AI has rapidly moved from a competitive differentiator to a baseline expectation for software companies. However, it has also become the most expensive “free feature” in software history across every segment of the industry. Why is that? Software companies building AI capabilities into their products are spending 12–23% of revenue on the infrastructure and development required to deliver them, yet 71% of vendors with AI functionality still aren’t charging for it, meaning only 29% are getting paid for this investment. This is not the result of a product issue; it is the result of a monetization issue. In fact, I have seen firsthand how companies fold in AI capabilities for free and then struggle to claw back margins.
The truth is, every company building AI software capabilities has struggled to price the intangible values the technology brings, like improved outcomes or better insights. As a result, AI has driven high customer expectations but has also eroded profit margins. We are now entering a phase where AI winners will not be determined by capabilities alone, but by economic design. The challenge for companies now is how best to monetize the technology. However, in my experience, the fastest way to stall AI momentum is to try to monetize too early before customers trust the value. Companies need to ensure customers understand the value so it’s not a friction point.
Within this challenge, though, lies an unprecedented opportunity, because the companies that can solve AI monetization first will have a significant competitive advantage when it comes to premium pricing, building deeper customer relationships, and capturing more revenue. Further, when AI monetization is aligned to trust and measurable value, it stops being a cost center and becomes a growth engine.
In this article, I provide a practical framework for how to turn AI from a cost sink into a scalable revenue driver, whether you’re just starting to build AI into your offerings or integrating it into existing technology platforms. The key is to consistently follow these principles: align pricing with value delivery, match business models to cost structures, and build infrastructure that enables rapid experimentation.
The issue is that per-seat pricing assumes that more users equal more value. AI flips that assumption on its head, meaning the old pricing playbook no longer works.
The AI Cost Structure Revolution
AI has fundamentally broken the economic assumptions that traditional software pricing models were originally built on. Traditional SaaS economics were built on near zero-marginal cost. AI reintroduces marginal cost back into the equation. This is because AI introduces variable cost complexity across inference, infrastructure and operational overhead, along with non-linear scaling where double the usage often requires triple the resources. It also creates new categories of spend, including safety and quality systems, governance and compliance workflows, and the people needed to run them.
This is why per-seat pricing can backfire. If an AI workflow automates work that used to require humans, charging per user can become counterproductive. I’ve seen this strategy happen myself where companies adopt AI to reduce seats and increase automation, while vendors try to monetize by adding seat-based friction back into the experience. Companies need to remember that value should scale with outcomes, not with the number of people clicking into the tool.
In addition, flat-fee structures also aren’t effective because AI value delivery scales with usage. If both value and cost scale with usage, price must also scale with usage. For example, a customer using AI to process 1,000 documents versus 100,000 documents receives a very different value, and flat subscription fees don’t capture the difference.
The main takeaway here is that if AI usage drives value and cost, your monetization model must be designed to scale with both. Monetization must now become part of your core product strategy.
Define AI’s Value in Ways Customers Can Understand
As I mentioned earlier, it’s nearly impossible to quantify the overall abstract benefits of AI. How can you put a price tag on “intelligence”? In the end, customers are focused on buying outcomes that are tangible and measurable, such as time savings, revenue gains, risk reduction, and enhanced satisfaction.
The bridge between AI’s capabilities and business value needs to be measurable and include metrics that your customers can track and finance teams can defend. Examples include API calls, documents processed, minutes analyzed, workflows completed, automations triggered, and tasks resolved that directly correlate to productivity.
As measurement maturity improves, these values will move closer to business outcomes, including qualified leads, successful resolutions, accurate forecasting, risk detection, fraud prevention and reductions in deal-closing time. In the meantime, companies should prioritize selecting value metrics that map directly to customer KPIs and are directly observable within the product. Remember, if your customers can’t see or trust the value they will receive, they certainly won’t pay for it.
One reason companies get stuck is that they assume pricing must be “right” on day one. In reality, effective AI monetization is an evolution.
The Four-Stage AI Monetization Evolution
To reap the benefits of successful AI monetization, companies must first ensure they have the billing infrastructure and technical capabilities in place, including real-time usage processing, multi-dimensional billing at global scale and performance, and the ability to iterate pricing without months of development effort.
