Everyday AI could become as ubiquitous and necessary as electricity

Artificial intelligence (AI) is becoming ubiquitous. It provides directions as we drive, answers our questions, provides music recommendations and drives a growing number of business processes in the workplace.

In fact, AI permeates so many aspects of our personal and professional lives that my company has come to refer to it as “everyday AI.” Soon, I would say, it will become as ubiquitous—and necessary—as electricity.

But despite the progress, we’ve only scratched the surface of the potential ways AI can and undoubtedly will change business and the world. Gartner predicted that it will take until 2025 for half of organizations worldwide to achieve what Gartner’s AI maturity model describes as the “stabilization phase” of AI maturity or beyond.

So there is still a lot of work to do to get all the AI ​​parts in place: the software, systems, machine learning (ML) algorithms, data pipelines, and governance controls. As more organizations build and expand this type of AI infrastructure, the benefits of everyday AI — including productivity, efficiency, and data-driven insights — will increasingly pay off for businesses of all sizes.

AI: The Electrical Evolution of the 21st Century

In many ways, AI is like electricity in the 19th century: nascent, promising but untested, potentially dangerous without safeguards, and with huge implications for how it could potentially transform society.

Some of the underlying forces of electricity — magnetism, polarity, electric charge — were understood long before Benjamin Franklin flew a kite with a metal key attached to it in 1752 to demonstrate that lightning was nature’s form of electricity. But it wasn’t until the 19th century that emerging electrical engineering put in place the necessary parts to make electricity available to people all over the world, including one of the world’s first “DC” power grids in New York City.

Today, you could say we are in the early incandescent or electric toaster stage of AI. It’s still proof of concept in many places, and the AI ​​grid — the systems and software that extend AI applications to global businesses and consumers — has yet to be fully implemented.

And yet many people already have their first experiences with AI, directly or indirectly. Amazon’s Alexa and Apple’s Siri, those ubiquitous voice-activated virtual assistants, are two of the most widely recognized examples, but there are many more. Website chatbots, auto-correct text tools, and facial recognition for authentication are ubiquitous and easily accessible.

In these everyday situations, we understand that AI algorithms are the driving force behind business processes and user experiences. We know AI when we see it.

But AI also works in less obvious ways. It is often behind the scenes in things like loan approvals, supply chain optimization and production automation.

Approach with caution

So the line between when AI starts and stops can get blurry. I once said this to a room full of data scientists in an exercise called ‘Do I Do AI?’ mentioned. The idea was to show that we don’t always recognize when AI powers processes, such as automatically generated news articles, which are now commonplace in the media.

At the time, I suggested using four characteristics — learning, interaction, perception, and goal-seeking — to measure the relative “AI-ness” of things on a case-by-case basis. This is the intelligence aspect of AI and increasingly it will manifest itself in more and more of our digital experiences.

But we have to be careful here. As more companies move in this direction, it is essential that we have the safety mechanisms in place to prevent the AI ​​equivalent of electric shock. One of the reasons the power grid works as well as it does, despite occasional outages, is that circuit breakers and other safety equipment are designed to prevent accidents.

AI needs its own grounding rods: ways to minimize AI bias, deep fakes, privacy breaches, and other unwanted consequences. I recently saw a report that more than a dozen autonomous vehicles were jumbled up at a San Francisco intersection, resulting in a two-hour traffic jam. It’s all part of the AI ​​learning curve, so we need to be prepared.

Trust and transparency in everyday AI

The key to long-term success is establishing trust in AI through transparency. AI must not only behave as expected, but we must also be able to demonstrate and even prove it. Therefore, the concept of AI explainability, a method to track and validate AI-driven processes, is vital.

How can business leaders ensure their organizations have the right AI governance framework in place to meet this high threshold? Essentially, governance requires prioritization and standardization of rules and processes in the design and deployment of AI. In addition, it is important to align AI outcomes not only with financial outcomes, but also with non-financial objectives such as sociological and environmental objectives.

As AI progresses along this path, new use cases will multiply and drive greater adoption. Just as the demand for electricity grew with the advent of light bulbs, toasters and coffee percolators, AI will be accelerated by the rise of virtual assistants, autonomous vehicles and smart homes.

In much of the world, electricity is now considered a necessity. There is a logical consequence as AI progresses from pilot projects to enterprise-wide initiatives in business. There may come a day when we can no longer imagine our work and personal lives without the enlightening possibilities of AI.

Florian Douetteau is CEO of Dataiku.