I’ve been following the recent discussion around AI pricing with interest. Much of the debate focuses on whether AI should be priced per user, based on usage, according to outcomes, or as a separate product capability altogether.
The topic resonates because I’ve had to make similar decisions across multiple products, with very different customers, cost structures, and usage patterns.
The assumption behind many of these conversations is that outcome pricing is the natural destination. If software can perform work directly, why not charge for the value delivered?
In some cases, that works remarkably well. Customer support is the obvious example. Products like Intercom Fin have demonstrated that customers are willing to pay for outcomes rather than seats or usage when the outcome is sufficiently clear and both parties can agree on what success looks like.
The farther you move from those types of workflows, however, the more complicated the picture becomes.
Consider Excel. It is one of the most successful software products ever created (I may be a little biased) and is used for thousands of different purposes.
If you wanted to implement outcome pricing, what would the outcome even be? Revenue generated, hours saved, better decisions, or errors avoided?
Even if you could measure those things, how much of the outcome should be attributed to Excel itself? A spreadsheet may contribute to a better decision, but so do the employee using it, the quality of the underlying data, the business process surrounding it, and the manager ultimately making the call.
The discussion reminds me a little of activity-based costing from my MBA accounting classes (many years ago now). The basic idea was that traditional allocation methods often didn’t accurately reflect what was actually consuming resources, so companies looked for cost drivers that better represented the underlying activities.
In some ways, AI pricing feels like a modern version of the same search. Seats, tokens, agent runs, resolutions, and outcomes are all attempts to find a unit that better reflects cost, consumption, or value. The challenge is that no single unit captures all three perfectly.
The more I think about it, the more I believe outcome pricing works best when three conditions exist:
- The unit of work is discrete.
- Success can be observed with relatively little disagreement.
- There are few enough other contributors that customers can reasonably attribute the outcome to the software.
Customer support often meets those conditions. Many other categories do not.
The farther a product moves from discrete, observable work with few competing contributors, the harder outcome pricing becomes.
I’ve encountered this problem in various forms across different software businesses.
At Vbrick, we introduced AI capabilities such as transcription, translation, and facial recognition. Because those capabilities had different costs and usage patterns, we ultimately adopted a credit-based approach. Outcome pricing occasionally came up in the discussion, but attributing outcomes such as engagement, attendance, or content consumption to the platform itself proved challenging. The platform clearly influenced those outcomes, but it did not fully control them.
Years later at PerformYard, the challenge looked different but felt familiar. AI capabilities were packaged within a broader Premium offering alongside other advanced functionality and enterprise features. Outcome pricing sounds attractive in performance management software, but employee engagement and performance are influenced by managers, compensation, leadership, culture, and many other factors beyond the software itself.
More recently, while building GeoSheet, I found myself balancing a different set of tradeoffs. The base subscription includes a certain amount of AI usage, while heavier usage consumes additional credits. The goal was to allow customers to experience the value of the capability without requiring a separate purchase decision while still protecting the business from highly variable consumption costs.
One recurring tension across all three examples was adoption versus monetization. Separately pricing AI often made the economics easier to manage, but it also increased the likelihood that customers would never experience the capability in the first place.
These are very different products serving very different markets, yet they all surfaced similar questions. How do you balance customer predictability against variable costs? How do you encourage adoption without giving away unlimited usage? How do you price around outcomes when attribution is unclear?
The answers differed in each case, but none ultimately landed on pure outcome pricing.
At this point, someone will inevitably point to Intercom Fin or similar products. And they should.
Fin is one of the strongest examples of outcome pricing currently in the market. Customers are charged based on successful resolutions rather than seats or usage.
What’s interesting, however, is that Fin doesn’t necessarily solve attribution. Instead, it works because the conditions are favorable. A support interaction is a relatively discrete unit of work. Resolution can usually be observed. And while there are still multiple contributors to success, the number of plausible contributors is much smaller than in many other software categories.
Even then, the definition of success isn’t perfect. What counts as a resolution has required significant effort to define, measure, and validate, and those definitions continue to evolve. The point isn’t that attribution has been solved. It’s that both buyer and seller can agree on a practical proxy that is good enough.
The companies succeeding with outcome pricing are generally not proving causation. They’re finding outcome proxies that both buyer and seller are willing to accept.
The farther the software sits from the ultimate business outcome, the harder it becomes.
Excel struggles because the outcome is rarely discrete and almost always has multiple contributors. Performance management software faces a similar challenge. Even if employee engagement can be measured, managers, compensation, leadership, culture, and career opportunities all contribute to the result.
Video platforms sit somewhere in the middle. Attendance and content consumption can be observed, but the platform is only one factor among the speaker, the content itself, the audience, and the importance of the topic being discussed. The challenge isn’t that the outcomes can’t be measured. It’s that the software is only one contributor to the result.
This experience has made me skeptical of arguments that any single pricing model will eventually dominate.
Per-user pricing remains popular because it is simple and predictable. Consumption pricing better aligns costs with usage, but it introduces budgeting uncertainty. Outcome pricing can work when the conditions are right, but those conditions do not exist everywhere.
There are also adoption considerations. If AI is completely separated from the core product experience, customers may never discover its value. Product teams often want customers to experience new capabilities before asking them to make additional purchasing decisions. That reality has led many companies to include AI capabilities in subscriptions, provide limited usage allowances, bundle AI into broader product tiers, or adopt credit-based approaches that sit somewhere between subscriptions and consumption.
That may be why hybrid pricing models continue to emerge across the industry. Companies are trying to balance customer predictability, vendor economics, variable AI costs, product adoption, and perceived fairness simultaneously. In that context, hybrid pricing starts to look less like a temporary compromise and more like a practical response to competing constraints.
Today’s market encourages companies to make AI highly visible. Customers ask about it. Buyers expect it. Marketing teams highlight it.
At the same time, public attitudes toward AI appear to be becoming more complicated. Concerns about job displacement, layoffs, misinformation, and unrealistic expectations are creating pressure in the opposite direction. As AI becomes more commonplace, customers may become less interested in whether a product uses AI and more interested in whether it solves a meaningful problem.
If that happens, AI may follow the path of many successful technologies before it: becoming part of the product rather than a feature in its own right.
Attribution challenges make pure outcome pricing difficult. The gradual embedding of AI into products may eventually make separate AI pricing difficult as well. Both forces push toward broader packaging and hybrid models.
The current pricing debate often focuses on what should be measured: usage, outcomes, or value.
Those questions matter, but after working through versions of this problem across multiple products, I’ve become more interested in a different one.
Can buyer and seller agree on what success looks like?
In the narrow cases where they can, outcome pricing can work remarkably well. In many others, the challenge isn’t measurement at all. It’s attribution, agreement, and the reality that software is often only one contributor to the result.
That may be why so many companies keep arriving at some variation of hybrid pricing. Not because they’ve failed to find the “right” model, but because they’re trying to balance multiple competing realities at the same time.
It may also explain why, despite very different products, customers, and markets, I ended up wrestling with many of the same tradeoffs each time.