The Economic Impact of Generative AI: The future of work in Malaysia

This study marks the latest in our endeavours to evaluate the influence of this new age of AI. It indicates that generative AI is ready to revolutionise roles and improve efficiency in areas such as sales and marketing, customer service, and software creation. In doing so, it could generate trillions of dollars in value across industries from ecommerce to healthcare. For the marketing industry, our platform can help you create content, automate product description creation, craft advertising messages, and generate customer communication to improve engagement, retention, and revenue. For the entertainment industry, our generative AI technology could help your company by creating customized content with a click, producing multiple headlines, calls-to-action, real-time commentaries, summaries, and valuable statistics. The economic potential of generative AI is likely going to experience exponential growth in ways we probably haven’t considered or seen coming.

Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity. Generative AI is poised to impact various industries, with banking, high tech, and life sciences expected to experience significant transformations. McKinsey identifies customer operations/service, marketing and sales, software engineering, and R&D as the most valuable business functions likely to benefit from generative AI. The McKinsey report defines generative AI as applications typically built using foundation models.

I believe the time is now for businesses to think about how to capitalize on generative AI to augment workflows, gain a competitive advantage and create their ideal future. Interacting with most discriminative AI models requires the use of specific syntax or knowledge of a programming language. This takes time to adapt to and greatly limits the range of people capable of using the model. However, generative models use Natural Language Interfaces (NLIs) to interpret text as opposed to code. NLIs reduce the technical learning curve and widen the potential user base, empowering a much larger number of people to utilize the model effectively. AI has been driving value for businesses since the early 2000s; however, the majority of AI models have been discriminative, not generative.

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In factories where people operate complex machines and work with hazardous materials, avoiding accidents and ensuring safety are priorities. Machines and robots can perform these more laborious tasks with increased efficiency and without causing harm. As a result, companies don’t have to stress about extra costs resulting from job-related accidents, and employees can focus on other lower-risk tasks. Though generative AI will have a significant impact across all industry sectors, banking, high tech and life sciences are among the industries that could see the biggest impact on percentage of their revenues from generative AI, McKinsey said. Generative AI could add as much as $4.4 trillion annually to the global economy and will transform productivity across sectors with continued investment in the technology, according to a new study. Generative AI is one of the rare technologies powerful enough to accelerate overall economic growth — what economists call a “general-purpose technology.” These innovations have the potential to positively transform economies and societies.

• It can also enhance performance visibility across business units by integrating disparate data sources.
• AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks.
• Understanding what’s coming next demands recognising the significant advancements that have paved the way for generative AI, a development that spanned decades.

This report seeks to contribute to this discussion by providing early insights and raising awareness of the economic opportunities that generative AI can create, and what it means for local industries and workforce readiness. Another approach for businesses would be to scale up by collaborating with other industry players and create a consortium or third-party platform that is not directly aligned with any single company. This would allow them to pool resources and develop a more competitive AI-driven interface that can cater to a wide range of consumer preferences.

The economic potential of generative AI: 75% of AI value comes from Customer Operations & Sales (McKinsey)

“Although the impact of AI on the labor market is likely to be significant, most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI,” the authors write. In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. Generative AI’s evolution has been gradual, fueled by substantial investments in advanced machine learning and deep learning projects.

Having both these things requires a big increase in investment in semiconductors which in turn requires a big increase in investment in network capacity. We spoke with Briggs about how the team’s forecast has held up over the past year, which businesses are adopting generative AI, and the technology’s impact on the labor market. A report by McKinsey & Company found that AI could automate up to 45% of the tasks currently performed by retail, hospitality, and healthcare workers. While this could lead to job displacement, the report also noted that just because AI could automate a job doesn’t necessarily mean that it will, as cost, regulations, and social acceptance can also be limiting factors. Optimizing inventory management and recommending products to customers based on their purchase history and browsing behavior is only part of the value of gen AI in the retail industry.

