Device manufacturers are struggling to scale and grow revenue because monetizing software is fundamentally different to selling hardware.

As physical products evolve into intelligent, software-defined systems, the way value is created is shifting faster than the way it is commercialized.

Engineering teams are embedding AI, connectivity, and analytics into products designed to operate for 10 to 15 years in the field. Commercial models, however, often remain anchored to hardware-era assumptions. Features are built first. Pricing models are bolted on later.

The gap between digital innovation and monetization architecture is where structural friction accumulates.

Monetization is an Architectural Decision

When entitlement governance, pricing flexibility, and lifecycle strategy are retrofitted rather than designed in, companies accumulate monetization debt. These structural constraints limit agility, complicate compliance, and slow digital expansion across portfolios.

Manufacturers at the forefront of digital transformation trends treat monetization infrastructure as foundational, alongside connectivity, cybersecurity, and analytics.

The following six architectural decisions determine whether monetizing software becomes a competitive advantage or a structural bottleneck.

1. Define Software as a Strategic Asset

Before debating pricing models, leadership must determine the structural role software plays in the value proposition.

Is software a differentiator? A modular enhancement? Or a service platform that evolves independently of hardware?

Organizations that continue to bundle software implicitly with hardware often limit their ability to evolve packaging and pricing over time. Elevating software to a strategic asset requires alignment across product, engineering, finance, and sales.

Siemens Building Technologies recognized that increasing value was coming from digital services and formalized software as a revenue driver rather than an accessory. That shift required governance and cross-functional ownership, not just feature innovation.

For industrial OEMs, this transition is cultural as much as technical. Hardware organizations are optimized for physical SKUs and transactional revenue. Monetizing software demands lifecycle ownership, portfolio governance, and executive sponsorship.

2. Align Monetization with Customer Perception of Value

Industrial monetization succeeds or fails based on where value is positioned.

Recurring fees tied directly to device-level functionality often encounter resistance. Customers investing in capital equipment are cautious about incremental recurring charges layered onto hardware assets.

By contrast, monetization positioned at the enterprise or service layer, such as centralized visibility, predictive analytics, alarm filtering, performance optimization, and managed services, resonates more strongly. Customers are more willing to invest in measurable operational outcomes than in feature access alone.

Monetization architecture must therefore support layered value capture, enabling differentiation between embedded capabilities and enterprise-level digital services without engineering dependency.

3. Architect Pricing for Long-Lifecycle Product Markets

Intelligent device manufacturers operate under constraints that differ significantly from SaaS-native environments:

• 10-to-15-year product lifecycles
• Multi-generation installed bases
• Channel-driven distribution
• Hybrid CAPEX and OPEX procurement
• Regulatory oversight

Selecting a pricing model such as subscription, usage-based, tiered, or outcome-aligned pricing requires deliberate alignment with these structural realities.

Teradici’s transition from hardware sales to monetizing software via subscriptions illustrates the operational shift required. By implementing structured entitlement governance, the company enabled recurring revenue while supporting both on-premises and cloud deployments.

Organizations that automate entitlement and licensing infrastructure have reduced licensing enablement time for new product releases by up to 90%, converting administrative delay into measurable revenue acceleration.

In long-lifecycle markets, monetization maturity is operational as much as commercial. Without lifecycle-aware architecture, pricing flexibility erodes and portfolio complexity compounds over time.

4. Establish Monetization as an Enterprise Capability

When device manufacturers start monetizing software, many build licensing internally, embedding it within engineering teams. While this may address short-term requirements, it diverts focus from core product innovation and rarely scales across portfolios or acquisitions.

Compounding the challenge, organizations often lack a clearly defined licensing owner. Responsibilities are fragmented across engineering, IT, and sales operations. Without centralized accountability, entitlement strategy evolves reactively, resulting in manual workflows, inconsistent packaging logic, and delayed self-service initiatives.

Entitlement governance intersects commercial systems, compliance frameworks, lifecycle management, and customer experience. It is a specialized enterprise capability, not merely a firmware feature.

Treating monetization infrastructure as enterprise architecture rather than product code reduces technical debt and supports long-term scalability.

Industry analysis indicates that organizations can recapture approximately 4% of annual software revenue lost to noncompliant use by identifying that unlicensed use and converting it into new or additional revenue. In large portfolios, that percentage can represent tens of millions in unrealized or recoverable revenue. Monetization architecture is not theoretical risk mitigation. It is balance sheet protection.

5. Design for Governance, Compliance, and Self-Service

In regulated industries, feature activation is not simply a commercial action. It can impact validation workflows, audit trails, export controls, and cybersecurity posture.

Manual, file-based licensing models remain common in industrial environments. When provisioning, upgrades, and entitlement changes require support intervention, scalability becomes constrained by internal capacity rather than market demand.

Self-service entitlement provisioning enables customers to purchase, activate, and upgrade capabilities without manual order creation. This shifts monetization from operational friction to scalable digital execution.

A global medical equipment manufacturer launching AI-powered SaaS solutions across more than 200 markets faced this challenge. By leveraging enterprise-grade entitlement management solutions rather than building ad hoc systems internally, the company accelerated time to market while maintaining compliance and traceability.

Benchmarks from organizations monetizing software indicate that implementing structured entitlement governance can achieve payback on infrastructure investment in less than six months due to operational efficiency gains and revenue acceleration.

Governance designed early prevents friction later.

6. Unlock AI-Driven, Outcome-Aligned Monetization

The next phase of industrial differentiation will increasingly be driven by embedded AI capabilities such as predictive maintenance, proactive alert filtering, dynamic performance optimization, and real-time operational adjustment.

Unlike traditional feature licensing, these capabilities generate measurable operational impact. As energy efficiency improves, downtime decreases, and maintenance costs decline, monetization can align directly with realized outcomes.

When entitlement governance integrates with usage visibility, organizations gain the flexibility to introduce performance tiers, usage thresholds, and outcome-aligned AI pricing models without rewriting firmware or multiplying SKUs.

As AI becomes embedded across intelligent devices, monetization architecture must enable adaptive value capture across the product lifecycle.