Once those are ready, companies must then follow a strategic progression that balances both revenue predictability and value alignment.
Once that foundation is in place, there’s a natural progression to follow – one that balances revenue predictability against how closely price tracks value. That progression tends to move through four stages as AI capabilities mature and costs get easier to pin down.
Stage 1: Subscription Foundation to Establish Revenue Predictability
This works best in three situations: early-stage AI features where the value is still hard to quantify, traditional SaaS companies bolting AI onto an existing product for the first time, and markets where customers want cost predictability above almost anything else.
In practice, that means pricing AI as a monthly subscription and naming exactly what customers get for it – enhanced model access, video generation, whatever the actual feature is – rather than folding it in as a vague upgrade. From there, tiering by AI capability is a natural next step if the product supports it.
The benefit is clear: predictable revenue that is easy to report and a simple customer on-ramp with minimal implementation complexity. The tradeoff is that subscription pricing does not scale with customer value. Light and heavy users pay the same, leaving revenue on the table as usage grows.
Stage 2: Usage-based Transformation to Price to Value
For most companies, this is where AI monetization eventually lands.
In fact, OpenView research reports 61% of SaaS companies adopted usage-based pricing in 2023, up from 27% in 2018.
Usage-based models are a natural match for AI because they scale with consumption. The most visible examples of pure pay-per-use AI are the token-based LLM pricing models employed by companies such as OpenAI for GPT and Anthropic for Claude.
A common practical approach is to use a hybrid model that combines subscription and usage to maximize predictability. With this approach, companies can implement usage charges above included limits, such as $99 a month plus $0.002 per API call above 50,000.
The primary advantages of this model are that customers pay more when they get more value and revenue can grow as AI usage grows over time. Additionally, this model is globally scalable and provides a low barrier to entry for organic customer growth.
Stage 3: Tiered Optimization to Create Customer Segment and Upgrade Paths
Once you understand and implement usage patterns, tiers create clear upgrade paths. This can be volume-based (more usage, better unit economics) and/or feature-based differentiation, such as a standard model with rate limits, a pro model with advanced features, and even an enterprise model that is customized with SLA guarantees.
A prime example of this strategy is Stability AI. The free version is 25 credits a month, the pro version is $10 a month plus usage-based credits, while the enterprise version is custom pricing with volume discounts, depending on the specific needs of any given company.
The benefits of this model are that it has natural upgrade incentives, customer segmentation by value, and volume discounts that can help improve unit economics.
Stage 4: Outcome-based Premium Model That Charges for Business Results
Outcome-based pricing is the most sophisticated AI monetization model because the pricing is based on measurable business outcomes, such as qualified leads generated, ticket resolution time reduction, or costs saved through automation, rather than resource consumption. It requires strong measurement and successful customer partnerships.
A success story example for this model is Talkdesk, an AI-powered customer service platform that charges $2.50 per ticket successfully deflected by AI, which generated $23 million in first-year revenue while reducing customer costs by 34% on average.
However, this model does come with implementation complexity since it requires sophisticated measurement systems and customer success infrastructure that is typically implemented after success with earlier stages.
The strategic power of this model, though, is that it delivers the highest margins through demonstrated value, deeper customer partnerships that are aligned with success metrics and competitive differentiation that is harder to replicate. When implemented successfully, this approach shifts the conversation entirely, because you’re no longer selling AI usage – you’re selling business impact.
AI Monetization and the Path Forward
AI is rewriting the economics of software and is creating the largest platform shift since cloud computing. The companies that will succeed will not necessarily have the best technology, but the smartest business models that help them improve customer retention and achieve higher revenue growth. And, from what I’ve learned, the companies that put in place the best teams that think like architects and work seamlessly together when it comes to product, pricing, and infrastructure are truly unstoppable.
For the best path forward, companies should assess their current state to understand their cost structures and revenue gaps. They should also invest in the infrastructure that will help them to build billing capabilities that enable experimentation and scale, allowing them to pick and choose which models best align with their business needs.
The time is now for companies to act on an AI monetization strategy that will help them capture value rather than limit it.
