AI Pricing: AI monetization in 2024 survey
Steven Forth is a Managing Partner at Ibbaka. See his Skill Profile on Ibbaka Talio.
Large amounts of money have been invested in Artificial Intelligence, especially generative AI, and there have been many AI and AI-enabled product launches.
How will companies monetize their investments in AI innovation?
What packaging patterns are most common for AI and AI-enabled products?
How is pricing being designed?
What pricing metrics are being used?
What is the impact on revenue growth?
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There are several ways to think about AI monetization.
Value Lens
Market Lens
Packaging Lens
Value Lens on AI Monetization
One way to approach AI monetization is to begin with value creation.
Who does the AI create value for?
How does the AI create value?
What impact does the AI have on the market?
Answering these questions provides the key insights needed for pricing, a necessary step in AI monetization.
Basically, this is asking does the AI:
Create new value drivers?
Or does it
Enhance existing value drivers?
And does it do this for
Current Customers?
Or for
New Customers?
Putting this together gives the following 2x2 matrix.
Deciding where the AI solution lies in this matrix is a critical where to play choice (in Roger Martin’s Strategic Choice Making framework).
Market Lens on AI Monetization
Another way to think about AI monetization is with the traditional market innovation grid.
Better serve existing customers
Is the goal of the AI investment to serve current customers with the current product better, and to grow market share?
Or is a new product being created for current customers, letting one grow a share of wallet?
Find new customers
For some companies augmenting their current products with AI will make it possible to target new customers and expand their current market.
The most ambitious monetization strategy is to create a new product for new customers and a new market.
Packaging Lens on AI Monetization
Note: The original idea for modular operators comes from Design Rules: The Power of Modularity by Carliss Baldwin and Kim Clark.
One of the most interesting possibilities for AI is to repackage. There are many ways to combine data and functionality to create packages to price and sell. Some companies are taking advantage of AI-driven innovation to repackage all of their functionality. One emerging approach is to move to a Platform + Extensions pattern with AI as the underlying platform. There is even talk of an AI-based operating system and an operating system for AI.
All 8 modular operators can be seen in packaging new AI functionality.
