Some might profess disappointment with Apple’s “Apple Intelligence” features including AI-assisted Siri, Writing Tools and Image Playground, as none arguably introduce new “killer app” features and mostly make existing use cases “smarter.” But that is likely to be the case for most AI implementations for a while.
But there's a strong argument to be made that artificial intelligence will be deployed and experienced by most people primarily through their phones and internet experiences; primarily as enhancements or extensions of existing features.
Most people already have experienced AI when issuing voice commands to their devices; shopping; using social media; smartphone cameras or any form of content recommendation. AI will be applied more often and more extensively for such use cases.
As most people experience the internet using their phones, so most people are likely to frequently use AI-assisted phone experiences. And it is perhaps pointless to compare the value or importance of work and consumer AI experiences.
It is hard to compare the “impact” of AI in consumer smartphone and internet interactions, compared to AI use in business or work applications, in part because the “value proposition" is different. “Fun” or “enjoyment” or “convenience” is often the expected outcome of a consumer AI use case, where “productivity” is typically the desired work outcome.
The sheer ubiquity of the smartphone means it will be the most-common framework for AI encounters by consumers. Compared to other potential platforms for AI, like smart cars or refrigerators, smartphones are nearly universally carried and used. The constant accessibility makes them a prime real estate for AI.
Also, AI on phones is already pervasive. Many features we take for granted, like facial recognition device unlocking, spam filtering, and voice assistants, are powered by AI, and it seems reasonable to suggest that AI acting as a personal assistant will eventually be a major use case.
With the caveat that individuals will vary in their usage of various apps, potential use of AI-assisted experiences is significant.
Also, software interactions that are potentially AI-assisted are frequent for office or knowledge workers; less often the case for workers in some industries such as construction, agriculture or manufacturing.
Also, interactions with software in many industries likely includes significant interaction with machines that might incorporate software and AI (cash registers, scanners, other machinery) rather than productivity apps, content or documents.
In the construction industry, some workers may spend one to three hours daily using project management software, computer-aided design tools, and productivity suites for documentation and communication.
At least some agriculture workers typically interact with farm management software, GPS/GIS mapping tools, and data analytics platforms for one to two hours per day.
Manufacturing employees often monitor or interact with computer-aided manufacturing software, enterprise resource planning systems, and productivity tools, accounting for two to four hours of daily usage.
Educators, including teachers and administrators, might use software, productivity suites and so forth when planning lessons, grading papers or tests or doing other support work, with an estimated four to six hours of daily usage, assuming roughly half the day is spent in actual instruction.
In the retail industry, workers interact with point-of-sale (POS) systems, inventory management software, and productivity tools for three to five hours per day.
Finance professionals, such as bankers, accountants, and analysts, spend a significant portion of their day using financial software, data analysis tools, and productivity suites.
But the point is that perceptions of Apple Intelligence as “underwhelming,” compared to the potential of artificial general intelligence, for example, miss the point. Most AI implementations will make existing experiences more useful, more fun, more entertaining or more productive, but perhaps not so noticeably at first.
Over time, more-developed capabilities will emerge, often in the form of fully-autonomous apps, actions or machines. But that is a way off. Right now, most AI implementations will “make things better” by making them more predictive, more accurate or faster.
The big leaps will come later.
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