The History And Evolution Of Artificial Intelligence

So, as a simple example, if an AI designed to recognise images of animals has been trained on images of cats and dogs, you’d assume it’d struggle with horses or elephants. But through zero-shot learning, it can use what it knows about horses semantically – such as its number of legs or lack of wings – to compare its attributes with the animals it has been trained on. Some researchers and technologists believe AI has become an “existential risk”, alongside nuclear weapons and bioengineered pathogens, so its continued development should be regulated, curtailed or even stopped. What was a fringe concern a decade ago has now entered the mainstream, as various senior researchers and intellectuals have joined the fray. Stanford researchers published work on diffusion models in the paper “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” The technique provides a way to reverse-engineer the process of adding noise to a final image. Sepp Hochreiter and Jürgen Schmidhuber proposed the Long Short-Term Memory recurrent neural network, which could process entire sequences of data such as speech or video.

Simon’s work on artificial intelligence began in the 1950s when the concept of AI was still in its early stages. He explored the use of symbolic systems to simulate human cognitive processes, such as problem-solving and decision-making. Simon believed that intelligent behavior could be achieved by representing knowledge as symbols and using logical operations to manipulate those symbols. One of Minsky’s most notable contributions to AI was his work on neural networks. He explored how to model the brain’s neural networks using computational techniques.

It also served as a cautionary tale for investors and policymakers, who realised that the hype surrounding AI could sometimes be overblown and that progress in the field would require sustained investment and commitment. This happened in part because many of the AI projects that had been developed during the AI boom were failing to deliver on their promises. The AI research community was becoming increasingly disillusioned with the lack of progress in the field. This led to funding cuts, and many AI researchers were forced to abandon their projects and leave the field altogether.

Traditional translation methods are rule-based and require extensive knowledge of grammar and syntax. Language models, on the other hand, can learn to translate by analyzing large amounts of text in both languages. However, it’s still capable of generating coherent text, and it’s been used for things like summarizing text and generating news headlines. They’re designed to perform a specific task or solve a specific problem, and they’re not capable of learning or adapting beyond that scope.

It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation. The problem is, no one knows quite how to build neural nets that can reason or use common sense. Gary Marcus, a cognitive scientist and coauthor of Rebooting AI, suspects that the future of AI will require a “hybrid” approach—neural nets to learn patterns, but guided by some old-fashioned, hand-coded logic. This would, in a sense, merge the benefits of Deep Blue with the benefits of deep learning.

Neuralink aims to develop advanced brain-computer interfaces (BCIs) that have the potential to revolutionize the way we interact with technology and understand the human brain. Showcased the potential of artificial intelligence to understand and respond to complex questions in natural language. Its victory marked a milestone in the field of AI and sparked renewed interest in research and development in the industry. Who created artificial intelligence and when it was invented is a question that has been debated by many researchers and experts in the field. However, one of the most notable milestones in the history of AI was the creation of Watson, a powerful AI system developed by IBM. Frank Rosenblatt was an American psychologist and computer scientist born in 1928.

AlphaGo’s success in competitive gaming opened up new avenues for the application of artificial intelligence in various fields. It demonstrated that AI could not only challenge but also surpass human intelligence in certain domains. The groundbreaking moment for AlphaGo came in 2016 when it competed against and defeated the world champion Go player, Lee Sedol. This historic victory showcased the incredible potential of artificial intelligence in mastering complex strategic games. Was a significant milestone, it is important to remember that AI is an ongoing field of research and development. The journey to create truly human-like intelligence continues, and Watson’s success serves as a reminder of the progress made so far.

But with embodied AI, machines could become more like companions or even friends. They’ll be able to understand us on a much deeper level and help us in more https://chat.openai.com/ meaningful ways. Imagine having a robot friend that’s always there to talk to and that helps you navigate the world in a more empathetic and intuitive way.

Language recognition and production is developing fast

Researchers have shown that having humans involved in the learning can improve the performance of AI models, and crucially may also help with the challenges of human-machine alignment, bias, and safety. If an AI acquires its abilities from a dataset that is skewed – for example, by race or gender – then it has the potential to spew out inaccurate, offensive stereotypes. And as we hand over more and more gatekeeping and decision-making to AI, many worry that machines could enact hidden prejudices, preventing some people from accessing certain services or knowledge.

AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright. There are also thousands of successful AI applications used to solve specific problems a.i. is its early days for specific industries or institutions. In some problems, the agent’s preferences may be uncertain, especially if there are other agents or humans involved. Will there come a time when we can build AI so human-like in its reasoning that humans really do have less to offer—and AI takes over all thinking? But even these scientists, on the cutting edge, can’t predict when that will happen, if ever.

By training deep learning models on large datasets of artwork, generative AI can create new and unique pieces of art. Expert systems are a type of artificial intelligence (AI) technology that was developed in the 1980s. Expert systems are designed to mimic the decision-making abilities of a human expert in a specific domain or field, such as medicine, finance, or engineering. They’re designed to be more flexible and adaptable, and they have the potential to be applied to a wide range of tasks and domains. Unlike ANI systems, AGI systems can learn and improve over time, and they can transfer their knowledge and skills to new situations. AGI is still in its early stages of development, and many experts believe that it’s still many years away from becoming a reality.

The development of AI dates back several decades, with numerous pioneers contributing to its creation and growth. Business landscapes should brace for the advent of AI systems adept at navigating complex datasets with ease, offering actionable insights with a depth of analysis previously unattainable. Alongside this, we anticipate a conscientious approach to AI deployment, with a heightened focus on ethical constructs and regulatory frameworks to ensure AI serves the broader good of humanity, fostering inclusivity and positive societal impact. The cognitive approach allowed researchers to consider “mental objects” like thoughts, plans, goals, facts or memories, often analyzed using high level symbols in functional networks. These objects had been forbidden as “unobservable” by earlier paradigms such as behaviorism.[h] Symbolic mental objects would become the major focus of AI research and funding for the next several decades.

Thanks to advancements in cloud computing and the availability of open-source AI frameworks, individuals and businesses can now easily develop and deploy their own AI models. Neuralink was developed as a result of Musk’s belief that AI technology should not be limited to external devices like smartphones and computers. He recognized the need to develop a direct interface between the human brain and AI systems, which would provide an unprecedented level of integration and control. Siri, developed by Apple, was introduced in 2011 with the release of the iPhone 4S.

ServiceNow’s research with Oxford Economics culminated in the newly released Enterprise AI Maturity Index, which found the average AI maturity score was 44 out of 100. If you’re new to university-level study, read our guide on Where to take your learning next, or find out more about the types of qualifications we offer including entry level

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In the 1980s, the development of machine learning algorithms marked a major turning point in the history of AI. These algorithms allowed computers to learn and adapt based on data input, rather than being explicitly programmed to perform a specific task. However, despite these advancements, the AI hype of the 1980s eventually led to an ‘AI winter’, as the technology failed to live up to some of the lofty expectations set for it. In the 1970s, the focus in AI shifted from symbolic reasoning to more practical applications, such as expert systems and natural language processing. Expert systems were designed to mimic the decision-making abilities of human experts in specific domains, while natural language processing aimed to develop machines that could understand and respond to human language. However, progress in AI was limited due to computational constraints and a lack of funding, leading to what became known as the ‘AI winter’.

In addition to his contribution to the establishment of AI as a field, McCarthy also invented the programming language Lisp. It became the preferred language for AI researchers due to its ability to manipulate symbolic expressions and handle complex algorithms. One of the pioneers in the field of AI is Alan Turing, an English mathematician, logician, and computer scientist. Turing is widely recognized for his groundbreaking work on the theoretical basis of computation and the concept of the Turing machine. His work laid the foundation for the development of AI and computational thinking. Turing’s famous article “Computing Machinery and Intelligence” published in 1950, introduced the idea of the Turing Test, which evaluates a machine’s ability to exhibit human-like intelligence.

All major technological innovations lead to a range of positive and negative consequences. As this technology becomes more and more powerful, we should expect its impact to still increase. The chart shows how we got here by zooming into the last two decades of AI development. The plotted data stems from a number of tests in which human and AI performance were evaluated in different domains, from handwriting recognition to language understanding. To see what the future might look like, it is often helpful to study our history.

