As artificial intelligence continues to disrupt industries, the traditional SaaS pricing model based on a per-seat or per-user approach is quickly becoming obsolete.
With AI-based tools now capable of perform tasks previously performed by humans, fewer “seats” are needed, making the per-station model less relevant.
Salesforce 1 has already taken steps in this direction, including: proclaims the recent Agentforce platformwhich can charge $2 per call, not per user or location.
This marks a shift towards value-based pricing, where firms pay for actual results somewhat than access to the platform. Other SaaS firms might want to adapt to changes driven by AI and automation to stay competitive.
Here are some points to think about.
Adopt performance-based or value-based pricing
As AI increases efficiency, SaaS firms should move away from charging based on the number of users and as an alternative charge for the value an AI-powered product delivers.
Whether it’s the number of transactions, conversations or customer interactions powered by AI, this model ensures that firms only pay for the value they get.
This approach not only makes pricing more scalable, but also aligns with growing expectations that firms should pay for results, not only access.
Moving to usage-based pricing requires firms to rethink their pricing strategies from the ground up. For example, SaaS providers offering customer support tools can charge per interaction or ticket resolved by AI, ensuring that firms only pay for the service they actively use.
This helps SaaS providers attract a broader customer base because smaller firms can access premium tools without having to pay for unused storage.
Offer AI-powered levels or hybrid models
With fewer human positions needed because of AI, SaaS providers can implement tiered pricing based on the balance between human and AI tasks. Companies could bill for highly automated, AI-based processes in one way and for solutions that require human involvement in a different way.
For example, an HR software provider may offer one price for fully AI-powered recruitment processes, while human-assisted verification could also be priced in another way.
This model gives firms the flexibility to decide on the right combination of artificial intelligence and human involvement depending on their needs and budget.
Some may care more about volumes that AI can do higher, and some may value quality and human interaction with fewer volumes.
By adopting this model, SaaS providers can enable customers to scale usage in keeping with specific business needs, paying more as they leverage AI tools to extend efficiency. This gives enterprises a clear incentive to adopt more AI capabilities, while offering flexibility for those that are not yet able to take the step.
Reframe value propositions to spotlight the strategic advantages of AI
SaaS firms must move beyond price adjustments and redefine their value proposition to spotlight the strategic advantages their AI solutions deliver.
For example, AI enables predictive insights, personalized recommendations, and proactive decision-making that deliver real competitive advantage.
A cybersecurity platform can highlight how AI improves threat detection, while a marketing tool can focus on how AI optimizes campaigns in real time.
By positioning AI as a business enabler that drives success, SaaS firms might help their customers succeed and gain greater value, thereby building deeper customer relationships and creating stronger monetization strategies.
Itai Sagie is a strategic advisor to technology firms and investors, specializing in strategy, development and mergers and acquisitions, a guest contributor to Crunchbase News and an experienced lecturer. You can connect with him on LinkedIn for further observations and discussions.