Pricing is the
In the age of AI-enabled software, pricing and product are linked; pricing fundamentally impacts usage, which directly informs product quality.
Therefore, pricing models that limit usage, like the predominant per-seat per month structure, limit quality. And thus limit companies.
For the first time in 20 years, there is a compelling argument to make for changing the way that SaaS is priced. For those selling AI-enabled software, it’s time to examine new pricing models. And since AI is currently the best-funded technology in the software industry — by far — pricing could soon be changing at a number of vendors.
Why per-seat pricing needs to die in the age of AI
Per-seat pricing makes AI-based products worse. Traditionally, the functionality of software hasn’t changed with usage. Features are there whether users take advantage of them or not — your CRM doesn’t sprout new bells and whistles when more employees log in; it’s static software. And since it’s priced per-user, a customer incurs more costs with every user for whom it’s licensed.
AI, on the other hand, is dynamic. It learns from every data point it’s fed, and users are its main source of information; usage of the product makes the product itself better. Why, then, should AI software vendors charge per user, when doing so inherently disincentivizes usage? Instead, they should design pricing models that maximize product usage, and therefore, product value.
Per-seat pricing hinders AI-based products from capturing value they create
AI-enabled software promises to make people and businesses far more efficient, transforming every aspect of the enterprise through personalization. Software tailored to the specific needs of the user has been able to command a significant premium relative to generic competitors; for example, Salesforce offers a horizontal CRM that must serve users from Fortune 100s to SMBs across every industry. Veeva, which provides a CRM optimized for the life sciences vertical, commands a subscription price many multiples higher, in large part because it has been tailored to the pharma user’s end needs.
AI-enabled software will be even more tailored to the individual context of each end-user, and thus, should command an even higher price. Relying on per-seat pricing gives buyers an easy point of comparison ($/seat is universalizable) and immediately puts the AI vendor on the defensive. Moving away from per-seat pricing allows the AI vendor to avoid apples-to-apples comparisons and sell their product on its own unique merits. There will be some buyer education required to move to a new model, but the winners in the AI era will use these discussions to better understand and serve their customers.
Per-seat pricing will ultimately cause AI vendors to cannibalize themselves
Probably the most important upsell lever software vendors have traditionally used is tying themselves to the growth of their customers. As their customers grow, the logic goes, so should the vendors’ contract (presumably because the vendor had some part in driving this growth).
Tethering yourself to per-seat pricing will make contract expansion much harder.
However, effective AI-based software makes workers significantly more efficient. As such, seat counts should not need to grow linearly with company growth, as they have in the era of static software. Tethering yourself to per-seat pricing will make contract expansion much harder. Indeed, it could result in a world where the very success of the AI software will entail contract contraction.
How to price software in the age of AI
Here are some key ideas to keep top of mind when thinking about pricing AI software:
- Start by using ROI analysis to figure out how much to charge
This is the same place to start as in static software land. (Check out my primer on this approach here.) Work with customers to quantify the value your software delivers across all dimensions. A good rule of thumb is that you should capture 10-30% of the value you create. In dynamic software land, that value may actually increase over time as the product is used more and the dataset improves. It’s best to calculate ROI after the product gets to initial scale deployment within a company (not at the beginning). It’s also worth recalculating after a year or two of use and potentially adjusting pricing. Tracking traditionally consumer usage metrics like DAU/MAU becomes absolutely critical in enterprise AI, as usage is arguably the core driver of ROI.
While ROI is a good way to determine how much to charge, do not use ROI as the mechanism for how to charge. Tying your pricing model directly to ROI created can cause lots of confusion and anxiety when it comes time to settle up at year-end. This can create issues with establishing causality and sets up an unnecessarily antagonistic dynamic with the customer. Instead, use ROI as a level-setting tool and other mechanisms to determine how to arrive at specific pricing.