China’s Tianhe-2 doubled the world’s top supercomputing speed at 33.86 petaflops, retaining the title of the world’s fastest system for the third consecutive time. Jürgen Schmidhuber, Dan Claudiu Cireșan, Ueli Meier and Jonathan Masci developed the first CNN to achieve “superhuman” performance by winning the German Traffic Sign Recognition competition. Danny Hillis designed parallel computers for AI and other computational tasks, an architecture similar to modern GPUs. John McCarthy developed the programming language Lisp, which was quickly adopted by the AI industry and gained enormous popularity among developers. Artificial intelligence, or at least the modern concept of it, has been with us for several decades, but only in the recent past has AI captured the collective psyche of everyday business and society.

Poised in sacristies, they made horrible faces, howled and stuck out their tongues. The Satan-machines rolled their eyes and flailed their arms and wings; some even had moveable horns and crowns. You can foun additiona information about ai customer service and artificial intelligence and NLP. Waterworks, including but not limited to ones using siphons, were probably the most important category of automata in antiquity and the middle ages. Flowing water conveyed motion to a figure or set of figures by means of levers or pulleys or tripping mechanisms of various sorts. A late twelfth-century example by an Arabic automaton-maker named Al-Jazari is a peacock fountain for hand-washing, in which flowing water triggers little figures to offer the washer first a dish of perfumed soap powder, then a hand towel. It has been argued AI will become so powerful that humanity may irreversibly lose control of it.

2021 was a watershed year, boasting a series of developments such as OpenAI’s DALL-E, which could conjure images from text descriptions, illustrating the awe-inspiring capabilities of multimodal AI. This year also saw the European Commission spearheading efforts to regulate Chat GPT AI, stressing ethical deployments amidst a whirlpool of advancements. In 2014, Ian Goodfellow and his team formalised the concept of Generative Adversarial Networks (GANs), creating a revolutionary tool that fostered creativity and innovation in the AI space.

Language models have made it possible to create chatbots that can have natural, human-like conversations. This means that it can understand the meaning of words based on the words around them, rather than just looking at each word individually. BERT has been used for tasks like sentiment analysis, which involves understanding the emotion behind text.

Priority 2: Align your generative AI strategy with your digital strategy (and vice versa)

Unlike traditional computer programs that rely on pre-programmed rules, Watson uses machine learning and advanced algorithms to analyze and understand human language. This breakthrough demonstrated the potential of AI to comprehend and interpret language, a skill previously thought to be uniquely human. With the perceptron, Rosenblatt introduced the concept of pattern recognition and machine learning. The perceptron was designed to learn and improve its performance over time by adjusting weights, making it the first step towards creating machines capable of independent decision-making. While the term “artificial intelligence” was coined in 1956 during the Dartmouth Conference, the concept itself dates back much further. It was during the 1940s and 1950s that early pioneers began developing computers and programming languages, laying the groundwork for the future of AI.

Transformers-based language models are able to understand the context of text and generate coherent responses, and they can do this with less training data than other types of language models. The future of AI in entertainment holds even more exciting prospects, as advancements in machine learning and deep neural networks continue to shape the landscape. With AI as a creative collaborator, the entertainment industry can explore uncharted territories and bring groundbreaking experiences to life. As the field of artificial intelligence developed and evolved, researchers and scientists made significant advancements in language modeling, leading to the creation of powerful tools like GPT-3 by OpenAI. As AI has advanced rapidly, mainly in the hands of private companies, some researchers have raised concerns that they could trigger a “race to the bottom” in terms of impacts. As chief executives and politicians compete to put their companies and countries at the forefront of AI, the technology could accelerate too fast to create safeguards, appropriate regulation and allay ethical concerns.

While some argue that AI-generated text lacks the depth and nuance of human writing, others see it as a tool that can enhance human creativity by providing new ideas and perspectives. The use of generative AI in art has sparked debate about the nature of creativity and authorship, as well as the ethics of using AI to create art. Some argue that AI-generated art is not truly creative because it lacks the intentionality and emotional resonance of human-made art. Others argue that AI art has its own value and can be used to explore new forms of creativity. These techniques continue to be a focus of research and development in AI today, as they have significant implications for a wide range of industries and applications.

