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.

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.

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.

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?”

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.

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.

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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.

Take a close look at your monthly software bills. The charges that once seemed small and separate can add up to a new kind of dependency: subscriptions for word processing, creative tools, storage, and now AI features bundled into products, whether you asked for them or not.

For many people, it’s no longer a choice between buying software or renting it. The software they used to run on a PC increasingly depends on an account, an internet connection, a cloud service, or a particular plan. Even when an application runs locally, its essential features may rely on online authentication, remote storage, or services that can change without much notice.

Microsoft and Adobe are prominent examples of this shift. Their subscriptions offer real benefits, including frequent updates, collaboration, and access across devices. But people who are looking for a dependable word processor or video editor can find themselves navigating bundles, account requirements, cloud storage prompts, and AI features they don’t want. Users are growing frustrated that they have little choice about being forced into cloud services.

Interest in local computing

The response is a renewed interest in local computing: software that runs on a person’s own computer, stores work where they choose, and does not require a recurring payment for basic use. This isn’t a rejection of cloud computing. It’s a demand for a more balanced relationship between local machines and remote services. Home users and small businesses are turning to open source and other locally installed applications. They may not offer every advanced feature of a commercial suite, but many users don’t need every feature. If a free, locally run tool can handle the work, then “good enough” can be a rational decision.

The same logic applies to AI. Some users want powerful models hosted in the cloud. Others want to experiment with AI on their own hardware, keep sensitive information close, or avoid paying for an always-online service. Local AI still has limitations, including hardware demands and uneven performance. But it gives people another option, and options matter when customers feel locked into a remote service.

Cloud computing is also no longer limited to systems an organization deliberately chooses to put in a public cloud. It is embedded in everyday software. A productivity application can depend on identity services, online licensing, synchronization, remote storage, telemetry, or AI services. A tool that looks like a desktop program may be part of a much larger cloud relationship. Such an arrangement can be useful. Centralized updates and shared documents make work easier for distributed teams. But when software depends on a provider’s back end, the organization inherits risks it may not have assessed: outages, price changes, loss of access, data retention questions, and shifting product terms.

Users are voting with their feet when they choose local alternatives. Providers should read that vote as feedback, not nostalgia. Customers want the convenience of cloud services without surrendering control over where their software runs, where their files live, or whether an AI assistant is part of the package.

Providers should offer real options

The answer is not to abandon cloud platforms. For many workloads, cloud infrastructure remains the best option for scale, resilience, collaboration, and access to managed services. The answer is to stop treating cloud dependence as the inevitable price of using modern software.

Providers can make the choice real by offering offline access where practical, clear and durable licensing, straightforward file export, and local storage as a genuine option. They should explain which features require connectivity and what happens when a subscription ends. If AI is included, users should be able to understand what it does and how to turn it off without losing unrelated functionality.

This is not just a consumer relations issue. It is strategic planning. Enterprises should consider three things now.

First, inventory the software employees rely on and identify which functions depend on a vendor’s cloud. Include desktop applications, browser extensions, identity systems, storage, and AI features. Record what breaks during an outage or after a license expires. A dependency map can reveal that a supposedly local workflow relies on remote services for authentication, collaboration, or even basic operation. That knowledge helps organizations plan for continuity and negotiate from a position of understanding.

Second, establish policies for where work data may be stored and processed. Decide which information can go to external services, which AI tools are approved, and how employees can use local alternatives. These rules should be practical enough to follow. A blanket prohibition may drive users toward unsanctioned tools. A clear set of approved choices can reduce risk while recognizing that teams have different needs.

Third, build flexibility into long-term procurement and architecture. Evaluate products not only for features and subscription cost, but also for offline capability, data portability, exit terms, and support for local deployment. Keep important workflows from depending on a single vendor when a reasonable alternative exists. This does not mean avoiding subscriptions. It means knowing what the organization receives for the recurring cost and how it can leave if the deal changes.

The local computing movement is a reminder that technology is valuable when it solves a problem on terms users and organizations can accept. For some needs, that’s a managed cloud service. For others, it’s a dependable application running on a computer the user controls.

Cloud providers that dismiss this movement risk confusing convenience with consent. Customers will keep choosing tools that fit their work, budgets, and privacy expectations, even when those tools are simpler. The winning strategy is not to force every workload into the cloud. It is to make cloud services compelling enough that customers deliberately choose them, while preserving a credible path to local computing when they do not.

Every quarter, I look at the capital expenditure numbers coming out of the major cloud providers, and every quarter, they manage to surprise me. This time, though, we’ve crossed into territory that’s genuinely hard to process. The five biggest US cloud and AI infrastructure providers (Microsoft, Alphabet, Amazon, Meta, and Oracle) have collectively committed to spending between $660 billion and $690 billion on capex in 2026, nearly doubling what they spent just a year ago.

