing AI today.
The three most common approaches are putting AI in a higher-priced subscription tier, including it at no additional charge, and using hybrid models that combine a subscription with some form of usage-based or outcome-based models.
Revenera
What’s striking is that established software companies aren’t rushing toward pure consumption pricing. Over the next 18 months, they expect predictability-oriented approaches to grow faster than pure usage-based models.
AI revenue isn’t automatically translating into AI profit
The economics of AI have become almost as important as the capabilities themselves. For AI-native companies, much of the discussion has centered on the enormous infrastructure costs required to train and run models. Established software vendors face a somewhat different challenge: they are adding those new costs to businesses that already have existing products, architectures, and margin expectations.
Our research includes companies delivering software across SaaS, on-premises, embedded, edge, and other environments. Many are seeing revenue growth from AI, and more than half report improved profitability.
That doesn’t mean the economics are easy. Increased AI delivery costs and cloud costs rank among the biggest barriers to growing annual recurring revenue. So does the inability to implement
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