Neural Networks and Cognitive Science

In the following decades, many researchers and innovators contributed to the advancement of AI. One notable milestone in AI history was the creation of the first AI program capable of playing chess. Developed in the late 1950s by Allen Newell and Herbert A. Simon, the program demonstrated the potential of AI in solving complex problems.

Cotra’s work is particularly relevant in this context as she based her forecast on the kind of historical long-run trend of training computation that we just studied. But it is worth noting that other forecasters who rely on different considerations arrive at broadly similar conclusions. As I show in my article on AI timelines, many AI experts believe that there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner. Generative AI, especially with the help of Transformers and large language models, has the potential to revolutionise many areas, from art to writing to simulation. While there are still debates about the nature of creativity and the ethics of using AI in these areas, it is clear that generative AI is a powerful tool that will continue to shape the future of technology and the arts. Generative AI is a subfield of artificial intelligence (AI) that involves creating AI systems capable of generating new data or content that is similar to data it was trained on.

It became fashionable in the 2000s to begin talking about the future of AI again and several popular books considered the possibility of superintelligent machines and what they might mean for human society. Dendral, begun in 1965, identified compounds from spectrometer readings.[183][120] MYCIN, developed in 1972, diagnosed infectious blood diseases.[122] They demonstrated the feasibility of the approach. Nvidia stock has been struggling even after the AI chip company topped high expectations for its latest profit report. The subdued performance could bolster criticism that Nvidia and other Big Tech stocks were simply overrated, soaring too high amid Wall Street’s frenzy around artificial intelligence technology. In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further.

It required extensive research and development, as well as the collaboration of experts in computer science, mathematics, and chess. IBM’s investment in the project was significant, but it paid off with the success of Deep Blue. Deep Blue was not the first computer program to play chess, but it was a significant breakthrough in AI. Created by a team of scientists and programmers at IBM, Deep Blue was designed to analyze millions of possible chess positions and make intelligent moves based on this analysis.

Known as “command-and-control systems,” Siri and Alexa are programmed to understand a lengthy list of questions, but cannot answer anything that falls outside their purview. The period between the late 1970s and early 1990s signaled an “AI winter”—a term first used in 1984—that referred to the gap between AI expectations and the technology’s shortcomings. While Shakey’s abilities were rather crude compared to today’s developments, the robot helped advance elements in AI, including “visual analysis, route finding, and object manipulation” [4].

The work of visionaries like Herbert A. Simon has paved the way for the development of intelligent systems that augment human capabilities and have the potential to revolutionize numerous aspects of our lives. Despite his untimely death, Turing’s contributions to the field of AI continue to resonate today. His ideas and theories have shaped the way we think about artificial intelligence and have paved the way for further developments in the field. He is widely regarded as one of the pioneers of theoretical computer science and artificial intelligence. One of the key figures in the development of AI is Alan Turing, a British mathematician and computer scientist. In the 1930s and 1940s, Turing laid the foundations for the field of computer science by formulating the concept of a universal machine, which could simulate any other machine.

Instead of replacing designers and animators, generative AI can help them more rapidly develop prototypes for testing and iterating. Instead of deciding that fewer required person-hours means less need for staff, media organizations can refocus their human knowledge and experience on innovation—perhaps aided by generative AI tools to help identify new ideas. This same sort of pattern recognition also was important to scaling at the consumer packaged goods company we mentioned earlier.

In one human-with-laptop match in 2005, a pair of them won the top prize—beating out several grand masters. Not long after his loss to Deep Blue, he decided that fighting against an AI made no sense. The machine “thought” in a fundamentally inhuman fashion, using brute-force math.

Deep Blue

As we look towards the future, it is clear that AI will continue to play a significant role in our lives. The possibilities for its impact are endless, and the trends in its development show no signs of slowing down. There is an ongoing debate about the need for ethical standards and regulations in the development and use of AI. Some argue that strict regulations are necessary to prevent misuse and ensure ethical practices, while others argue that they could stifle innovation and hinder the potential benefits of AI. Ray Kurzweil has been a vocal proponent of the Singularity and has made predictions about when it will occur.

