Generative AI for Economic Research: Use Cases and Implications for Economists American Economic Association

This shift, while disruptive, brought about immense benefits, creating new industries, jobs, and economic opportunities. Today, as we stand on the brink of another transformative era – the age of Artificial Intelligence (AI) – it’s worth looking back at this historical shift for insights and lessons. The rise of generative AI has brought forth a myriad of ethical the economic potential of generative ai and societal considerations. You can foun additiona information about ai customer service and artificial intelligence and NLP. One major concern revolves around the potential misuse of AI-generated content, raising issues related to misinformation, deepfakes, and intellectual property infringement. The ability to create convincing fake images and videos poses a threat to trust in media and challenges the authenticity of information circulating online.

To stay ahead of the game, corporations must define an AI/ML roadmap to outline a clear path toward maturity in this space. The rapid changes brought by the spread of generative AI will require prompt and effective reskilling efforts. These efforts will be able to draw on generative AI itself, a tool with the unique ability to help people learn how to use it better.

This technology can foster the same efficiency and accuracy that it does in other industries, making it a potential cost-saver for media companies. In the healthcare industry, gen AI is used to analyze medical images and assist doctors in making diagnoses. According to a report by the World Health Organization (WHO), up to 50% of all medical errors in primary care are administrative errors. Gen AI has potential to increase accuracy, but the technology also comes with vulnerabilities, as its trustworthiness depends heavily on the quality of training datasets, according to the World Economic Forum. Previous automation technology was particularly good at collecting and processing data — and these tasks can be further automated by generative AI’s natural language ability. The impact of generative AI — such as ChatGPT and its competitors — is likely to be a business automation and productivity game-changer.

In a study, AI-powered cybersecurity solutions helped reduce the time to detect and remediate cyber-attacks by 635%. StoryLab – StoryLab.ai solves common problems marketers face, such as time constraints, inconsistency in quality, lack of collaboration, and difficulty in capturing attention. I agree with the findings; if you are a marketer, software developer, or R&D professional and aren’t leveraging AI, you will probably not be competitive in the employment market and probably much sooner than one might think. I also believe it’s not a death sentence but an opportunity for those willing to update their skills. Just four months after that, OpenAI introduced GPT-4, a new large language model (LLM) with significantly enhanced abilities.

Generative AI one day could even change the way people form relationships, for better or worse. Some 14% of consumers said they prefer interacting with AI because they believe generative AI can be more emotionally intelligent than humans. Apps like Replika are redefining companionship, allowing users to craft virtual friends who are always there to listen.

In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. We take a first look at where business value could accrue and the potential impacts on the workforce. In healthcare, AI-generated models can assist in diagnosing diseases and personalizing treatment plans, thereby improving patient outcomes and reducing costs. In finance, generative AI can create sophisticated risk models and enhance decision-making processes. Even in entertainment, AI-generated scripts, music, and art are beginning to revolutionize content creation, offering new forms of engagement and expression. While the promise of generative AI is vast, realizing its full benefits will take time and effort.

This leads to more efficient utilization of resources, cost savings, and increased overall IT operational performance. According to the report, the banking, technology, and life sciences industries will see the highest impact in terms of revenue generated by adopting and using this technology. The report mentions that, for example, the banking industry could see an additional $200 to $340 billion per year when generative AI is fully implemented. Another example highlighted by the report is the retail sector, where the financial return could be as high as $660 billion per year. Generative AI like QuickVideo.ai holds significant economic potential in the marketing and creative industries. Imagine creating personalized marketing videos at scale with AI-powered avatars that can speak different languages or cater to specific demographics.

Additionally, the efficiency gains and cost savings from AI adoption can be reinvested into the economy, fostering new businesses and industries. The emergence of foundation models in generative AI dramatically lowers the barriers to AI adoption, simplifying labeling requirements and enhancing the accuracy and efficiency of AI-driven automation. This means that more companies can now deploy AI in a range of critical operations, signaling a new era of AI integration across industries. Generative AI represents a class of advanced deep-learning models that can process and “learn” from massive datasets, such as the entire content of Wikipedia or the collective works of artists like Rembrandt.

However, generative AI’s greatest impact is projected to be on knowledge work — especially tasks involving decision-making and collaboration. For example, according to McKinsey, the potential to automate management and develop talent (ie, the share of these tasks’ worktime that could be automated) increased from 16% in 2017 to 49% in 2023. Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation.

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We bring world-class expertise to deliver customers actionable, objective insight for faster, smarter, and stronger performance to thrive in any digital economy. For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals. There were also follow-on effects of that job creation, as the boost to aggregate income indirectly drove demand for service sector workers in industries like healthcare, education and food services. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain.

In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.

By automating or enhancing tasks that demand human creativity or intelligence, generative AI can elevate the quality and quantity of outputs, cut costs and errors, and unlock new avenues for expression and discovery. From art and design to music composition, the technology empowers creatives to explore new frontiers. Imagine AI systems collaborating with artists to produce unique masterpieces or composing symphonies that resonate with human emotions. The economic impact of such collaborations is not only cultural but extends to new revenue streams and market opportunities.

Andrew McAfee is Technology & Society’s inaugural fellow, a Principal Research Scientist at the MIT Sloan School of Management, and the co-founder and co-director of MIT’s Initiative on the Digital Economy. He studies how technological progress transforms the world, with a particular focus on how computerization affects competition, society, the economy, and the workforce. Previous general-purpose technologies have resulted in changes to the companies and countries leading the way in different industries. Previous general-purpose technologies like the steam engine and electrification have brought their changes over decades. However, we anticipate that generative AI’s effects will be felt more quickly due to its ability to diffuse quickly via the internet and its ease of use owing to its natural language interface.