While adoption of generative AI is lagging investment in the technology, Goldman Sachs Research sees potential for AI to automate many work tasks. It’s expected to start having a measurable impact on US GDP in 2027 and begin affecting growth in other economies around the world in the years that follow. You can foun additiona information about ai customer service and artificial intelligence and NLP. The use of gen AI in finance is expected to increase global gross domestic product (GDP) by 7%—nearly $7 trillion—and boost productivity growth by 1.5%, according to Goldman Sachs Research. Gen AI is a good fit with finance because its strength—dealing with vast amounts of data—is precisely what finance relies on to function. In the financial industry, AI algorithms detect fraud and identify investment opportunities. Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations.

According to the same research by Goldman Sachs, only 7% of U.S. jobs risk automation, while 63% will leverage AI-enabled augmentation, and roughly 30% will remain unaffected. Traditional models have been trained on smaller, specialized datasets to serve a specific purpose (e.g., analyze previous machine maintenance patterns to predict when servicing is necessary). Generative AI models are trained on large databases, such as the entire publicly available internet, and so can serve a much wider range and versatility of use cases. Luxembourg can leverage its strong position on fundamental AI adoption drivers, but needs more talent and innovation to capture the potential. If you look at forecast revisions for AI hardware providers, they imply about a $250 billion increase since a year ago.

Business leaders need to bring them along by listening and addressing their concerns, and up/reskilling their employees along the way. Of CEOs surveyed by IBM, 75% believe businesses leveraging the most advanced generative AI will garner a distinct competitive advantage. The technology’s ability to widen the range of tasks AI can automate has already led to a reduction in time-consuming work and a subsequent surge in productivity.

Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. AI algorithms learn from the data they are trained on, and if that data is biased or incomplete, the algorithms can perpetuate those biases in their outputs. A trial conducted at five Johns Hopkins Medicine System-affiliated healthcare facilities found that using AI algorithms to analyze medical images led to a 20% reduction in sepsis deaths in hospitals. Sepsis, which happens when the response to an infection spirals out of control, is responsible for one out of three in-hospital deaths in the United States. According to the Centers for Disease Control and Prevention, about 1.7 million adults in the U.S. develop sepsis each year, and about 350,000 of them die. Develop a deployment strategy for incorporating AI, ML, and Big Data into your organization that will take advantage of cutting-edge technologies from Penn’s Wharton Business School.

Drucker is often considered the father of modern management due to his extensive contributions to the field. Drucker’s philosophy and thoughts on management focused on people and human relationships. He taught that knowledgeable workers are the essential ingredients of the modern economy. Central to this philosophy is the view that people are an organization’s most valuable resource and that a manager’s job is preparing and freeing people to perform. This is not the same narrative we are hearing, as many people fear that artificial intelligence may take over their jobs.

Furthermore, according to the same source, this is more than the United Kingdom’s GDP of $3.07 trillion over the same period. GDP is a standardized monetary tool that measures the market’s value based on the final goods and services produced in a country over a determined time period. By now, you have probably heard of or have an idea about what this technology is and what it does. Generative AI is a subset of artificial intelligence that can be used to produce various outputs, like image, text, audio, and other forms of data. The output depends on the intended purpose of the AI model, which can be tweaked to suit the needs of individuals and organizations based on several parameters.

And because generative AI accumulates knowledge and makes it available on demand, it’s particularly effective at improving the performance of entry-level employees, helping with wage inequality. We found significant improvements in worker productivity as measured by the number of customer issues workers were able to resolve per hour. Within four months, treated agents were outperforming nontreated agents who had been on the job for over twice as long. For AI to be deployed on a widespread basis, there’s a lot of things that need to happen. First you need to have models that are powerful enough and trained appropriately so they can actually be useful in everyday work product. Then you need to have the capability to facilitate and answer all the queries that people are going to be posing to AI models, when they do use them every day multiple times a day when they’re engaged in regular work.