This revolutionary invention marked a significant milestone in the history of AI. He was particularly interested in teaching computers to play games, such as checkers. Through extensive experimentation and iteration, Samuel created a program that could learn from its own experience and gradually improve its ability to play the game. Simon’s work on symbolic AI and decision-making systems laid the foundation for the development of expert systems, which became popular in the 1980s. Expert systems used symbolic representations of knowledge to provide expert-level advice in specific domains, such as medicine and finance.

Our 26th Annual Global CEO Survey found that 69% of leaders planned to invest in technologies such as AI this year. Yet our 2023 Global Workforce Hopes and Fears Survey of nearly 54,000 workers in 46 countries and territories highlights that many employees are either uncertain or unaware of these technologies’ potential impact on them. For example, few workers (less than 30% of the workforce) believe that AI will create new job or skills development opportunities for them. This gap, as well as numerous studies that have shown that workers are more likely to adopt what they co-create, highlights the need to put people at the core of a generative AI strategy.

Self-driving cars have slammed into fire trucks parked on highways, because in all the millions of hours of video they’d been trained on, they’d never encountered that situation. Ironically, that old style of programming might stage something of a comeback as engineers and computer scientists grapple with the limits of pattern matching. The old-school style of handcrafted rules may have been brittle, but it was comprehensible. The inverted fortunes of Deep Blue and neural nets show how bad we were, for so long, at judging what’s hard—and what’s valuable—in AI.

Successful innovation centers also foster an ecosystem for collaboration and co-innovation. Working with external AI experts can provide additional expertise and resources to explore new AI solutions and keep up with AI advancements. Pacesetters report that in addition to standing-up AI Centers of Excellence (62% vs. 41%), they lead the pack by establishing innovation centers to test new AI tools and solutions (62% vs. 39%). Pinned cylinders were the programming devices in automata and automatic organs from around 1600.

IBM’s Watson Health was developed in 2011 and made its debut when it competed against two former champions on the quiz show “Jeopardy! Watson proved its capabilities by answering complex questions accurately and quickly, showcasing its potential uses in various industries. Uber, the ride-hailing giant, has also ventured into the autonomous vehicle space. The company launched its self-driving car program in 2016, aiming to offer autonomous rides to its customers.

However important, this focus has not yet shown itself to be the solution to all problems. A complete and fully balanced history of the field is beyond the scope of this document. Another definition has been adopted by Google,[338] a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence. When natural language is used to describe mathematical problems, converters transform such prompts into a formal language such as Lean to define mathematic tasks. Greek mythology featured stories of intelligent robots and artificial beings like Pandora, and the idea of “biotechne,” or how technology can alter biological phenomena.

Computer vision is also a cornerstone for advanced marketing techniques such as programmatic advertising. By analyzing visual content and user behavior, Pathlabs programmatic advertising leverages computer vision to deliver highly targeted and effective ad campaigns. BERT, which stands for Bidirectional Encoder Representations from Transformers, is a language model that’s been trained to understand the context of text. ASI refers to AI that is more intelligent than any human being, and that is capable of improving its own capabilities over time.

• It is difficult to pinpoint a specific moment or person who can be credited with the invention of AI, as it has evolved gradually over time.
• In their attempt to clarify these concepts, researchers have outlined four types of artificial intelligence.
• The wide range of listed applications makes clear that this is a very general technology that can be used by people for some extremely good goals — and some extraordinarily bad ones, too.
• The concept of AI was created by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in 1956, at the Dartmouth Conference.

In the context of the history of AI, generative AI can be seen as a major milestone that came after the rise of deep learning. Deep learning is a subset of machine learning that involves using neural networks with multiple layers to analyse and learn from large amounts of data. It has been incredibly successful in tasks such as image and speech recognition, natural language processing, and even playing complex games such as Go. The key thing about neural networks is that they can learn from data and improve their performance over time. They’re really good at pattern recognition, and they’ve been used for all sorts of tasks like image recognition, natural language processing, and even self-driving cars.

