Revenera’s annual industry report on software monetization models and strategies is essential reading for business leaders looking to navigate the complexities of AI pricing, customer adoption, and profitability.
By surveying 415 senior executives at global technology companies, we’ve compiled a rich dataset that sheds light on industry trends, best practices, and common roadblocks to revenue growth.
Big talking points from this year’s Monetization Monitor include:
- 96% of respondents now offer AI-driven capabilities – up from 80% last year – but almost half (48%) say customer adoption is limited or low.
-
Offering AI is driving revenue growth for 69% of respondents, with 52% also reporting improved profitability. However, AI delivery costs remain a significant challenge, with 41% reporting squeezed margins and 47% citing rising AI delivery costs as the biggest threat to future growth.
- Amid widespread reports of “bill shock” as organizations overspend on AI budgets, respondents are nearly three times more likely to prioritize predictable pricing for customers over near-term revenue maximization.
- While 45% report downward pricing pressure related to AI, only 7% have experienced seat loss to date.
- Only 18% of producers claim to have an efficient renewal management process, with many lacking the depth of monetization analytics that can highlight churn risk and guide retention.
Please read the full report for more detailed analysis, but some of the key takeaways you should be aware of are outlined below.
Proving the Value of AI
AI has become almost universal across established technology companies, with 96% already offering AI features and the remaining 4% planning to follow within the next 18 months. Predictive and analytical insights lead the way, offered by 70%, followed by workflow automation at 56%, and generative AI at 55%.
Shipping AI capabilities may have been relatively straightforward, but proving their value and getting customers to use them is certainly harder. Only 44% report moderate or high customer adoption of the AI they’ve rolled out.
For product teams, the next step is understanding which capabilities customers value and what will encourage wider adoption. That starts with better visibility into product usage data. Without it, it’s difficult to distinguish features that deliver real value from those that fail to gain traction, let alone determine what customers might be willing to pay for them.
The research highlights this gap, with 49% of respondents citing data availability and insights as a barrier to aligning price with value, and the same proportion struggling to understand customer value drivers.
AI Pricing Isn’t Just About Consumption
Consumption-based pricing often dominates discussions around AI, particularly when delivery costs depend on how much a customer uses a feature. Yet established software producers are taking a more measured approach, with predictability a major consideration when deciding how to charge for AI.
The three most common AI pricing models today are placing AI features in a higher-priced subscription tier (40%), including them in an existing product at no extra cost (39%), and adopting a hybrid model that combines a subscription with usage- or outcome-based charges (37%). These approaches offer different ways to balance customer value, revenue, and delivery costs.
Looking ahead to 2028, 58% plan to expand their use of higher-priced AI tiers, 45% expect to increase the use of no-extra-cost AI, and 42% plan to expand hybrid approaches. Pure usage-based models – whether prepaid or postpaid – are also set for growth, but established producers aren’t rushing to abandon the commercial models they already know.
Read More:
The Product Leader’s Guide to Consumption-Based Monetization
Seat Loss Isn’t the Whole Story
Concerns about AI agents replacing traditional software seats have generated plenty of attention, but the immediate impact seems more limited than headlines imply. Among respondents, just 7% report having sold fewer seats because of AI, although 23% anticipate reduced demand for subscriptions due to competition from AI.
There’s still a meaningful pricing challenge, even without widespread seat loss. Almost half (45%) report downward price pressure related to AI. That includes pressure to offer bigger discounts because customers could build their own AI-powered alternatives, as well as pressure to reduce renewal prices as AI competes for enterprise IT budgets.
For software producers, the commercial impact can arrive through a negotiation over price long before it shows up as a lost subscription. Understanding the value a product delivers is becoming increasingly important when customers question what they’re paying for and where their software budgets should go.
Growth Doesn’t Guarantee Healthier Margins
69% say their AI offerings are growing revenue, and 52% report improved profitability. However, 41% also say the operational costs of providing AI are squeezing margins. Those findings can coexist because generating revenue from AI and making that revenue profitable aren’t the same thing.
Increased AI delivery costs are cited by 47% as the biggest barrier to growing recurring revenue, followed by increased cloud costs at 43%. A further 37% point to the inability to implement new software monetization models.
Producers need enough flexibility to adjust their commercial approach without creating an operational headache every time the product changes. That can be difficult when 55% say implementing a new monetization model takes longer than six months.
Additionally, pricing decisions can’t be separated from the economics of delivering a product. A model that works for one capability or customer segment may not make sense for another, particularly when usage and delivery costs vary, which is why it’s important to stay on top of modern SaaS KPIs.
Flexible Software Monetization Models
AI is entering a market where established software monetization models are still prominent. Subscription remains the leading model, with 66% saying it generates moderate or significant revenue. Perpetual follows at 44%, while prepaid usage, postpaid usage and outcome-based models each play a role.
Looking ahead, 65% expect subscription revenue to grow as a share of overall revenue by 2028. Usage-based models are growing too, with 45% anticipating growth in prepaid usage and 39% in postpaid usage. The likely direction is a broader mix of models, not a wholesale replacement of subscriptions.
The challenge is making that flexibility work across established portfolios. More than half of respondents use fragmented monetization tools, while complex or manual workflows remain a major quote-to-cash obstacle. New pricing approaches are harder to scale when the underlying systems and processes can’t keep pace.
These operational challenges can also affect customer retention. Coordinating efforts across teams is the biggest renewal challenge for 40% of respondents. Without clear visibility into product usage and customer activity, teams can struggle to spot early warning signs and act before an account is at risk.
Better data and more automated workflows can help producers identify renewal risks sooner, coordinate their response and address potential churn before it’s too late.
Evolving Software Monetization Strategies
According to Greek philosophy, “Change is the only constant in life,” and this surely rings true when it comes to evaluating your software monetization strategy, especially as you consider new initiatives such as how to monetize AI.
AI is accelerating the need for greater flexibility in how software is packaged, priced, delivered, and managed. With customer expectations evolving and delivery costs putting pressure on margins, producers need to understand how customers use their products, which capabilities drive engagement and where they find value. These insights are essential to making informed pricing decisions that support adoption and sustainable growth.
Building these commercial foundations requires effective tools and processes. A specialized software monetization platform can give producers greater visibility into software usage and entitlements, alongside the flexibility to adapt pricing and packaging as market conditions change.
It can also help reduce time-to-market for new releases and monetization models, keeping commercial strategies in step with product innovation.
Ultimately, successful software monetization is an ongoing process of learning, adapting and refining. Producers that can turn customer usage insights into practical changes across their portfolios will be better positioned to capture value from AI, protect profitability, and respond to whatever comes next.
Common Questions
What are the biggest challenges to growing annual recurring revenue (ARR)?
The report highlights increased AI delivery costs, increased cloud costs, and an inability to implement new monetization models are the biggest blockers to ARR growth. Inefficient renewal management and a lack of deep revenue analytics are also major obstacles that contribute to churn risk.
Which software monetization models are gaining momentum?
Subscription licensing remains the most common model, but usage-based pricing is growing. Blended models that combine subscriptions with consumption components are becoming more popular, especially for AI products, while perpetual licensing and pure on-prem deployments are still holding relatively strong.
How are companies addressing hybrid monetization challenges?
Centralized entitlement management solutions enable support for multiple deployment models, improve compliance, and simplify the quote-to-cash process, reducing complexity and accelerating time-to-market.
For deeper insights, be sure to read the full Monetization Monitor report and please contact us if you’d like to schedule a call to see how Revernera’s state-of-the-art solutions can accelerate your goals.
