How AI is changing software pricing
InfoWorld ·
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 new monetization models quickly enough. This is an important distinction. AI can generate incremental revenue and still create margin pressure at the same time. For product and business leaders, the question is no longer simply, “Can we charge for AI?” It is increasingly, “Can we charge for AI in a way that supports sustainable economics?” Revenera Source: Revenera Limited usage data is making AI pricing harder There’s another problem that gets less attention: many companies still don’t have the data they need to make good pricing decisions. A large share of organizations either lack clear visibility into how customers use AI features or rely on manual analysis and engineering work to get that information. That creates a fundamental problem. If you don’t know who is using a feature, how often they use it, or which capabilities drive value, it’s very difficult to know what metric to price against. It’s also harder to distinguish between an AI feature that customers don’t value and one that simply hasn’t been packaged or introduced effectively. Usage data isn’t just a billing requirement for consumption pricing. It’s increasingly an input into product strategy, packaging, and monetization. Revenera Source: Revenera 2027 outlook: AI monetization will be evolutionary, not revolutionary There is no single right way to price AI. The approaches taken by AI-native companies have understandably dominated the conversation in 2026. Established software companies, though, are operating under very different constraints. They aren’t replacing their businesses with a new AI pricing model. They are figuring out how AI fits into products and commercial models that already exist. In practice, I think many companies will move through this process in stages. They may begin by including AI at no additional charge while they build adoption and learn how customers use it. As value becomes clearer, they can move capabilities into higher-priced tiers, often with usage limits that protect against runaway costs. Once usage patterns become more predictable, some will adopt hybrid models that combine a recurring subscription with bundled consumption and transparent pricing for additional usage. Not every product will move through those stages at the same pace. A mature AI capability in one product may already be ready for usage-based pricing while a newly launched feature somewhere else in the portfolio is still focused entirely on adoption. That is why I don’t think the winners in AI monetization will be the companies that pick the “right” pricing model today. The advantage will come from being able to keep changing it. This advantage starts with better visibility into how customers use software, where they find value, and which capabilities actually drive engagement. Companies that can turn those insights into changes in pricing, packaging, and product strategy will be in a much better position to adapt as both AI technology and customer expectations continue to evolve. — New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com .