Monetizing Software to Drive Growth

In many industrial organizations, thoughts about monetizing software only occur after digital products are built. Yet the ability to evolve pricing, activate features remotely, govern compliance, support self-service provisioning, and monetize installed bases often determines which manufacturers scale digital revenue successfully.

The most effective strategies do not force digital business models onto hardware. They align pricing with perceived value, decouple entitlement governance from product code, design for regulatory complexity, and enable enterprise-layer monetization where ROI is clearest.

As differentiation shifts from mechanical performance to software intelligence, competitive advantage will not belong to those who build the most features. It will belong to those who architect how those features are commercialized.

Monetization is not a pricing exercise. It is strategic infrastructure.

If you’d like expert advice on monetizing software across your device portfolio, please contact Revenera.

SAP recently signaled a major shift in how it plans to charge for AI, moving away from subscription pricing in favor of usage-based monetization. The rationale is simple and hard to ignore: as AI automates work, the number of users no longer reflects the value delivered.

That message matters because SAP isn’t experimenting on the margins. When a company of that scale publicly rethinks pricing around consumption, it confirms what many software leaders already sense: usage-based monetization is moving from optional to inevitable.

But here’s the part that often gets overlooked. If you’re planning a move to consumption-based pricing, your first step isn’t pricing. It’s usage analytics.

Usage-Based Monetization Starts Long Before Pricing Changes

Across SaaS, enterprise software, and AI-driven products, teams often treat usage-based monetization as a commercial decision. They debate terminology, such as tokens versus credits, prepaid versus postpaid, or how to position hybrid pricing models.

Those conversations matter, but they are downstream.

Consumption-based licensing only works when organizations understand how their products are actually used. Without this foundation, pricing becomes guesswork, and guesswork leads to customer friction, revenue leakage, and internal mistrust.

Before finalizing your AI pricing strategy, you need clear answers to basic questions, including:

• What’s being used, and by whom?
• Which actions correlate with value, renewal, and expansion?
• What does normal usage look like across customers and segments?
• Where do usage spikes occur, and why?

Without this strategic insight, usage-based monetization fails to deliver value for both buyers and suppliers.

The Tree You Should be Planting Today

As the proverb goes: the best time to plant a tree was 20 years ago. The second-best time is today.

For software monetization, the modern version is straightforward:

If you think usage-based monetization is in your future, you should already be collecting usage analytics.

Even if your pricing model isn’t changing this year, the data you collect now becomes the baseline for future decisions. It gives product teams evidence to define value metrics. It gives finance teams confidence in forecasting. It gives customers transparency when consumption becomes part of the commercial relationship.

Delaying the implementation of software usage tracking until pricing decisions are made effectively means you’re pricing blind.

AI Makes Usage Analytics Non-Negotiable

AI doesn’t just increase usage. It changes usage patterns entirely.

AI workloads can scale rapidly and unpredictably, often driven by automation rather than human interaction. That volatility is why AI monetization conversations focus so heavily on AI tokens, capacity allocation, and consumption limits.

But none of those mechanisms work without accurate, real-time usage visibility, measured consistently, transparently, and in a way customers can trust.

You cannot monetize what you cannot measure.

Billing isn’t the Hard Part

Many usage-based monetization initiatives focus too narrowly on billing. Billing answers one question: how much was used?

Modern software monetization must also answer a more important one: what is the customer allowed to do right now?

That real-time entitlement decision is where consumption-based licensing either builds confidence or erodes it. Customers expect clarity. They want to see usage as it happens, understand limits, and avoid surprise costs. Internally, finance teams need auditable data, and product teams need the freedom to evolve packaging without reengineering applications.

This is why entitlement management and usage analytics must be designed together. Usage-based monetization isn’t just about invoices. It’s about governing access, consumption, and value delivery in real time.

Your Path to Usage-Based Monetization

For technology companies preparing for usage-based AI licensing, the most effective approach is incremental and evidence-driven.

1. Establish a usage truth layer. Start collecting granular, accurate usage analytics now, even if pricing isn’t changing yet.
2. Connect usage to entitlements. Define what customers are entitled to consume and under what conditions.
3. Introduce consumption mechanics with guardrails that protect both customers and revenue.
4. Iterate pricing and packaging based on real-world usage patterns, not assumptions.

This sequence allows teams to move toward usage-based monetization with confidence instead of urgency-driven compromises.

Alignment with Revenera

Revenera’s approach to software monetization is grounded in the understanding that usage-based pricing succeeds only when measurement, entitlement management, and governance work together.

Revenera enables real-time, auditable usage capture that supports consumption-based licensing models. Pricing and packaging can be adjusted through configurable rate tables without disrupting applications or customer workflows. Usage insights are designed to support both operational decision-making and customer transparency.

This foundation allows organizations to introduce usage-based monetization deliberately, adapt as markets evolve, and avoid the trust issues that arise when monetization outpaces measurement.

Download Your Free eBook: AI Monetization, Done Right

The Big Takeaway

SAP’s move toward usage-based monetization isn’t just news. It’s a signal.

If introducing AI tokens and consumption-based pricing is part of your roadmap, the most strategic move you can make today isn’t to finalize a pricing model.

It’s to start collecting the usage analytics you’ll need to price, package, and govern consumption effectively.

The best time to plant that tree was years ago. The second-best time is now.

If you’d like to discuss how to implement usage-based monetization, please contact us and a member of our team will be happy to talk.

Software usage tracking used to be fairly straightforward. You collected data, generated reports, and reviewed them periodically to understand how your software was being used.

That approach no longer holds up.

As technology companies move toward SaaS, hybrid, and usage-based monetization models, usage data has become central to how value is delivered, measured, and renewed. The question isn’t whether you’re tracking usage, it’s whether that data is giving you a clear, shared view of what’s actually happening across your customer base. Detailed software usage tracking should reveal engagement levels, feature adoption, service denials, and upcoming renewals – allowing you to identify churn risk and potential expansion opportunities.

Recent updates to the Monetization Analytics Dashboard in FlexNet Operations reflect this shift, focusing on making usage insight easier to access, interpret, and share across teams.