This implies that more than 85% of employment growth over the last 80 years is explained by the technology-driven creation of new positions, our economists write. A new wave of AI systems may also have a major impact on employment markets around the world. Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation, Briggs and Kodnani write. Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases.

This consideration creates the necessity for new regulations and legal frameworks to ensure algorithms are used ethically. Professionals must also refrain from copying content verbatim since they may receive copyright strikes. Despite the immense creativity generative AI provides, ethical considerations regarding accountability, bias, and privacy arise. For instance, can companies fully trust an AI tool to process their employees’ sensitive data safely?

The latter has propelled AI into previously unimaginable situations which has got people divided, including well respected and highly regarded professionals in technology. It makes me (Tom Allen) laugh when people think they have got the answer for what its use will mean. When you might have got a solution for how to use Generative AI figured out, not what the eventual outcome will be as it changing every second of every day. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation.

Applications of Generative AI

While the economic potential of generative AI is valid, its implementation may prove challenging for many companies. Professionals with remarkable technical expertise must be recruited so they can operate the algorithm effectively. Therefore, many organizations that can’t afford such additions may be left behind and make massive efforts to catch up to their competition. While traditional manual labor positions may fall into obscurity or decrease significantly, other, more technical jobs will be created. However helpful and life-saving AI-powered machines may be, they can’t operate on their own.

The algorithm can monitor everyone’s performance, provide feedback, notice skill gaps, and advise on development opportunities. Many organizations across the globe are now using AI tools to create content for recruitment as HR benefits highly during acquisition and onboarding. They ask the algorithm to create job postings based on skills, keywords, and older listings. In advanced cases, companies may design avatars for each candidate and provide personalized feedback. The rapid development of generative AI also has the potential to “change the anatomy of work” and can automate work activities that absorb 60 to 70 per cent of employees’ time today.

Now, the generative AI market is expected to grow from $40 billion in 2022 to $1.3 trillion over the next 10 years. In this article, I aim to demystify how generative AI constitutes a distinct revolution and explore the prospective economic impacts of deploying this technology across diverse sectors. In fact, if we look at the labor demand that is generated it’s probably driven a net increase in employment. And so, it’s very well possible, and probably even likely, that the net impact on the labor market has been positive thus far.

Managing through the generative AI revolution will involve diving into the most relevant use cases, evaluating a strategic approach to leveraging AI tools, and re-skilling the workforce to match changing demand. Overall, generative AI presents both challenges and opportunities, and organizations must be prepared to navigate the changing landscape to stay ahead of the game. Generative AI is only a piece of the pie organizations should consider in context of the value AI can generate.

These matters should be addressed early on and companies must devise plans to effectively treat the gray areas. Excitement over this technology is palpable, and early pilots are compelling,” the McKinsey report said. Ahead of the meeting, major AI companies, including Microsoft and Alphabet’s Google, committed to participating in the independent public evaluation of their systems.

Specifically, AI-powered personal assistants like Siri, Alexa, and Google Assistant can now understand and respond to complex requests, making it possible to deliver on the promise of truly personalized assistance. As AI continues to advance, these personal assistants will become even more sophisticated, learning individual preferences and providing tailored recommendations, while also generalizing to a broader set of tasks. This shift in interaction modality has significant implications for businesses, as it means consumers may no longer be directly influenced by traditional advertising methods. Instead, advertisers may need to target the AI agents themselves, which will be responsible for surfacing brands to their users. Generative AI represents the next frontier of productivity and revenue growth in the IT and software development industries.

From specialized to generalized intelligence: The pathway paved by generative AI models

Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.2 to 3.3 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.

Around the same time, Variational Autoencoders (VAEs) and Recurrent Neural Networks (RNNs) began to demonstrate their ability to generate novel content. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge.

Goldman Sachs estimates that generative AI could automate tasks that take up to one-fourth of employees’ time today. These assessments have sparked concerns about job displacement and an uncertain future of work. No, it really hasn’t changed because our forecasts don’t assume any AI boost at all before 2027. Even though we do still think that it’s going to be a significant driver of productivity and GDP growth over a much longer horizon.