Treasury yields also stumbled in the bond market after a report showed American manufacturing shrank again in August, sputtering under the weight of high interest rates. The Dow Jones Industrial Average dropped 626 points, or 1.5%, from its own record set on Friday before Monday’s Labor Day holiday. To complicate matters, researchers and philosophers also can’t quite agree whether we’re beginning to achieve AGI, if it’s still far off, or just totally impossible. For example, while a recent paper from Microsoft Research and OpenAI argues that Chat GPT-4 is an early form of AGI, many other researchers are skeptical of these claims and argue that they were just made for publicity [2, 3]. In a new series, we will test the limits of the latest AI technology by pitting it against human experts. AIs are getting better and better at zero-shot learning, but as with any inference, it can be wrong.

Geoffrey Hinton and neural networks

These are just some of the ways that AI provides benefits and dangers to society. When using new technologies like AI, it’s best to keep a clear mind about what it is and isn’t. Here are his top outdoor escapes in his homeland of Canada, from dogsledding in Inuvik to hot tent camping in Temagami.

Unlike past systems that were coded to respond to a set inquiry, generative AI continues to learn from materials (documents, photos, and more) from across the internet. In 1974, the applied mathematician Sir James Lighthill published a critical report on academic AI research, claiming that researchers had essentially over-promised and under-delivered when it came to the potential intelligence of machines. At a time when computing power was still largely reliant on human brains, the British mathematician Alan Turing imagined a machine capable of advancing far past its original programming. To Turing, a computing machine would initially be coded to work according to that program but could expand beyond its original functions. In the 1950s, computing machines essentially functioned as large-scale calculators.

The success was due to the availability powerful computer hardware, the collection of immense data sets and the application of solid mathematical methods. In 2012, deep learning proved to be a breakthrough technology, eclipsing all other methods. The transformer architecture debuted in 2017 and was used to produce impressive generative AI applications. Weak AI, meanwhile, refers to the narrow use of widely available AI technology, like machine learning or deep learning, to perform very specific tasks, such as playing chess, recommending songs, or steering cars. Also known as Artificial Narrow Intelligence (ANI), weak AI is essentially the kind of AI we use daily.

AI has a range of applications with the potential to transform how we work and our daily lives. While many of these transformations are exciting, like self-driving cars, virtual assistants, or wearable devices in the healthcare industry, they also pose many challenges. For example, self-driving cars use a form of limited memory to make turns, observe approaching vehicles, and adjust their speed. However, machines with only limited memory cannot form a complete understanding of the world because their recall of past events is limited and only used in a narrow band of time. The increasing accessibility of generative AI tools has made it an in-demand skill for many tech roles. If you’re interested in learning to work with AI for your career, you might consider a free, beginner-friendly online program like Google’s Introduction to Generative AI.

Deep Blue’s victory was the moment that showed just how limited hand-coded systems could be. Most people who’d been paying attention to AI—and to chess—expected it to happen eventually. But in its 36th move in the second game, Deep Blue did something Kasparov did not expect. The early excitement that came out of the Dartmouth Conference grew over the next two decades, with early signs of progress coming in the form of a realistic chatbot and other inventions.

ServiceNow and Oxford Economics learned that more than three-quarters of organizations surveyed focused AI returns on increased productivity, enhanced customer experience, higher revenue, improved competitive positioning, and faster innovation. For example, 74% of Pacesetters report AI investments are achieving positive returns in the form of accelerated innovation. Progress toward unknown fronts requires direction and measures to chart momentum. It’s critical to put in place measures that assess progress against AI vision and strategy. Yet only 35% of organizations say that have defined clear metrics to measure the impact of AI investments.

Pacesetters prioritize growth opportunities via augmentation, which unlocks new capabilities and competitiveness. Another finding near and dear to me personally, is that Pacesetters are also using AI to improve customer experience. These companies are setting three-year investment priorities that include harnessing genAI to create customer support summaries and power customer agent assistants. Working smart and smarter is at the top of the list for companies seeking to optimize operations. Pacesetters are more likely than others to deploy AI for data cleaning, management, integration, and visualization (76% vs. 42%), performance management (68% vs. 36%), case summarization (60% vs. 40%), and predictive analytics (60% vs. 37%). The research published by ServiceNow and Oxford Economics found that Pacesetters are already accelerating investments in AI transformation.