Key Benefits of Software Usage Tracking and Reporting

1. Clearer insight into customer value: In modern software businesses, revenue doesn’t stop at the point of sale. Renewals, expansion, and long‑term growth all depend on how customers adopt and use what they’ve purchased. Software usage tracking helps teams see whether customers are getting real value, rather than relying on assumptions or surface‑level signals.
2. Earlier visibility into risk and opportunity: Usage patterns often tell a story long before renewal conversations begin. Strong adoption can point to expansion potential; while declining or uneven usage can highlight risk early enough to act. With the right reporting in place, teams can move from reactive conversations to proactive engagement.
3. More confident, data‑driven decisions: When usage data is clear and accessible, decisions become grounded in evidence rather than anecdotes. Product, revenue, and customer success teams can align around the same understanding of what’s working, what isn’t, and where to focus next.

How to Track Software Usage More Effectively

The real challenge isn’t collecting data; it’s making it usable.

Usage insight is often fragmented across systems and reports, with each team seeing only part of the picture. Product teams might focus on feature adoption, finance teams on entitlements and revenue, and customer success teams on engagement signals. When those views aren’t connected, teams are left piecing things together manually, often without the clarity or time needed to act.

In this kind of environment, the data quickly loses impact. Static reports and siloed insights make it difficult to answer even basic questions about how customers are using what they’ve purchased. What’s needed isn’t more tracking, it’s shared visibility that teams can trust and interpret in their own context.

Modern, flexible software usage tracking helps close that gap. Not by giving everyone the same dashboard, but by creating a shared operational view of entitlements, usage, and adoption that different teams can explore the same underlying data in ways that align with their role and priorities. Customizable views and interactive experiences make it possible for teams to work from a single source of truth, while still focusing on the metrics and KPIs that matter most to them.

Renewal risk becomes visible earlier because declining adoption or unmet entitlements stand out in real time. Expansion opportunities are easier to identify because usage patterns clearly show where customers are getting value, and where there’s room to grow. Product decisions become more grounded, guided by how software is actually being used rather than assumptions or anecdotal feedback.

Software Usage Reports That Go Deeper

Revenera’s Analytics Dashboard offers several smart software usage tracking reports, including:

• Service Denials: When customers are denied access due to insufficient capacity or use rights, this is a clear signal of an upsell or cross-sell opportunity – especially if it’s repeated behavior over a prolonged period.
• License Server Usage: Drill into specific feature usage to see whether key customers are making the most of your product, or if action is needed to boost adoption. This data can also inform overage billing scenarios.
• Fulfillment: By comparing available quantities to activations, you can see if customers are maxed out and could benefit from more, or if you need to intervene to reduce churn – either by driving adoption or recommending right-sizing, which improves satisfaction and lowers cancellation risk.
• Renewals: A real-time view of upcoming renewals helps your team stay ahead. You can also conduct customer churn data analysis by filtering churn by region, allowing you to audit expired entitlements and assess your options for re-engagement campaigns.

Each report supports interactive drilldowns and filtering by key attributes, such as Product and Account, making it easy for teams to derive insights relevant to their specific needs. These reports become even more powerful when paired with conditional notifications. Instead of reviewing trends after the fact, teams can define rules, such as repeated service denials over time or declining usage ahead of renewal, and receive alerts as soon as those conditions are met, enabling faster, more targeted follow-up.

Ready to Get Started?

As analytics capabilities continue to evolve, focusing on visibility and accessibility lays the groundwork for deeper insights in the future. More advanced analysis, predictive signals, and intelligent decision support all depend on having a solid foundation in place first.

The latest Analytics Dashboard update is built with this reality in mind. A more modern, interactive experience makes it easier to explore entitlements, usage, activation, and renewal data in one place, with customizable views that reflect different products, customers, and business priorities. This helps teams work from a shared, real‑time view of adoption and value, rather than relying on static reports that quickly fall out of sync in hybrid monetization models.

If you’d like more information on how Revenera’s software usage tracking capabilities can help you grow recurring revenue with insights delivered to your inbox, let’s talk.

Software monetization is at an inflection point, as the challenge of AI licensing creates new layers of risk, uncertainty, and opportunity.

Traditional monetization strategies are struggling to keep pace with the demands of scaling AI-powered solutions. Costs are unpredictable, usage patterns are dynamic, and customers expect flexibility without surprises.

In this environment of rapid experimentation, producers face a critical question: How do you capture economic value while continuing to drive innovation?

The Economic Value Principle

At its core, AI licensing should ensure fair value for customers and sustainable profit for producers.

Historically, perpetual software licenses shifted most of the financial risk to the customer. Buyers paid upfront and then assumed responsibility for deployment, integration, maintenance and long-term infrastructure investment, regardless of whether the application ultimately delivered the expected business value.

Once the license was sold, the producer’s revenue was largely fixed. If the customer achieved exceptional outcomes, the producer did not participate in that upside. If the customer underutilized the software, the financial burden remained with them.

Subscription models improved this dynamic by lowering upfront commitments and spreading payments over time. However, they still rely on relatively stable cost structures. When underlying costs fluctuate significantly – as they do with AI workloads – fixed subscription pricing can create margin pressure for producers or misalignment with customer value.

Why Subscription Alone Isn’t Enough

AI breaks the assumption of delivery cost stability. Compute and storage requirements can spike unpredictably, so fixed subscription pricing (even if recurring) can erode margins.

Subscriptions also don’t serve customers well when usage varies significantly over time. Some customers overpay relative to value received, while others generate costs that outpace the subscription price. Neither scenario is sustainable.

AI is changing the economics of software. When customer usage directly impacts producer costs, AI pricing models must evolve accordingly.

AI Licensing: Aligning Price with Value

To address this shift, leading producers are moving toward hybrid AI licensing models that combine subscription foundations with consumption-based pricing.

This approach achieves four critical AI licensing goals:

• Fairness and Transparency: Customers pay for what they use, aligning cost with delivered value.
• Margin Protection: Producers recover variable costs without sacrificing profitability.
• Revenue Predictability: Subscription protects base ARR (Annual Recurring Revenue) while consumption captures upside.
• Flexibility for Innovation: Customers can adopt new AI capabilities without large upfront commitments.

In practice, this often means monetizing AI with prepaid/postpaid consumption layered onto a subscription base.

This approach also opens the door to premium pricing. When producers can tie pricing to measurable outcomes, they create economic value that customers are willing to pay for.

