Monetizing AI: "Picks and Shovels" or "Personalization"

“Monetization” of investments in artificial intelligence already are a top issue for CxOs who have to authorize the spending. 

So far, the early winners are suppliers of “picks and shovels” such as Nvidia, which supply infrastructure necessary to use AI. 

The tougher issue comes in areas where “personalization” based on “behavioral and data mining” come into play as the value drivers. And that is the likely monetization case for most firms and most people. 

Better “personalization” is likely the key upside for most applications, services and products. Consider the old adage about wasted advertising: “We know half our advertising is wasted; we just don’t know which half.”

As has been the case for data mining so far, so too will AI provide value in terms of surfacing customer behavior and demand in a more precise way. 

All of that assumes data privacy rules do not prevent this, of course, allowing a “social graph” to become something more like a “hyper-personalized” human context. Or call it an:

• Intelligent social graph

• AI-powered social graph

• Semantic social graph

• Contextual social graph

• Cognitive social graph

• Behavioral social graph

• Predictive social graph

The point is that AI extends the capabilities of existing social graphs as they are relevant for advertising, retailing, marketing and other existing operations supporting existing monetization models. 

A retailer could use AI to develop a personalized recommendation engine that suggests products to customers based on their social media interactions, purchase history, and the purchase history of their friends and connections. 

An advertiser could use AI to target ads to customers based on their social graphs and interests. For example, if a customer has recently liked a post about a new restaurant on social media, the advertiser could serve them an ad for that restaurant.

A social media platform could use AI to recommend new people to follow based on a user's interests and social connections.

A job search platform could use AI to match candidates with jobs based on their skills, experience, and social connections. For example, if a candidate has the skills and experience required for a job and their friends are connected to people who work at the company that is hiring for the job, the platform could recommend the job to the candidate.

For any marketing-related or ad-related expenditure on the part of a seller, or the fulfillment operations of a platform, the value will come in the form of an outbound marketing and selling value that has higher conversion rates, even if the services and products bought by the advertiser or marketer cost more. 

More-personalized capabilities will benefit retailers for similar reasons. Knowing the size and shape of any potential customer’s foot, their preferred indoor and outdoor activities, travel preferences and interests, where a person has traveled recently or regularly will allow retailers to more effectively sell footwear, socks and related gear, for example. 

For most CxOs, firms and people. AI will be something like a new adjective modifying an existing noun. New features and capabilities will be used to support existing processes. 

Eventually, some entirely novel use cases could develop, with new business models. It’s just hard to predict what they will be. 

For nearly-all practical purposes, firms will take advantage of their “picks and shovels” value or will apply AI to personalize existing operations in ways that support the existing business model. 

But in all these cases, firms will sell more of something they already supply. In a fewer number of cases the monetization will be quite direct (picks, shovels) but in most cases the benefit will be indirect (better personalization leads to higher sales volume or conversion rates).