Monetizing AI for Growth
AI is accelerating innovation across the Tech industry and for us, at Pricing Shastra, many of our conversations with leaders of fast-growing tech companies focus around the best ways to monetize AI driven innovation.
While the spotlight so far has been on the Foundational Models, AI is also powering the applications that are building on top of the Foundational Models and requires a revaluation on everything from how AI delivers value, how it fits in with pricing structure, most effective way to monetize etc.
There are two key assessments needed to define your AI monetization strategy -
What is the impact of AI on your pricing model?
How much incremental value does AI offer to customers?
Let us discuss both of these here.
Impact of AI on Pricing Model and choosing the right model
Many Gen AI products seem to be choosing consumption based pricing models because LLMs are priced on token-based models. You do not always have to choose a consumption based pricing model for your product just because LLMs are priced that way. It depends a lot on the value of your product.
We also see many providers who believe including AI in their highest priced plans is a good way to monetize. That in our view is also an extremely simplistic way to approach AI monetization.
Here are our thoughts on when a rethink of the existing monetization model may be needed vs. when a simpler approach is more appropriate.
Rethink pricing model or Monetize incrementally?
AI can significantly influence product usage patterns - mostly because of elimination/ automation of activities. From a pricing perspective, a key question you need to ask is “How does it impact monetization based on my current model?”. Let’s look at it for the two broad monetization models in use - User based and Usage based.
User based pricing model
In user based pricing models, user count can go down with the introduction of AI. Gen AI products generally result in increased productivity, which could result in workforce reduction and fewer addressable users. In this case, you need to rethink your pricing model and meters. Example: Agent based subscriptions for Service Management software such as Zendesk, Salesforce, ServiceNow etc.
If however, there isn’t any significant impact to User counts because of AI, you can continue the existing model and monetize AI incrementally. Example: Office 365 subscriptions and the introduction of Office CoPilot (priced as an add-on but not changing base model).
Usage based pricing model
If you currently have a usage based pricing model, usage may go down with the introduction of AI. Example: For case based pricing for Service Management, AI can deflect case creation and reduce the number of cases. In this case, you need to rethink if the usage meter is still the right one or if additional meters need to be introduced.
If usage is not expected to change drastically, you can introduce AI as an additional charge or might just monetize because of additional volume generated.
Value delivered from AI and impact on PnP strategy
We think of AI value along three dimensions -
Productivity improvements: This is the most common benefit that AI provides. Co-pilots that reduce effort tremendously - whether that is to generate code, to create a slide or to create a summary from a document. May not always result in headcount savings but results in increased productivity by enabling focus on high-value tasks - faster time to output, faster decision making.
Tech stack consolidation and spend reduction: Quite often, AI and automation in one product starts making an adjacent product less relevant. For e.g. one of our conversations was with a company building products to automate Sales and BDR activities. It is not hard to extrapolate that the more Sales and BDR activities are automated using AI, the less relevant marketing automation (which is an upstream activity) becomes. So, a strong case starts building up to save from consolidation across the Tech stack.
Personnel reduction: AI-driven automation in tasks can result in cost savings through personnel reduction. For e.g., headcount reductions through automation in Customer Service, IT service functions. Some vendors in this space (Salesforce, ServiceNow, Zendesk, Moveworks etc.)
Magnitude of value delivered generally keeps increasing from productivity improvements to personnel/HC reduction.
How does value delivered from AI impact monetization strategy?
There are explicit and implicit ways to monetize any technology/innovation. Implicit monetization is when it is included in higher tiers of the pricing lineup to drive upgrades. Explicit monetization is when an innovation is priced as an add-on or included in a higher tier and price is increased.
Depending on the AI value drivers discussed above, you need to fundamentally assess the impact of AI and identify whether an implicit or explicit monetization is more appropriate.