Revenera’s Role in Enabling Value Capture

Revenera helps technology companies implement hybrid AI pricing strategies through a combination of:

• Flexible Monetization Models: Node-locked, floating, metered, and token-based models tailored for AI and SaaS, with seamless hybrid combinations.
• Advanced Analytics for Value Reporting: Producers can measure usage and outcomes, enabling data-driven pricing decisions.
• Dynamic Monetization: Modern platforms designed for connected and regulated environments, supporting elastic access, surge pricing, and diverse deployment scenarios.

See how Revenera’s Dynamic Monetization enables flexible, usage-based AI licensing models in this short video:

The Road Ahead

The future of AI licensing agreements will be defined by strategies that align price with measurable value.

The industry is moving toward hybrid models that layer consumption or outcome-based pricing on top of traditional subscriptions. This evolution isn’t just about covering costs. It’s about capturing the full economic value of innovation.

Producers who embrace this shift will not only protect margins but also strengthen customer relationships by aligning price with measurable success.

Read Now:
AI Monetization, Done Right: A Practical Guide for Product and Pricing Leaders

Smarter AI Licensing Models

If you’re a software producer navigating the complexities of AI monetization, start by asking:

• How much risk are you removing for your customers?
• Can you measure and communicate the value you deliver?
• Are your monetization models flexible enough to adapt to unpredictable costs and dynamic usage?

Those who answer and act on these questions will lead the next era of software monetization and set the blueprint for AI business strategy.

If you’d like more information on how Revenera’s monetization platform enables consumption-based AI licensing models, please contact us today. 

Engineering teams don’t need convincing that billing is hard.

Recently, I read an article from Lago Billing – “Why Billing Systems Are a Nightmare for Engineers”, which clearly explains that what starts as “simple subscription billing” quickly turns into a maze of proration rules, usage calculations, retries, refunds, tax compliance, edge cases, and race conditions. Billing systems live at the intersection of money, contracts, and time, and that alone is enough to keep teams busy for years.

Yet many companies make software billing even harder by asking it to do more than it was ever designed to do.

One of the most common and costly mistakes?
Embedding entitlement management inside the billing system.

Software Billing and Entitlements Solve Different Problems

Let’s be very clear: billing and entitlement management are not the same thing.

Billing answers one question:

How much does the customer owe us, and how do we collect it?

Billing systems are responsible for:

• Pricing calculations
• Invoices and payments
• Subscriptions and renewals
• Usage aggregation for charges
• Taxes, credits, refunds, and revenue recognition

This domain is already complex and highly regulated. Any added responsibility increases risk and slows engineering velocity.

Entitlement management answers a very different question:

What this customer is allowed to use, and how they are actually using it?

Entitlement management focuses on:

• Who can access which products, features, or bundles
• Usage rights across SaaS, on-prem, embedded, and hybrid deployments
• License enforcement and fulfillment
• Tracking real usage against purchased rights
• Providing insight into adoption, expansion, and compliance

Trying to force this logic into a billing system is like asking your payment gateway to manage feature flags and access control. It’s the wrong abstraction, and this is evident over time.

Why Mixing Entitlements into Software Billing Systems Makes Everything Worse

“Billing answers who pays and how.
Entitlements answer who can do what. Mixing them is like asking the cashier to run security.”

When entitlement logic is buried inside billing systems, teams start to experience:

• Tightly coupled code paths where a pricing change can break access control
• Slower releases because billing changes are high-risk
• Poor visibility into real product usage
• Workarounds and spreadsheets to track who is entitled to what
• Billing systems turning into pseudo-licensing engines, which they were never designed to be

The result is not just technical debt, it’s monetization debt. Teams lose the ability to evolve pricing, packaging, and product strategy quickly.

How Pricing, Software Billing, and Entitlements Should Work Together

The right approach is not “either billing or entitlements.”
It’s a clear separation with strong integration.

A healthy model looks like this:

1. Pricing lives in billing
   • Plans, tiers, usage rates, discounts, invoices
2. Entitlements live in an entitlement management system
   • Access rights, feature limits, usage rights, and enforcement
3. Usage data flows from entitlements to billing
   • Accurate, auditable usage signals not raw product noise
4. Analytics inform business decisions
   • Renewals, upsells, churn risk, and packaging strategy

Each system does what it does best, and together they enable modern monetization.

Why Entitlement Management Deserves Its Own System

Modern products are no longer simple. They have various complex requirements to support.

• SaaS + on-prem hybrid deployments
• Usage-based and outcome-based; hybrid pricing
• Feature-level packaging
• Device-based, user-based, and capacity-based models
• Customer self-service expectations, smooth integrations, and activations

Entitlement management becomes the source of truth for customer rights and product usage. It gives product, engineering, sales, and finance teams a shared understanding of:

• What was sold
• What was delivered
• What is actually being used

Without this clarity, monetization becomes guesswork.

How Revenera Gets This Right

Revenera’s entitlement management platform (FlexNet Operations) is built around one core idea:
Don’t overload software billing systems, empower monetization through entitlements.

What makes it effective:

• Designed for SaaS, IDM, and hybrid products
  Supports subscriptions, perpetual licenses, usage-based models, and everything in between.
• Centralized entitlement and usage visibility
  A single source of truth across cloud, on-prem, and embedded environments.
• Usage intelligence, not just enforcement
  Monetization analytics help teams identify adoption gaps, expansion opportunities, and churn risk.
• Clean integration with billing and CRM
  Entitlements inform billing; they don’t compete with it.
• Scales with business complexity
  As pricing models evolve, entitlement logic doesn’t become a bottleneck.

This separation allows companies to move faster, price smarter, and monetize more effectively without turning billing into an unmaintainable monolith.

Final Thought: Complexity Is Inevitable, Confusion Is Optional

Software billing will always be complex.
AI and outcome-based pricing will always add a new nuance.
Hybrid monetization will always evolve.

But confusion comes from poor boundaries, not from complexity itself.

By keeping billing and entitlement management separate and letting each system excel at its purpose, you don’t just reduce engineering pain, you unlock better monetization.