Discriminative models excel at making predictions from existing data and identifying anomalies. These models power everything from social media content recommendation engines to financial fraud detection platforms. I think that a lot these reasons broadly reflect that companies want to make sure that they get generative AI right, and companies are therefore taking deliberate approach to AI adoption.

AI-powered security solutions can analyze large datasets, detect patterns and anomalies, and suggest countermeasures before cybercriminals strike. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. We would just add that generative AI is just that, in the business of generating something, usually to see, although there are aspects such as generative code and other similar offerings. The quiet technology revolution is happening in predictive and analytics AI, where hyper-personalisation for each consumer’s product selections applicable to the immediate needs and maximum relevancy is quietly taking over. And situations like this are likely going to become a reality for companies in various sectors of different sizes.

First and foremost, we see adoption rates higher in areas like information services, finance and insurance. The motion picture and sound recording industry, for instance, is another area where adoption is far above the economy wide average. “That being said, the early signals of future productivity gains look very, very positive,” he adds.

To harness generative AI’s potential, significant investments in workforce adaptation are essential. This includes reskilling workers and managing transitions as certain roles evolve or disappear. If managed well, generative AI could boost labor productivity by 0.1 to 0.6 percent annually through 2040 and combine with other technologies to enhance overall productivity growth by 0.5 to 3.4 percentage points annually. Malaysia’s digital economy has experienced rapid growth, with the ICT sector contributing 22.6% to Malaysia’s GDP in 2021, driven by government initiatives, private sector investment, and increasing adoption of digital technologies. The National Fourth Industrial Revolution (4IR) Policy document identifies AI as one of the key technologies that ‘’are foundational to the nation’s 4IR agenda’’ and stresses the need to develop ethical use of AI for transforming the economy.

As computational power increased, deep learning algorithms became increasingly successful, leading to an explosion of interest in AI in the 2010s. The adoption of generative AI is expected to significantly impact various industries and job markets, including manufacturing, healthcare, retail, transportation, and finance. While it is likely to lead https://chat.openai.com/ to increased efficiency and productivity, it is also expected to lead to job displacement for some workers. Generating new content based on cumulative data input makes gen AI worthwhile in many industries. The speed with which this technology can create content can help employees develop more content in less time and/or work more efficiently.

While generative AI will impact a wide variety of industries, 75% of its potential value spans just four sectors. When you combine the broader capabilities of generative models with the democratization of access provided by NLIs, the explosive rise of ChatGPT and massive generative AI market predictions become more understandable. The adoption and usage will occur when these pieces are in place, and companies actually start using AI on an everyday basis. We see about 5% of companies reporting that they do use generative AI today in regular production, but this is a fairly small share relative to the overall number of companies that we think will ultimately benefit. And ultimately, that’s going to require an increase in electricity and collective power investment to support the increase in demand that facilitating queries will require. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

Companies such as Tesla and Toyota are leveraging AI-driven simulations and generative design algorithms to create lighter, more fuel-efficient vehicles with enhanced safety features. As the automotive industry transitions towards electric and autonomous vehicles, generative AI will play a pivotal role in shaping the future of transportation. While much is unknown about how generative AI will influence the world economy and society, and it will take time to play out, there are clear signs that the effects could be profound.

Such feelings, in turn, could lead to decreasing engagement and productivity, and higher turnover. Learn more about the overall report on The economic opportunity of generative AI in D9+ and get links to all country reports. These views generally align with what we’ve seen in some of the business surveys, where CEOs are asked about their intention to use generative AI. Very few say that they expect it’s going to significantly impact their business over the next one to three years. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity.

If we’re talking about the things that people say that they’re using it for, marketing, automation, chatbot, speech text, and data analysis are all areas that stand out as ways that companies are applying AI right now. This is kind of the low hanging fruit where AI is most applicable, at least in its current form. Ultimately, we think that a broader set of tasks are going to be automated by generative AI.

