It is a reasonable enough assumption that an AI-enhanced Alexa will represent an incremental enhancement rather than a disruptive change, at least at first. And perhaps the same might be said for AI-enhanced search, social media, advertising, e-commerce customer service or virtually any other process that underlines a business model.
In most cases, generative artificial intelligence will be used to upgrade or enhance existing apps and use cases.
Perhaps of more interest are ways GenAI might be used to support entirely new use cases, such as AI-driven or automated art generators, music composers, and story writers. Of course, some might argue that AI-generated art is an extension of existing computer-generated graphics functions.
Generative AI might also be used to accelerate and automate drug discovery by generating new molecular structures and predicting their properties.
GenAI also can be used to generate realistic gaming environments, characters, and storylines.
So far, there is not widespread agreement on whether GenAI can be a platform for entirely-new use cases with new business models.
A related question is whether third-party models or in-house models will drive such developments.
Many use cases already are being built on third-party models. Siri using a platform provided by Anthropic’s Claude provides an example. The use of third-party platforms rather than in-house models is a choice most firms likely will be making, and for reasons similar to their use of any important technology.
Core firm competence almost never lies in the area of operating system, computing appliance or platform development. Also, time-to-market concerns plus performance make “build your own” approaches either too time-consuming or too expensive or both.
Most firms have no interest in building their own chips, operating systems, core apps, computing hardware, networks or AI models. But lots of firms might have interest in customizing existing third party models for a particular industry business process.
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