Artificial intelligence is moving at breakneck pace, and software leaders are racing to embed new capabilities into their products. But as innovation accelerates, monetization strategies often lag behind, leading to unpredictable costs, squeezed margins, and value-driven revenue left on the table.

To keep up, your AI business strategy must prioritize:

• Robust entitlement management
• Allocation controls
• Hybrid monetization models

These foundations are non-negotiable, allowing you to scale AI-driven offerings without exposing your company to unnecessary risk.

The Monetization Gap

When monetization is an afterthought to innovation, ROI and long-term growth are undermined, leading to increased C-suite scrutiny of AI initiatives.

Consequences of this misaligned approach include:

• AI features are launched before AI pricing models are defined.
• Revenue forecasts become unreliable as usage grows without controls.
• Subscription models tied to users or seats don’t scale when “agents” or automated processes replace human users.
• Usage-based billing can lead to runaway customer costs if not managed.
• Customers want to pay for value but fear unpredictable bill shock.

How these issues are addressed will shape the success of your AI business strategy. The impact is already evident for many organizations, with 70% of Monetization Monitor respondents saying the cost of delivering AI functionality is destabilizing profitability.

Respondents describe these pressures in their own words:

“We face challenges aligning AI costs with revenue, managing complex pricing, and unifying usage analytics across multiple systems efficiently.” – Engineering/Development Executive, Device/Hardware company with $250m+ in annual revenue

“Key challenges include effectively pricing AI-powered features, optimizing subscription models, and leveraging data for revenue generation while maintaining user satisfaction.” – Operations Director, SaaS/Cloud company with $25m-$100m in annual revenue

“We don’t [currently price our AI offering]. It’s bundled in the core product price as clients expect to have basic capabilities just like reporting.” – Director of Strategy, SaaS/Cloud company with $250m+ in annual revenue

Why Billing Isn’t Enough

Billing is about collecting money; monetization is about aligning pricing with value, usage, and outcomes. Without flexible models and controls, even the best AI features can become a liability.

During her recent AI Monetization Unlocked webinar, IDC’s Tiffany McCormick discussed the confusion around billing for AI usage in the context of hybrid monetization models, stating:

“When vendors are considering these different pricing models, they’re leaning onto their billing companies, not realizing they actually have an entitlement problem. At the point where you decide the threshold of where subscription ends and usage starts, that’s not a billing problem. It’s an entitlement problem.” – Tiffany McCormick, Research Director, AI Monetization, Pricing Strategies and Business Models, IDC

In hybrid subscription and usage-based models, the real challenge isn’t billing – it’s defining and managing who has access to what, when, and under which conditions. Without a centralized entitlement management system, usage data is fragmented across siloed systems, making it difficult to accurately measure consumption, establish meaningful usage baselines, or understand how subscription and usage interact.

Centralizing entitlements creates a single source of truth, enabling reliable software usage tracking, clearer thresholds between subscription and consumption, and a more informed AI pricing strategy over time.

What Your AI Business Strategy Needs

As your AI business strategy moves from experimentation to monetization, it’s essential to build these commercial foundations:

• Hybrid pricing models: Combine subscription, consumption, and outcome-based approaches to match how customers use and benefit from AI.
• Self-service budget controls: Empower customers to set limits, allocate spend by team or product, and avoid surprises.
• Flexible allocation: Support prepaid, postpaid, and mixed models, with the ability to allocate by team, product, or environment.
• Value reporting: Show customers not just what they used, but what they achieved, helping justify spend and build trust.
• Margin protection: Vendors should ensure they can scale AI offerings without sacrificing profitability.

Empowering Hybrid AI Business Strategies

Revenera enables software producers to close the AI monetization gap by:

• Enabling flexible models that monetize not just users, but agents, transactions, and outcomes.
• Providing enterprise-ready solutions for cross-portfolio access, burst capacity, and allocation by team, product, or environment.
• Supporting usage-based pricing that covers variable costs and preserves margins as AI consumption fluctuates.
• Delivering self-service controls and governance for both producers and end customers.

You can learn more about Revenera’s Dynamic Monetization in this short video:

Monetize at the Speed of Innovation

AI is changing the game, and only those who adapt their monetization strategies will win. A practical response starts with the following steps:

• Make monetization and pricing a core discipline in product planning, not just a feature in the backlog .
• Build flexibility and transparency into your models.
• Empower customers and protect your margins.

To discuss how Revenera’s technology can empower your AI business strategy, please contact us today.

How much attention do you pay to product usage data? There’s a wealth of information contained within software usage analytics, but how much time do you commit to reviewing and learning from the insights at your fingertips?

When focused on feature requests and product roadmaps, it’s easy to skip the routine of stepping back and studying customer behavior data, but neglecting this process risks missing upsell opportunities, overlooking churn signals, and failing to spot your next innovation based on real usage insights.

According to the 2026 Monetization Monitor, 41% of software producers believe they currently collect product usage data “very well.” 

While this is a 3% uplift on the previous year, the majority of companies still have room for improvement, with an additional 41% saying they collect data but it requires manual processes or engineering work rather than having an automated solution.

Not having a streamlined process to review product usage data puts you at a disadvantage when it comes to growing annual recurring revenue, especially in an age where usage-based pricing for SaaS and AI is reshaping traditional monetization strategies.

“Balancing innovation with profitability is tricky – subscription fatigue, evolving AI costs, and data-driven pricing all add complexity. Smart analytics and adaptable monetization models are key to staying ahead.”

– Product Management Director at an on-premises software company with $101–$250 million USD annual revenue

Usage Analytics Tooling

Behind the ability to collect and analyze product usage data is appropriate tooling. Organizations may, and often do, use multiple tools. Many (39%) use homegrown software, while the majority (53%) use a commercial usage analytics solution. 

While the organizational needs behind decisions vary, we’re increasingly seeing challenges of homegrown solutions, as they struggle to adapt and scale to the rapidly-changing needs of monetizing AI products and features. 

Today, only 15% of survey respondents say they have no tool for collecting and analyzing usage, which underscores the growing importance of data insights.

However, 30% say they collect telemetry data, but don’t analyze it – letting it go to waste. This is a huge jump from the 11% who collected but failed to analyze data three years ago, so while more organizations have the ability to collect it, many are struggling to put it to good use.

