When you purchase through links in our articles, we may earn a small commission. We'll show you how to get started, what you can do, and how to make ChatGPT work for you instead of the other way round. Hardly anyone can have missed the AI phenomenon that has taken the world by storm. Almost every major company has some kind of AI initiative now. Politicians talk about how important it is not to "fall behind in the AI race," and hundreds of millions have started using AI chatbots. The AI wave took off when OpenAI released its chatbot ChatGPT, which gives large language models a conversational interface.

One day last spring, in a high school classroom in Texas, students were arguing about who to kill off first. It was a thought experiment with a sci-fi premise: A global zombie outbreak has decimated major cities. One hundred frozen embryos meant to reboot humanity are safe in a bomb shelter, but the intended adult caretakers never made it. Instead, 12 random civilians stumbled in. The students had to decide who would die and who would live to raise the future of the human race.

Brynjolfsson, along with researchers Danielle Li, and Lindsey Raymond, authored a study in which generative AI was used by over 5,000 customer support agents at a call center, and found that AI tools boosted workers productivity, reduced attrition, and were especially helpful for early-career workers. Through machine learning, the generative AI systems were able to use pattern recognition to identify successes and failures in customer service approaches. "It listened in on a whole bunch of transcripts and calls, and could see the patterns that turned out well the ones that didn't turn out well," says Brynjolfsson. "It captured that tacit knowledge and passed it on to the less experienced workers." Brynjolfsson said the AI system was able to recommend specific features to solve a customer's problems, or a tone of voice or phrasing that might work better. "Maybe no human had ever written down those rules before but the AI system, by looking at literally millions of transcripts, was able to pick up on these patterns." AI tools are likely going to impact tasks that are "routine, predictable, or standardized," according to Tomas Chamorro-Premuzic, a professor of business psychology and author of I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique. Though it might be tempting to brush off the sudden rise of AI tools as just a fad, Chamorro-Premuzic says it's important to become as familiar as possible with the tools, as they are likely to become ubiquitous. "These are tools that everybody will use, and if you're the only person not even trying it out or not using it, you might actually suffer," he says, comparing such resistance to deciding not to use Google's search engine.

Let's face it: AI investments are just that -- investments, where substantial costs in people, infrastructure, data (collection, hygiene), processes and tools are needed to realize the benefits of the AI deployment, be they efficiency, cost reduction, or revenue and growth. Universally, AI is recognized as a complex and often costly undertaking. For those reasons, AI is considered, but often not adopted, as a key lever in larger business transformation programs. Gartner, McKinsey and others have found that anywhere between 40% to 50% of enterprises planned to deploy or have deployed AI technologies by the end of 2020. This AI adoption curve includes companies undertaking "proofs of concept" as well as those deploying AI on use cases "at scale."

The news about artificial intelligence is mostly dominated by sensational stories such as the ominous threat of deepfakes, deep learning algorithms that create fake blogs, AI bots that create their own language, and generative adversarial networks that create realistic portraits of non-existent people. But the practical use of AI algorithms is much farther behind than the hype caused by the media. From peer-reviewed breakthrough research presented at mainstream AI conferences to PR-style videos created by large tech companies and well-funded research labs, only a trickle of the innovation we see in the field makes it into real business processes and applications. And the organizations that are putting AI to good use are those who understand the powers and limits of today's technology and master the challenges of integrating it into their processes and solutions. "AI does offer a lot of business value, but much of that value isn't terribly sexy or visible. Products and processes will be made somewhat better and easier to use. Decisions will be better informed. We'll continue -- and perhaps even accelerate a bit -- the amazing progress that we've seen over the last couple of decades in data and analytics. But as all of the early adopters have discovered, it's still difficult to create systems that think and communicate like humans -- even in narrow domains," academic and business author Thomas H. Davenport writes in his book The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.

Many companies are stuck in dead-end pilots while an elite few have figured out how to make artificial intelligence (AI) work at scale, according to a new Accenture report. "AI: Built to Scale" shows how difficult this transformation is as well as what it takes to do it successfully. "In a nutshell, what our report found is that the majority of companies are really struggling to scale AI," said Bob Berkey, MD, Accenture Applied Intelligence. "They're stuck in the Proof of Concept Factory, conducting AI experiments and pilots but achieving a low scaling success rate and a low return on their AI investments." Accenture surveyed 1,500 C-level executives across 16 industries to determine what makes AI projects successful.

There is a scene in Steven Spielberg's A.I. Artificial Intelligence where the robot character played by Haley Joel Osment meets the other versions of himself. This robot who wants nothing more than to be human is infuriated. He is the real David. These others are just imposters. To see how quickly, and how many, replicas can be made is terrifying to him.

There is a scene in Steven Spielberg's A.I. Artificial Intelligence where the … the amount of data, automation and machine learning in regards to both …

AI has developed from the early years of pure research to something we will take for granted as part of our daily business and home lives. Three big trends drive the adoption of AI. Let's take a brief look at each of them and what they might mean for your organization today, tomorrow and in the future. Each of these alone would not be enough to bring about "the life of AI." It is a chain of technology that together brings about the perfect ecosystem to deliver the AI storm.

AI. It's a term that used to conjure up images from science fiction movies – the ones filled with machines that could think and act with human-like intelligence. In fact, for most of the last 40 or so years AI has been closely linked with things like the Turing test, that famous assessment for measuring a machine's ability to exhibit intelligent behavior equivalent to or indistinguishable from that of a human. Oh, how times have changed. That once-prestigious definition is long gone. AI is now nothing more than a marketing term used for any software application displaying even the most rudimentary intelligence.