With the help of these advanced models, creative tasks can now be automated, diversified, and customized, leading to an overall enhancement of quality. This could have massive implications for all forms of digital content creation, from social media and user generated content to digital movie or game production. If the benefits of scaling up AI models reach the point of diminishing returns, then the restrictions on China’s AI development may become less effective.

The ServiceBot streamlines processes from order tracking to client information gathering. It handles service queries efficiently, integrates with the ERP and powers customer portals, ensuring a seamless service experience. We have seen that AI-powered conversational commerce can reduce customer service costs by about 30%. According to a research, AI-powered consulting and training tools can suggest optimal consulting solutions that increase ROI by 175%. AI-powered tools can suggest optimal consulting solutions, provide feedback, and suggest areas of improvement, and even provide personalized training.

For instance, generative AI could add $200 billion to $340 billion annually to the banking sector alone. The sudden emergence of AI chatbot ChatGPT and other tools have jump-started investment in the AI sector. More than $2 billion worth of investments were made in generative AI sector in 110 deals in 2022 alone, according to Goldman Sachs. The latest estimate is an upgrade from 2017 when the consultancy estimated AI to deliver $9.5 trillion to $15.4 trillion in economic value. Artificial intelligence (AI) is rapidly enabling solutions to the challenges we face in our lives. The term was coined in 1956, but the field has only recently begun having significant effects on the economy.

Foundation models, a key component of generative AI, process large and varied sets of unstructured data, enabling them to perform diverse tasks such as classification, editing, summarization, and content generation. Issues of data privacy, security, and ethical considerations around AI-generated content need to be addressed. Ensuring the economic potential of generative ai that AI systems are transparent and that their outputs are fair and unbiased is crucial for gaining public trust and maximizing the benefits of these technologies. Generative AI, characterized by its ability to create new content—whether text, images, music, or even complex data models—heralds a new era of creativity and innovation.

The financial services and investment banking sector is maximising how end users can make their money and assets do more for them. Global banking institutions such as Goldman Sachs and JPMorgan Chase are using AI-powered algorithms to optimize trading strategies and risk management processes. By analyzing vast amounts of financial data in real-time, these companies can make more informed investment decisions and mitigate potential risks, leading to increased profitability and market competitiveness. The implications of generative AI extend far beyond the confines of academia and research labs with the technology having real actions on modern society and how we interact, do business, chat to friends, spend our time, and everything else.

In April, Goldman Sachs said the sector could drive a 7 per cent – or almost $7 trillion – increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period. As AI continues to advance, businesses must be prepared to navigate the challenges and opportunities that come with this transformative technology. Census Bureau, there has only been one job throughout all time that has been fully automated—the elevator operator. While initially seen as job-killers for bank tellers, the adoption of ATMs led to more bank branches opening and even more roles for bank tellers in a phenomenon known as Jevon’s paradox. This is because the cost for opening a single branch dropped substantially due to automation. While large language model (LLM) technology has large potential for value generation, without strategic planning and investment, machine learning (ML) AI projects will likely fail.

“Generative AI can streamline business workflows, automate routine tasks and give rise to a new generation of business applications,” Kash Rangan, senior U.S. software analyst in Goldman Sachs Research, writes in the team’s report. The technology is making inroads in business applications, improving the day-to-day efficiency of knowledge workers, helping scientists develop drugs faster and accelerating the development of software code, among other things. Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. These are the result of huge investments in advanced machine learning and deep learning projects.

• EY is a global leader in assurance, consulting, strategy and transactions, and tax services.
• One-third of all entry-level roles could be automated; at the same time, junior employees armed with generative AI may potentially replace their first-line managers, leaving a vacuum in the middle of the job pyramid.
• He taught that knowledgeable workers are the essential ingredients of the modern economy.
• This leads to more efficient utilization of resources, cost savings, and increased overall IT operational performance.
• In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences.