Growth in Usage-Based Monetization

As perpetual licensing gradually fades and suppliers embrace new monetization models, reliable product usage data will be fundamental to those adopting consumption-based licensing, which is expected to grow 56% by 2027.

Naturally, usage analytics is recommended for all software licensing models as the insights can strengthen product development, positioning, and customer relationships, but it’s especially essential for usage-based monetization.

“Balancing fair pricing with maximizing revenue while effectively leveraging AI and analytics remains a key challenge in software monetization.” 

– Engineering and Development Director at a SaaS/Cloud company with $26–$100 million USD annual revenue

Approaches to Collecting Customer Insights

When asked about “highly effective” methods for customer data analysis, sales and support calls are the preferred qualitative approaches, while surveys and advanced product usage analytics are seen as the best quantitative approach.

While direct customer communication has clear benefits, this manual strategy requires a significant time commitment and doesn’t scale with ease. Therefore, having automated quantitative product usage data is key to developing rounded insights that can corroborate or challenge anecdotal exchanges.

Managing Customer Lifecycles

The top use case for collecting product usage data is to identify upsell opportunities, as reported by 70% of respondents. Identifying churn risk and prioritizing product roadmap decisions also scored highly, with each of these playing a vital role in revenue optimization.

Embedded analytics can provide immediate visibility into expansion opportunities, allowing you to engage customers that have high utilization figures or service denials, while also highlighting inactive accounts that require attention as part of retention initiatives.

Customer churn is a major barrier to recurring revenue. Analyzing product usage data is key to reducing churn rates, and although 60% of producers claim to be using data to identify risk, the survey results also suggest the majority could be doing more to proactively stop churn:

• Only 28% use automated notifications triggered by low customer engagement/activity,
• Only 42% review support tickets to spot churn risk,
• Only 47% analyze product usage data to identify decreasing engagement, 
• And, while in the minority, 3% don’t monitor churn risk at all. 

Renewal management is fundamental for successful software companies, but these figures indicate more needs to be done to boost customer retention.

Monetization Analytics

Being able to share product usage data and have open conversations with customers about entitlements/use rights and feature adoption facilitates healthy two-way communication, ensuring customers get the most value from their investment, which improves the likelihood they’ll renew.

In this short video, Vic DeMarines outlines how to grown recurring revenue with Revenera’s Monetization Analytics:

Centralized entitlement management software allows you to track activity across all product lines and deployment models, giving you complete visibility into customer health. Additionally, automated notifications allow you to set custom alerts so Customer Success and Sales teams can take action when churn risk or upsell opportunities emerge.

The Value of Product Usage Data

Product usage data is the key to unlocking future pathways, allowing you to make informed decisions based on facts rather than feelings.

Ultimately, gaining clarity into user activity and environments enables you to establish trends that can lead to better products and happier customers.

Additionally, as suppliers increasingly move toward a SaaS transition with hybrid deployment models and AI pricing strategies, product usage data is integral to modern monetization.

Common Questions

To get the full picture on product usage data trends, please download the 2026 Monetization Monitor and see how your strategy compares to industry peers.

When it comes to monetizing AI, one question keeps resurfacing: should consumption-based models be prepaid or postpaid?

There’s no consensus yet. Most companies are still experimenting, weighing predictability against flexibility as AI consumption reshapes pricing mechanics.

What is clear, however, is that whichever model prevails, accurate, real-time usage data sits at the center of the decision.

AI Changes the Monetization Equation

AI doesn’t behave like traditional software, and neither does its usage.

Tasks once handled by individual users are increasingly executed by AI agents, often operating continuously and in coordination with one another. These agents can invoke services at scale, generating volumes of activity that far exceed human-driven interaction.

That shift matters when monetizing AI. Pricing models built around “per user” assumptions risk underestimating actual consumption while infrastructure costs continue to climb.

Usage-based pricing grounded in real-time data reflects this new reality by tying revenue directly to actual consumption – whether driven by people, agents, or a combination of the two.

Predictability, Flexibility, or Both?

For subscription-led businesses, prepayment offers a familiar advantage: predictable revenue recognized evenly over time. Finance teams often want to preserve this stability when monetizing AI, but consumption-based strategies naturally introduce variability – unless consumption is prepaid and time-bound with a clear expiry date.

Historically, research has shown that customers prefer postpaid agreements – avoiding upfront commitments and paying only after value is realized. For producers, the trade-off is volatility. Revenue becomes closely tied to usage patterns, which can fluctuate significantly.

More recently, however, that preference has begun to shift. Analysts are observing growing acceptance of prepaid consumption models, driven by customer demand for cost control. By committing to a defined budget and placing guardrails around usage, buyers gain predictability – while producers benefit from smoother revenue patterns over a given period.

Part of this shift reflects a reaction to “bill shock,” a common risk in postpaid models when sudden usage spikes translate into unexpectedly high invoices.

Subscriptions, meanwhile, remain foundational. They provide predictable baseline revenue and align with how buyers and finance teams already think about software spend. Increasingly, the future looks less like subscription vs. consumption, and more like a hybrid: subscriptions for core functionality, paired with usage-based pricing for compute-intensive AI capabilities.

Across all models, one requirement remains constant: real-time visibility into usage. Without it, proactive controls, clear communication, and trust quickly break down.

Monetizing AI with Triple-A Data

Real-time usage data gives both producers and customers continuous insight into how AI features are consumed.

For customers, transparency is essential. Seeing usage as it happens – down to which teams, users, or agents are driving consumption – enables internal governance before costs escalate. Without this visibility, usage-based AI billing can feel opaque and risky, particularly in postpaid scenarios.

For producers, the stakes are just as high. New AI features rarely come with reliable usage baselines. Real-time data allows teams to monitor early adoption, detect anomalies, and project trends before those capabilities are fully commercialized. In that sense, usage data becomes more than a billing input – it’s a readiness signal that informs product rollout, customer communication, and profitable AI pricing strategies.

But not all usage data is equal. For consumption billing to be defensible, it must meet three criteria:

• Accessible: Usage data must be readily available to both internal teams and customers.
• Attributable: Consumption should be traceable to a specific action – whether a user request, an AI agent invocation, or an automated background task.
• Auditable: Data must accurately reflect what occurred, giving producers confidence in their invoices and customers clarity on what they’re paying for.

