AI Business Strategy: Don’t Let Innovation Outpace 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.