One recurrent problem with revenue forecasts for all new technologies expected to be widely embedded in most existing products is how to isolate the specific contributions made by the one new technology, compared to all the other processes, features, packaging, distribution and manufacturing changes that might also have occurred simultaneously.
Consider some studies of revenue upside from artificial intelligence for a variety of use cases and products, including virtual assistants, self-driving vehicles, smart factories, health care, fraud prevention, marketing, education or content creation. The numbers are big.
Some of that revenue will be earned by suppliers of computing infrastructure necessary to support widespread AI operations; operators of cloud computing services or suppliers of software, training and advice to implement AI is specific business functions. That might be the most-clear and most-direct AI contributions to firm and industry revenue.
That will be easier to measure than revenue upside for all other industries that might use AI in some way.
Obviously many of those products have existing customers, markets, business models and revenue. AI is expected to enable lower costs, add features to existing products and might also allow creation of some new products.
In many cases--probably most--AI impact might be indirect. Often AI features will be embedded in products with a revenue impact that is measured by new account additions, product model or version upgrades, lower churn or some other indirect outcome.