Managing and monetizing SaaS’s delicate AI transition

When introducing new AI tools and the pricing models that go with them, software companies face an inherent tension: monetize too early and you risk slowing customer adoption, wait too long and risk not capturing value and exposing yourself to additional costs.

This article draws on proprietary research conducted over the past 12 months, including interviews with approximately 100 software providers. Across the research, a consistent challenge emerged: how can vendors adapt their proposition with the pace of technological change, while continually aligning pricing and packaging.

AI is already reshaping the economics of software-as-a-service (SaaS); as AI-powered software becomes more productive, traditional seat-based pricing becomes harder to sustain. Providers face growing pressure to align pricing more closely to delivered value, while managing variable costs and educating customers on new commercial models.

Why this matters now

AI-driven productivity gains are already reducing the need for seats in some workflows, while hyperscalers and new entrants are reshaping cost structures. At the same time, public market volatility – the so-called 2026 “SaaSpocalypse” – has sharpened investor focus on durable growth and monetization. For management teams and sponsors, the question is no longer whether pricing will evolve, but how quickly and sustainably it can adapt.

Adoption is already well underway. In our interviews we found that 70% of customers of SaaS providers are either actively using or testing AI capabilities – split between 38% already subscribing and 32% still piloting, with maturity varying significantly by sector and use case.

Most organizations are still in a phase of broad experimentation, with limited visibility on the true cost and return on AI usage. In many cases, access to AI tools is being expanded with few guardrails. As this changes, customers are likely to become more selective, focusing AI on the highest-value use cases and introducing tighter controls on consumption.

This shift will have a direct impact on how AI functionality is packaged and monetized. It also highlights a structural challenge for software providers: pricing models must remain flexible enough to adapt to competitive tension and customer preferences, as well as absorb potential changes in underlying AI costs as the market evolves. 

Balancing monetization and customer adoption

A challenge facing software providers is when to monetize. There is a risk in trying to monetize AI before customers fully recognize its value. Accustomed to the predictability of per-user pricing, customers can resist new pricing models that introduce variable costs.

Crucially, customers are not paying for AI capability in the abstract. They are paying for measurable outcomes – including time savings, risk reduction and the ability to connect data and workflows in more adaptive and scalable ways. That creates a structural misalignment with traditional per-user pricing, which often measures headcount rather than value delivered.

Different pricing models present distinct challenges. Usage-based pricing can be difficult to structure, as consumption is inherently uncertain and some customers are wary of open-ended costs. Outcome-based pricing can be more intuitive but only works where value is clear and directly measurable, such as the number of invoices processed and the associated cost savings.

Not all AI use cases lend themselves equally to pricing. Workflow-led use cases are often easier to quantify and justify, while broader insight-led applications are harder to tie to direct economic value. Customers also face practical challenges around accountability. Who signs off on variable AI spend that is difficult to predict? And how should that spend be prioritized when the return is uneven across use cases?

Our interviews show that while demand for AI is strong, customers resist pricing models that expose them to higher or more variable costs before value is proven. As a result, many organizations start small, piloting AI with limited users before scaling. As one CTO noted, pricing that feels manageable at the single-seat level quickly becomes difficult to justify across larger teams.

The most effective vendors respond by sequencing monetization more carefully: embedding AI at low or no cost to drive adoption, then monetizing more advanced capabilities once usage is established.

What’s working: how leading companies approach AI pricing

What tends to work best in practice is a deliberate approach with five key elements:

1.    Start with adoption, not monetization

• Use base packages with built in AI functionality
• Lower friction to encourage usage, experimentation and learning

2.    Keep the commercial model simple

• Offer customers straightforward structures
• Keep complexity behind the scenes

3.    Link pricing to value when it’s clear

• Consumption-based pricing works where usage is measurable
• Outcome-based pricing works only where value is directly attributable to a task completed

4.    Introduce monetization in phases

• Start with bundled pricing
• Add paid tiers or add-ons as usage grows
• Build expansion paths over time

5.    Align pricing with how customers buy

• Reflect budget cycles
• Adapt to approval processes
• Respect internal ownership of spend

A responsive AI pricing capability

Rather than planning a multi-year pricing roadmap, software companies need to build a more responsive pricing capability, one that continually adapts as AI products and customer expectations evolve.

AI monetization is not a one off transition with a fixed endpoint. Both value and cost remain moving targets, so pricing must evolve alongside them. This requires faster iteration cycles, more frequent reassessment of how value is defined and measured and the ability to adjust pricing and packaging as usage patterns become clearer.

Internally, this has broader implications beyond pricing alone. Metering, cost transparency, sales motions and revenue models all need to continuously evolve as new AI functionality is introduced. The companies that succeed will be those able to recalibrate quickly rather than follow a predefined roadmap.

What this means for decision-makers

Software companies are well placed to turn this threat into a growth opportunity. To do so, decision-makers need to identify where value is created and measurable, put customer adoption first, and monetize that value over time.

Pricing should remain simple for customers, even as internal operations become more complex. Because the shift from seat-based to usage- or outcome-based pricing changes how revenue is generated and measured, companies need to adapt forecasting, sales incentives and performance management long before the new pricing reaches scale. Hybrid pricing models are the dominant approach to manage that transition.

The companies that get this right will help shape how AI value is priced in a fast-evolving software sector. For decision-makers, this is not simply a pricing decision, but a strategic choice about how commercial models, operations, and go-to-market approaches evolve to match the value AI is creating. Those that move thoughtfully should benefit from stronger valuations, whether in public markets or private capital.

We are helping software companies and investors navigate AI-driven changes to pricing and value creation. If you would like to explore what this means for your business, or how to capture value without slowing adoption please get in touch.

--

This article is written by Anthony Crutchett, Partner in our Technology practice and Patric Kirchner, Partner in our Pricing and Commercial Excellence practice. Drawing on our work across software, pricing and value creation, it reflects how we support software companies and investors as AI reshapes commercial models.