It’s that time of year again, when people publish their top-10 or top-20 lists of what lies in the year ahead. Rather than pile on with another list, I’m limiting my contribution to one compelling (or half-baked) trend for the year ahead.
So, here we go: In the year ahead, artificial intelligence will become ever more pervasive. So pervasive, in fact, that it will start to become invisible — to actually begin to vanish, from the hype cycles, even from our consciousness.
Only 4% of businesses currently use AI. In the future, it may be invisible to users, an analysis from NBC shows. “Few businesses will work with AI directly. Instead, they’ll use applications built on top of AI that look familiar and accessible.”
Consider the words of Lorissa Horton, senior VP and general manager of Webex: ”AI is embedded in every part of Webex.” In other words, powerful but invisible, behind the scenes.
In other words, awareness and hype around AI will follow the path of other foundational technologies. Consider how often we talk about desktop operating systems, or even cloud for that matter. In the year ahead, AI will also start to melt behind the scenes.
Wait, correction — AI is already melting behind the scenes, a view shared by many industry leaders canvassed for this article. “Invisible AI is not the future, it’s the present,” says Marinela Profi, AI strategy advisor for SAS. “AI functions are so well integrated that they become normal, unremarkable parts of a user’s interaction with the technology. It’s already part of many aspects of our daily lives working behind the scenes in ways that we might not immediately recognize — email spam filters, streaming service platform recommendations, smartphone predictive text and autocorrect, credit scoring, banking fraud detection, personalized advertising, and smart home devices.”
Within enterprise walls, “AI is already being embedded into our core technologies and that is only accelerating,” says Mike Haney, chief information officer at Battelle Memorial Institute. “I expect AI to be woven into the very fabric of every organization and become a foundational component of every piece of technology that we’d consider adopting.”
Similarly, “many of today’s products will become invisibly smarter due to AI,” says Or Hiltch, chief data and AI architect for JLL. “In commercial buildings, for example, AI-controlled and monitored carbon emission will become native to all parts of the building – HVAC, water heating systems, and electrical usage. In labs, restaurants and factories, predictive maintenance will become the standard, not the standout. Systems that currently require burdensome attention will eventually be discovered and governed by AI systems automatically.”
“It’s hard to imagine generative AI ever falling far from top-of-mind,” says Dr. Scott Zoldi, chief analytics officer at FICO. “But that is what happens with all technologies as they move, with almost clockwork reliability, through their hype cycle.”
While its impact has been substantial, “generative AI will follow in the hype cycle footsteps of other breakthrough technologies like blockchain,” Zoldi continues. “At their outset, both technologies appeared to be powerful novelties with great but unknown potential. As blockchain has matured and been applied in extremely useful ways beyond cryptocurrency – such as for model management governance. Gen AI will find similar tributary applications that will be less dramatic, but far more pragmatic.”
This also means an “AI paradox” is evolving, says Jean-Matthieu Schertzer, chief AI officer of Untie Nots, part of the Eagle Eye Group. “As soon as it works, no one calls it AI anymore. For example, Object detection on ImageNet was a major breakthrough in deep learning in 2012 and is now on every smartphone. No longer AI.”
The AI-charged smartphone is perhaps the most tangible example of AI no longer being AI. Consider the “classic usage of non-intrusive, under-the-cover AI in the Apple iPhone opening with Face ID,” says Andy Thurai, principal analyst with Constellation Research. The product employs “underlying technology using a complex neural network and running on a neural engine with a dedicated neural network that runs on an Apple A11 Bionic Chip.”
And guess what? Apple rarely ever mentions AI.
Such will be the case going beyond smartphones going forward. “AI is immensely powerful today but will not reach its true potential until it is fully integrated into the applications people use every day,” says Greg Pavlik, senior vice president of AI and data management services at Oracle. “Ironically, it will hit its full stride only when it disappears into the software and tasks that run our businesses.”
We’re also seeing “invisible” AI embedded in another pervasive business technology — email systems. “Features like spam filtering, classification and smart categorization of emails, quick replies, and help with creating replies based on context are some examples of increasing productivity,” says Thurai. Another use case is Netflix and Amazon’s “recommendation based on the user persona and watch history is based on machine-learning models.”
In our workplaces, “we already use many tools that leverage machine learning and AI capabilities without thinking about it,” says David Seidel, vice president for information technology and CIO at Miami University.
“AI will be both invisible because it is baked into things all around us and in our daily lives, and highly visible via marketing and advertising as part of the hype cycle,” Seidel says. “We already know it’s helping with responses as a writing and ideation partner, so the open question is how we get to verifiable knowledge and trustworthy answers, and how quickly that happens.”
Key to AI’s invisibility will be the ease with which end-users will be able to employ it in their day-to-day projects. “The technology needs to work efficiently and effectively without requiring the user to understand or interact with the AI component directly,” says Profi.
“Recent Generative AI capabilities are revolutionizing UX with automated systems. And people don’t talk a lot about a good UX; they just adopt it and use it,” says Schertzer. “Large language models are being used in retrieval-assisted generation systems, which makes it more efficient to explore a large document database — already used by some VC funds for their due diligence with companies. In this context, a good AI system is just a UI that works, a UX that helps be more effective.”