The pricing metric a company chooses, such as per user or per outcome, has a tremendous impact on everything from revenue captured to sales productivity. Yet, it is rarely re-evaluated by CEOs, CFOs, boards, and other executives.
Recent market dynamics, such as inflation, COVID, and most notably, the fast-moving impact of AI, are rendering more and more pricing metrics ineffective or possibly obsolete. To stay competitive, companies must reassess how they charge for value in today’s evolving landscape.
When market disruptions occur, like AI, it is important for companies to re-evaluate and adapt their pricing approach both to accelerate revenue and avoid downside risk.
To do this, companies should gather fresh market intelligence to deeply understand both the relative value of their offering in today’s environment and how market changes, such as AI, will impact their ability to drive revenue growth through pricing going forward.
Key takeaways
- AI is disrupting traditional SaaS pricing metrics—companies relying on per-user pricing may see declining revenue as AI automates tasks.
- No single “best” pricing model exists today—businesses must adapt based on value correlation, market trends, and buyer expectations.
- Hybrid pricing models are gaining traction—combining platform fees with usage or outcomes-based fees can help align pricing with value while accounting for the hard costs of AI (e.g., ChatGPT charges.)
Pricing metric trends
For at least the last ten years, much of the software industry has moved away from user-based pricing for two main reasons:
- customers often reduce users to cut costs and
- there is not always a tight correlation between value and users.
With the rise of cloud computing platforms like AWS and Azure, many software companies moved to consumption-based pricing, which resonated reasonably well with technical buyers, but less so with business buyers because it was not as predictable.
Today, there is no clearly preferred pricing metric for software companies.
How AI is impacting SaaS pricing
A year ago, our research showed that AI was one of the top 5 investments software companies were making, and we know this has accelerated much more. AI is very disruptive to software pricing for several reasons:
- It can lead to lower revenue over time depending on which pricing metric is currently in use
- It has the potential to change buyers’ perception of value
- It may create competitive advantage and pricing power for some and reduce it for others
- There is a significant transactional cost component of AI that software companies have not usually had to factor into pricing.
Companies need to determine how they should charge for AI (e.g., higher platform fee, separate module add-on fee, premium bundle).
To stay competitive, companies must reassess how they charge for value in today’s evolving landscape.
Take CX (customer experience) software, for example, which has historically been priced per-user, but now has AI agents resolving service tickets without any user involvement. Per-user pricing would lead to revenue reduction unless it is now changed to something that correlates more strongly with value, such as outcomes.
To maximize revenue from pricing without incurring higher churn, it is imperative that companies get fresh market insights about buyers’ evolving perception of value and evaluate whether a change in the pricing metric can accelerate revenue while simultaneously reducing risk.
We call this “futureproofing” your pricing strategy. This is best accomplished through interviews with customers and non-customers and buyer surveys. Our recent work has shown a number of key learnings, all of which led to better pricing and accelerated revenue growth:
- New buyers are open to a different pricing metric (versus the industry standard) due to widespread knowledge that AI will render the old metric ineffective.
- There are opportunities for software companies with pricing power or product superiority to lead the industry with a new and better pricing metric and structure.
- Switching from pure usage pricing to a combination of fixed platform fee plus usage or outcomes-based fees captures more direct revenue from AI functionality and reduces the revenue variability from usage.
Good pricing metrics should meet the following criteria:
- Simple to understand and communicate
- Strong correlation with value (both intuitively and based on ‘willingness to pay’ correlation analysis)
- Future proof (organically increasing industry-wide rather than decreasing)
- Easy for customers to forecast
- Easy to track and invoice
Many software companies risk revenue decline by staying with pricing metrics that no longer work in today’s AI-fueled market environment. To stay ahead, companies should evaluate this risk, and through fresh buyer and competitor research, determine whether their current pricing model is maximizing value – or leaving revenue on the table.
AI is very disruptive to software pricing for several reasons.
Successfully navigating these pricing shifts requires a data-driven, strategic approach. At Blue Ridge Partners, we help companies optimize their pricing models based on deep market insights, competitive intelligence, and proven pricing strategies.
Contact Blue Ridge Partners to explore how we can help you future-proof your pricing and accelerate revenue growth.
This article was originally published by Blue Ridge Partners in March 2025.