Without these qualities, trust erodes, disputes increase, and confidence is severely undermined.

AI Monetization Architecture Matters

The choice between prepaid and postpaid models has architectural consequences.

Prepaid models rely on upfront credit purchases that are drawn down as usage occurs. While this eliminates surprise bills, it requires strong “showback” capabilities – real-time visibility into how balances are being consumed.

Postpaid models reverse the flow. Usage is captured first and billed later, placing heavy demands on real-time, high-fidelity usage capture. Gaps in accuracy or latency can lead to delayed invoices, disputes, or revenue leakage.

Monetizing AI often combines both approaches. A common pattern is prepaid access up to a defined threshold, with postpaid overages applied when usage exceeds that limit. This hybrid model is typically applied where trust is high or where uninterrupted service is critical, even after prepaid rights are exhausted.

In all cases, best practice is to separate usage capture from billing logic. Usage should be collected, normalized, and enriched in a dedicated layer, then passed downstream to billing systems as needed.

This separation becomes especially important for organizations operating multiple billing, ERP, or CRM systems – often the result of growth through acquisition. A unified usage layer enables consistent AI monetization across the portfolio without first consolidating every downstream platform.

Tying this in with an entitlement management system, which records and tracks all monetization activity – perpetual, subscription, and consumption – creates a single view to make sense of the entire portfolio across the business, as well as at the account level.

Enabling Consumption Models at Scale

This is where Revenera’s Dynamic Monetization fits into the picture.

By delivering scalable, real-time, and auditable usage capture, Dynamic Monetization supports both prepaid and postpaid AI pricing models. It decouples usage measurement from billing systems, operates across complex environments – including connected and air-gapped deployments – and provides the transparency customers increasingly expect.

Crucially, Dynamic Monetization also empowers producers to make changes at speed. Rate tables can be instantly adjusted and new packaging configurations quickly introduced, providing agile flexibility to adapt pricing and monetization strategies as usage patterns and cost structures evolve.

With Dynamic Monetization, usage data is elevated from an operational necessity to a strategic asset. Producers gain the insight required to price AI accurately, customers gain visibility and control, and both sides benefit from a clearer relationship between cost, usage, and value.

As AI consumption accelerates, real-time usage data is no longer optional. It’s the foundation of accurate AI billing, and Dynamic Monetization makes it possible.

Learn more about monetizing AI with Revenera’s technology in this short video:

Once again, we’ve compiled aggregate Revenera Compliance Intelligence data to produce our annual list of the Top 20 Software License Misuse and Piracy Hotspots around the globe.

If you’re not familiar, Compliance Intelligence enables software suppliers to detect, identify and report on the unlicensed use of their applications so they can monetize license overuse and piracy.

These are the top 20 countries using pirated software as of November 2025:

Converting Software Pirates to Paying Customers

As reported in the Monetization Monitor survey of 501 global technology leaders, 31% say software piracy is a major cause of revenue leakage, while 25% state misuse and 23% overuse as major problems.

Overall, the data reveals the vast majority of producers believe piracy, misuse, and overuse are at least moderate problems that impact growth, which has led to many seeking advice on how to stop software piracy and improve compliance.

Regional strategies to generating revenue from the unlicensed use of software yield the best results. Traditionally a softer approach works well in North America and western Europe, but we are increasingly seeing that this approach is also effective in other regions like India (be sure to watch our roundtable discussion on software piracy and license compliance for more details).

Pursuing Software License Compliance Globally

Let’s take a closer look at the top four countries on our list this year.

Software Piracy in China

In our video podcast, Chris Bailey, Principal, Rouse Consultancy in Shanghai and Landy Jiang, Partner, Lusheng Law Firm in Beijing, shared how foreign software companies have been quite successful using China’s courts to achieve win rates of 85.3%. Even with China’s Personal Information Protection Law (PIPL), the use of software telemetry data in China plays an important role in securing evidence preservation orders. The courts in China don’t like lots of procedure and aim for settlements, so it is important to implement best practices including:

• Building a reputation for enforcement and it will make compliance work easier
• Being prepared to use telemetry data and be ready to engage experts or appraisal institutes to explain telemetry data
• Approaching targets professionally but show you are prepared to sue
• Leaving a gap between last negotiation and launching action to keep the element of surprise
• Keeping the door open for face-saving settlement – most cases settle, sometimes just before court hearings

Software Piracy in Russia

I don’t want to ignore Russia but given the current political status and realities it is a more challenging region than in the past. While Russia has seen successful software license compliance results, it is difficult to monetize the unlicensed use of software with the various restrictions on doing business there. I can’t tell you what your legal obligations are but collecting more infringement data is always useful.

Software Piracy in the United States (and Western Europe)

While it may not be surprising to see China and Russia on top of the list, you may be more surprised to see the United States in the #4 position and Germany climb from #11 to #6 on the list. In Germany’s case, the rise is primarily due to increased piracy of engineering simulation and computer-aided design (CAD) software.

However, there is a significant revenue recovery opportunity in these nations, as both have strong IP laws and a track record of successful license compliance programs.

By actively monitoring and addressing unlicensed use, software producers can unlock substantial revenue potential, transforming compliance into a strategic growth lever.

Watch Now:
See how Bentley Systems use Compliance Intelligence to grow revenue

Software Piracy in India

Over the last decade data-driven letter campaigns have been the most common and effective way of pursuing compliance in India to convert infringers into paying customers, but Covid has had an impact on this approach. As a result, a pre-litigation mediation strategy is being used even more often and seeing positive results.

Software suppliers are filing civil complaints in the courts and providing software telemetry data as evidence, which has enabled greater success in attaining injunctions. With an injunction in hand, suppliers have greater leverage and are able to quickly negotiate settlements. This strategy also establishes a strong precedent for a supplier’s reputation on enforcement that puts teeth into future letter campaigns.

Use of Unlicensed Software in Sanctioned Destinations

Iran moves down to #10 this year, which may or may not come as a surprise since Iran is a sanctioned destination. Having access to license infringement data like this makes it easier for software suppliers to track and address these issues, especially in cases where licensed customers have installed software in countries like Iran in violation of their license or export control laws.