Many of the most important debates about access and control of AI systems are downstream of the scale-up vs. scale-down debate, including the debate about open-source vs. closed-source AI. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy and according to McKinsey and it is already having a significant impact across all industry sectors. Generative AI is a branch of artificial intelligence that focuses on creating new and original content, such as images, text, or even code, using models trained on vast amounts of existing data. It goes beyond traditional AI techniques by enabling machines to generate creative and innovative outputs.

Goldman Sachs Research predicted last year that generative AI could boost GDP and raise labor productivity growth over the coming decade. Since publishing that outlook, investment in generative AI has boomed, but it will take time for the technology to filter into the overall economy. Generative AI has the potential to automate certain tasks, displacing some workers, and it can also create new jobs and industries.

But that probably requires a build out of an application layer to support the broader automation we see possible. We provide customized education solutions to upskill individuals and businesses to thrive in the creator economy. Our courses range from various creative segments taught by worldwide content creators, to latest creative technologies and tools including topics like GenAI tools. A study by the World Economic Forum found that adopting AI could lead to a net increase in jobs in some industries, particularly those that require higher levels of education and skills. However, the report also warned that the benefits of AI could be unevenly distributed, with some workers and regions experiencing more significant job displacement than others. In the entertainment industry, gen AI creates personalized recommendations for movies, TV shows, and music based on individual preferences.

The technology enables businesses to automate content creation, from writing compelling articles to designing engaging visuals. With personalized content becoming increasingly important, generative AI algorithms can analyze user preferences and deliver tailor-made experiences. This level of customization not only enhances user satisfaction but also drives customer loyalty and revenue growth. As we stand on the cusp of a new year, the buzz surrounding generative artificial intelligence (AI) is reaching a crescendo. The year 2024 promises to be a groundbreaking period for businesses and economies worldwide, as the economic potential of generative AI takes center stage.

Understanding what’s coming next demands recognising the significant advancements that have paved the way for generative AI, a development that spanned decades. For the context of this report, we’re referring to generative AI as the technology often developed with the help of foundational models. These foundational models are made up of complex artificial neural networks, modeled after the trillions of neurons found in the human brain. In pharma and medical products, the total potential gains from the use of generative AI could be as high as $110 billion. The main areas where the revenue increases occur in the life sciences industry include research and drug discovery, content and document generation, and contract creation.

By working together, businesses can create a more level playing field and ensure they are not left at the mercy of a single dominant player in the market. While generative AI will likely not lead to a decrease in jobs, it will change the employment landscape. There will be less need for certain human input as tasks that once took hours and many hands can be completed nearly instantaneously. Managing through the generative AI revolution will involve re-skilling the workforce to match changing demand. Generative AI’s capacity to automate tasks core to a wide range of industries has led to a general fear that it will have a depressive effect on jobs. The Technology & Society Visiting Fellowship brings experts to Google to proactively engage in critical societal discussions on the ramifications of cutting-edge technology and its impact on the world.

Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. “Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales and draft computer code based on natural-language prompts, among many other tasks,” the report said. Generative AI has opened the door to more possibilities and is expected Chat GPT to play a role in tasks requiring creativity, curiosity, and looking at information differently. Therefore, the potential of generative AI lies in its ability to enable people to achieve greater creativity, effectiveness, and efficiency in their work. This technology is already delivering large productivity gains, which will increase and spread as people and organizations come up with complementary innovations that leverage generative AI’s capabilities. As a result, overall productivity will improve, resulting in an acceleration of economic growth.

In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. Several studies and analyses have examined the impact of generative AI on the economy, with estimates ranging from $14 trillion to $15.7 trillion in economic contribution by 2030. The potential economic benefits of generative AI include increased productivity, cost savings, new job creation, improved decision making, personalization, and enhanced safety.

Today’s computing ecosystem – and, by extension, its energy consumption – is centralized in large cloud data centers. From 2010 to 2018 global computing output in data centers jumped six-fold while energy consumption rose only 6%. This relative efficiency reflects a concerted effort by cloud computing players and data center operators to optimize energy usage and performance.