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Artificial intelligence (AI) is gaining popularity. It provides directions while we drive, answers our questions, makes music recommendations, and powers a growing number of business processes in the workplace.
In fact, AI is working its way into so many aspects of personal and professional life that my company has started calling it “everyday AI”. I suppose it won’t be long before it becomes as ubiquitous – and as necessary – as electricity.
However, despite the advancements, we’ve only scratched the surface of the potential ways AI can, and will certainly change, business and the world. Gartner foretold that it will take until 2025 for half of organizations worldwide to reach what Gartner’s AI maturity model describes as a “stable phase” of AI maturity or beyond.
So there’s still a lot of work to be done to get all parts of AI in place – software, systems, machine learning (ML) algorithms, data pipelines, and administrative controls. As more and more organizations build and scale this type of AI infrastructure, the benefits of everyday WHO – including productivity, efficiency, and data-driven insights – will increasingly reap the rewards for businesses of all sizes.
AI: Electricity development of the 21st century
In many ways, AI is like 19th-century electricity: nascent, promising but unproven, potentially dangerous without safety precautions, and with enormous implications for the environment. how it can transform society.
Some of the fundamental forces of electricity – magnetism, polarity, electric charge – were well understood before Benjamin Franklin, in 1752, flew a kite with a metal key attached to it to prove that lightning was a spontaneous form of electricity. course. But it wasn’t until the 19th century that the emerging field of electrical engineering put the essentials in place to provide electricity to people everywhere, including one of the “direct current” power grids. world’s first in New York City.
Today, you could say that we are in the early stages of the lightbulb or the electric toaster of AI. It is still proof of concept in many places, and AI grids – systems and software that scale AI applications across global businesses and to consumers – are not yet fully deployed.
And yet, many people have had their first experiences using AI, either directly or indirectly. Amazon’s Alexa and Apple’s Siri, those are ubiquitous voice control virtual assistants, are two of the most widely recognized examples, but there are many others as well. Website chatbots, autocorrect text tools, and facial recognition for authentication are popular and easily accessible.
In these everyday situations, we understand that AI algorithms are driving business processes and user experience. We know AI when we see it.
But AI also works in less obvious ways. For example, it is often behind the scenes in loan approval, supply chain optimization, and production automation.
So the line between when AI starts and stops can become blurred. I’ve said this to a lot of data scientists in an exercise I call “Do I do AI?” The idea is to show that we don’t always realize when AI is powering processes like auto-generated articles, which have become ubiquitous in the media.
At the time, I suggested that we use four characteristics – learning, interaction, perception, and goal-seeking – to gauge the relative “AI-ness” of things in each particular case. This is Intelligence aspect of AI and increasingly, it will manifest in more and more of our digital experiences.
But we need to be careful here. As more and more businesses move in this direction, it is essential that we have safety mechanisms in place to avoid the AI equivalent of electric shock. One of the reasons the grid works well, despite occasional power outages, is because circuit breakers and other safety devices are designed to avoid hazards.
AI needs its own grounding bars: Ways to mitigate AI bias, profound tampering, invasion of privacy, and other undesirable consequences. Recently, I came across a report that more than a dozen self-driving vehicles swerved at an intersection in San Francisco, resulting in a two-hour traffic jam. It’s all part of the AI learning curve, so we have to be prepared.
Trust and transparency in everyday AI
The key to long-term success will be establishing trust in AI through transparency. Not only does AI have to work as expected, but we have to be able to demonstrate and even prove it. This is why the concept of AI explainability, a methodology for monitoring and validating AI-driven processes, is so important.
How can business leaders ensure that their organization has the right AI governance framework in place to meet this high threshold? Essentially, governance requires prioritizing and standardizing rules and procedures in AI design and implementation. In addition, it is important to align the results of AI – not only with financial results but with non-financial goals such as sociological and environmental goals.
As AI advances on this path, new use cases will increase and drive increased adoption. Just as demand for electricity is increasing with the advent of light bulbs, toasters, and coffee makers, so AI will be accelerated by the arrival of virtual assistants, autonomous vehicles, and smart homes. .
In most of the world, electricity is now considered a necessity. There is a corollary as AI evolves from pilot projects into enterprise-wide initiatives in the business. There may come a day when we can’t imagine our work and personal lives without the illuminating capabilities of AI.
Florian Douetteau is the CEO of Dataiku.
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