Data-Driven Software License Compliance

Armed with actionable compliance data, software suppliers are in a better position to prioritize their efforts to convert overuse and piracy and adjust their regional programs based on the trends that they are seeing.

The bottom line? Compliance Intelligence data has supported customer compliance programs generating more than $4.5 billion in new license revenue since 2010.

This year’s Top 20 shows that the opportunity to generate new license revenue is significant. Want to learn more about your own opportunity? Check out our content on What is Software Piracy? and be sure to download Best Practices for Assessing Your Software Piracy Risk or contact us about monetizing unlicensed use today.

Common Questions

The results are in – and they paint a brutal picture for software producers who need to get on top of their AI pricing strategy.

After surveying 501 product leaders, Revenera’s Monetization Monitor 2026 indicates 70% of those who currently offer AI-driven capabilities are struggling with delivery costs – particularly cloud spend – undermining profitability.

At the same time, Flexera’s IT Priorities Report highlights tension from the buyer’s perspective, with 36% of enterprise IT decision-makers believing they overspend on AI applications – the single biggest area of over-investment.

We’re at a critical juncture, as both buyers and suppliers feel the strain of a financial paradox where each side reports a raw deal, prompting industry analysts to publish advice on choosing new models that reflect value as producers seek improved AI pricing strategies.

AI Pricing Strategy Evolution

During the first wave of innovation, many tech companies introduced AI capabilities to existing products and subscriptions, enabling fast launches and early experimentation.

However, as the landscape matures and operational expenses are factored in – from cloud resources and compute power to data modeling and performance enhancements – there’s been a shift toward usage-based pricing models that ensure overheads are covered while providing more control over consumption.

Although subscription remains the most common framework for AI today, the Monetization Monitor forecasts pure subscription plans to decline by five percentage points over the next 12 months, with a more nuanced AI pricing strategy emerging in the form of blended subscription and usage-based models, which are set to grow by 5%.

In total, usage-based approaches – whether prepaid, post-paid, or combined with subscriptions – are set to make up 62% of all AI product pricing strategies by 2027, marking a shift away from traditional software licensing models.

On the surface, adopting a consumption mindset solves the issue of squeezed margins, as producers can set prices to ensure resource-intensive AI functionality is appropriately charged. Furthermore, as cost is directly linked to usage, customers can measure value more accurately, potentially alleviating concerns around overspending.

However, implementing a usage-based AI pricing strategy requires careful execution, as it carries inherent risk while also generating insights that can unlock new revenue opportunities.

Managing Hybrid AI Pricing Strategies

Much in the same way that bundling high-cost AI features into everyday subscriptions risks eroding profitability, consumption-based licensing creates uncertainty as revenue becomes more unpredictable.

This is why many companies are evaluating hybrid models that combine subscriptions with consumption-based elements, offering core features through annual or monthly plans while providing AI capabilities on a pay-per-use basis.

This layered approach maintains recurring revenue for standard access while mitigating costs for premium functionality.

Benefits aren’t limited to cost recovery, though, as selling units of usage – whether you call them credits, consumptive tokens, API calls, or similar – introduces a flexible revenue stream that scales with demand, making this a commercially viable AI pricing strategy that supports sustainable growth.

The biggest growth lever, however, comes from the rich stream of information captured by usage-based systems, allowing product leaders to conduct meaningful customer data analysis that identifies value drivers, highlights differentiators, and guides roadmap innovation.

Monetizing AI with Data Insights

While there’s widespread urgency to adopt modern AI pricing strategies that align profitability with value – especially amid ongoing media speculation about the durability of AI investment levels – data-driven software producers are looking at the bigger picture: not simply balancing costs, but studying usage to better understand customer outcomes and benefits.

This strategic approach turns raw data into actionable insights, enabling you to:

• Pinpoint high-impact features: Identify which capabilities drive adoption and deliver measurable outcomes – then use those insights to sharpen positioning and lead sales conversations with what truly matters.
• Refine pricing and packaging: Design packages that mirror how customers actually use your product, improving revenue capture as the relationship between pricing and value becomes clearer.
• Strengthen retention: Detect underutilized features, engagement gaps, or stalled adoption patterns and proactively reduce churn with targeted outreach campaigns that foster long-term loyalty.
• Prioritize innovation: Direct roadmap and investment decisions toward areas with the greatest customer impact, ensuring new development reinforces differentiation and keeps your product “sticky.”
• Unlock growth: Reveal upsell and cross-sell opportunities by tracking signals that indicate readiness to buy – such as approaching or exceeding usage thresholds, increased logins across global teams, or activating advanced functionality.

With the ability to capture missed revenue and learn how customers derive value through real-time data, it’s easy to see why usage-based models are becoming a core pricing strategy for AI companies and software producers.

However, as consumption inherently means variable revenue, monetizing AI increasingly involves balancing flexibility with predictability by layering consumption-based components onto subscriptions.

When customer outcomes are clearly understood and continuously optimized, usage-based revenue becomes easier to scale over time, making in-depth monetization analytics a strategic necessity in the age of AI.

Practical AI Pricing Strategy Advice

There’s no denying software pricing models are changing for the AI era and more flexibility is on the way, with à la carte options gaining traction – allowing customers to select precise packaging configurations and only pay for the functionality they need.

As tooling, hosting, storage, and power incurs significant expense for AI products, cost recovery is an important starting point, but the real opportunity lies in monetizing outcomes while the early-stage market is still taking shape.

Revenera’s Dynamic Monetization is designed with flexibility in mind, supporting blended approaches that extend beyond flat-fee subscriptions. In this short video, Jim Berthold demonstrates how Dynamic Monetization enables hybrid AI pricing strategies via consumptive tokens:

As you explore the best AI pricing models for your business, it’s advisable to choose a system that ensures transparency through clear usage reporting that validates spend and has strong guardrails to prevent unexpected charges.

Common Questions

With adjustable rate tables, real-time data, and easy API integrations, Revenera’s Dynamic Monetization provides a flexible platform to scale profitable AI monetization initiatives.

If you’d like expert guidance on implementing your AI pricing strategy and how Dynamic Monetization can help, please contact us to arrange a discovery call.