Amazon leads the pack with a projected $200 billion, most of it directed at data centers and AI compute. Alphabet has committed $175 to $185 billion, up from $91 billion in 2025. Microsoft is tracking toward $120 billion or more, having already spent $37.5 billion in a single quarter. Meta is at $115 to $135 billion, and Oracle is targeting roughly $50 billion. For context, four companies alone plan to invest about $630 billion, a 62% increase from the record $388 billion they spent in 2025.

What are they buying? Mostly chips, buildings, and electricity. The spending splits across the AI data center stack: GPUs and custom accelerators such as Google’s Tensor Processing Units and Amazon’s Trainium, the physical data centers themselves with their liquid cooling and high-density floors, and often the power infrastructure to run it all, including power purchase agreements and even on-site gas-fired generation where the grid can’t keep up. Roughly two-thirds of the money goes to compute, and one-third goes to facilities, power, cooling, and networking, with the power share rising every quarter.

Where the market is heading

My take on the market is that we’re in the steep part of a demand curve that has a few more solid years left in it. Short-term demand over the next two years is already firm. Every hyperscaler reports being supply-constrained rather than demand-constrained, and Microsoft is sitting on an $80 billion backlog of Azure orders it can’t fulfill, largely because of power availability. Enterprises are panic-buying cloud capacity and computing gear right now because they’re convinced the AI boom will leave the unprepared behind, and they want their infrastructure in place so they can take advantage of whatever form the transformation takes. I get it. Nobody wants to explain to their board in 2028 why they spent 2026 waiting in line.

This means hardware vendors such as Dell, HPE, and IBM, along with the hyperscalers themselves, are going to see sustained growth as long as the demand continues. Some of it is real, and some of it is fear driven, but for the vendors, it all spends the same. The shortages enterprises are seeing today for processors and RAM, along with the associated price increases and stretched lead times, will most likely persist through that window because global silicon and memory supply is being absorbed by AI clusters faster than the manufacturers can expand it.

But my long-term forecast is deliberately less rosy than the providers’ own. The build-out cycle runs three to four years from commitment to full utilization. A lot can change in the AI market in that time. Model efficiency improvements could slash compute requirements per unit of output. Enterprises may discover that their AI workloads don’t justify the cloud spend once the novelty fades. Competitive dynamics could compress pricing. By 2028 and 2029, I expect the growth rate to be real but not at the scale builders assume today, and I expect the industry to carry the weight of capacity committed in the panic phase of 2026.

Are vendors overbuilding?

Here is where I part ways with the prevailing wisdom, and where I think we need some disciplined skepticism. The thinking right now is that for every bit of data center capacity you build, someone will use it, if not the hyperscaler that owns it, then some other tenant. Given Microsoft’s $80 billion backlog, that logic holds for the near term.

But I’ve been around long enough to be suspicious of “this time is different” arguments. I watched the fiber overbuild in the late 1990s, and I watched the first cloud buildout get ahead of itself as well. The concern is that the cloud providers are getting out over their skis, building capacity for demand that may not show up at the scale they’re projecting. The revenue those investments depend on is largely speculative. If AI adoption progresses more slowly than anticipated, or if efficiency gains reduce the compute required per workload faster than expected, the return on this spending could disappoint badly. There is also the uncomfortable possibility that a meaningful share of today’s demand is circular: Enterprises and vendors may be panic-buying capacity from each other in a stack of commitments that may never consolidate into actual sustained workloads.

By the time these data centers come online—and we’re talking about capacity committed now for delivery in 2027—the growth may not be at the scale the builders are expecting. I expect many of the data centers being committed now won’t be utilized at the rates people are assuming today for 2029. That’s not a prediction of collapse. It’s an acknowledgement that the industry has seen a similar pattern before in fiber, colocation, and the first cloud buildout itself.

Advice for enterprise buyers

If you’re running enterprise IT, this cycle cuts both ways. In the near term, you’re facing higher prices, allocation limits, and longer lead times for processors, RAM, and cloud capacity. Budget accordingly, lock in what you genuinely need, and resist buying out of the fear that drives the very shortages you’re suffering from.

But in the next few years, the picture flips. Overbuilt supply means lower prices. If the providers overshoot, then cloud rates soften and less expensive data center space becomes available for enterprises that need it, including capacity for your own AI ambitions at prices that would be unthinkable today. The overbuilding that will burn the hyperscalers’ investors will create a bargain for their customers.

The playbook I recommend is the one I’ve always recommended. Architect for the cloud on your own terms, stay out of long-term capacity commitments you can’t justify with current demand, and let the providers take the utilization risk. They’re betting hundreds of billions that the demand curve keeps climbing. You don’t have to make the same bet to benefit. The oversupply, if it comes, will find you, and at a price you’ll like a lot better than today’s.