Key Statistics Highlighting Potential Impact Of Economic Potential Of Generative AI SS

Unlike traditional AI, which focuses on analysis and pattern recognition, generative AI produces novel outputs that can be indistinguishable from human-created content. This capability not only enhances creative processes but also opens up new avenues for product development and personalized customer experiences. In the annals of technological advancements, few innovations promise to reshape the economic landscape as profoundly as generative artificial intelligence (AI).

Early attempts date back to the 1950s, with the development of rule-based systems and expert systems. However, it was not until the 21st century that significant advancements, particularly in deep learning, propelled generative AI to new heights. Looking ahead, McKinsey’s adoption scenarios suggest that between 2030 and 2060, half of today’s work activities could be automated, with a midpoint estimate in 2045. The road to human-level performance in generative AI is predicted by the end of the decade, with potential competition with the top 25 percent of human performance in certain tasks before 2040. Despite the excitement over this technology, a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.

The Productivity J-Curve model implies that productivity metrics fail to capture the full extent of benefits during the initial stages of AI adoption, leading to underestimation of AI’s potential. Nearly half of consumers said they would place their faith in AI for big life decisions such as buying a home. Some 36% said they prefer AI over humans for financial advice, demonstrating the breadth of service generative AI can produce, even when solving for traditionally human needs. There are a few specific use cases that are emerging if you look across the industries that are using generative AI.

Learning or algorithm bias refers to societal biases and inequities that are transferred from humans to machines. Since people train AI-based algorithms, they often convey the same prejudices, meaning that machines can’t be objective. While adopting new technologies is exciting and helpful, organizations must invest resources in purchasing machines and infrastructure. Even if they don’t necessarily have to buy technological tools, they may need to train team members so they learn new skills. Gathering and analyzing employee performance data is a time-consuming and tiresome procedure. Empowering AI to perform these tasks can free up time and allow HR professionals to focus on other activities.

This symbiotic relationship between humans and AI can lead to breakthroughs that neither could achieve alone. Generative AI is poised to be a game-changer for the global economy, potentially adding $2.6 trillion to $4.4 trillion annually. This surge in economic value could increase the impact of all AI by 15-40%, and even more if generative AI integrates into existing software. We’ve already established the economic potential of generative AI and the risks it possesses. However, is there an even bigger Artificial Intelligence challenges you should know about?

It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. Generative AI can significantly change the face of business operations, as employees might spend less time on manual tasks that algorithms automate and streamline.

In this blog post, we will explore the transformative power of generative AI and its potential to reshape industries, drive innovation, and fuel economic growth. To my previous point about skills development in the 21st century, I think it behooves us all to learn as much as we can to ensure future employment and improve our prospects around that employment. You will examine three fundamental forces that enable AI in marketing strategies – Algorithms, Networks, and Data – and gain a deeper understanding of how businesses in various industries can get the most out of this exciting technology.

In this article, we will go over several of the report findings to better understand how this technology accelerates the potential of organizations and individuals alike. Breakthroughs in generative artificial intelligence have the potential to bring about sweeping changes to the global economy, according to Goldman Sachs Research. As tools using advances in natural language processing work their way into businesses and society, they could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period. Generative AI (gen AI) has completely revolutionized how professionals work, communicate, and complete daily activities. According to research, the economic potential of generative AI is massive as it will increase its impact by 15%-40%, which is equivalent to $2.6 to $4.4 trillion. The four areas the algorithm will impact the most are customer service, marketing and sales, software engineering, and research and development.

Gen AI is expected to help address this shortage through increased efficiency, allowing fewer workers to serve more patients. Gen AI’s impact on consumption patterns has made it easier for companies to personalize their marketing and advertising efforts. This has led to a more targeted approach to advertising, which can be beneficial but also problematic from a privacy perspective. FM is published by AICPA & CIMA, together as the Association of International Certified Professional Accountants, to power opportunity, trust and prosperity for people, businesses and economies worldwide.

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This McKinsey report discusses the transformative potential of generative AI, highlighting its ability to significantly boost productivity across various sectors. Generative AI applications, such as ChatGPT and GitHub Copilot, are revolutionizing tasks from data organization to creative content generation. Major impacts are expected in customer operations, marketing, sales, software engineering, and R&D, with substantial benefits for industries like banking, tech, and life sciences. However, successful implementation requires careful management of risks and workforce adaptation. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs.

Goldman Sachs is not providing any financial, economic, legal, accounting, or tax advice or recommendations. This material does not purport to contain a comprehensive overview of Goldman Sachs products and offering and may differ from the views and opinions of other departments or divisions of Goldman Sachs and its affiliates. This happens again in deployment phases with processes like CI/CD, where they must consider security while deploying. It’s hardly surprising that the most popular use cases for gen AI have been around natural or conversational language interfaces for tasks ranging from query to coding. We expect that data discovery and governance will be a major target for gen AI augmentation in the coming year.

This can reduce the need for human labor, raising concerns about job displacement and income inequality. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8).

Dispence information on Different Industries, Development Costs, Statistics Highlighting, using this template. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.

As AI continues to grow in power, so too does the need for economic research to better understand how we can harness its benefits while mitigating its risks. Generative AI (Gen AI) is a type of artificial intelligence designed to generate new content without human intervention, such as text, images, and even music. This technology uses complex algorithms and machine learning models to memorize patterns and rules from existing data and generate new content similar in style and structure.

Generative AI is set to reshape work by automating tasks that currently occupy 60-70% of employees’ time. This leap in automation capability, primarily due to the AI’s natural language processing, will especially affect knowledge work. As a result, the potential for automating work activities could arrive a decade earlier than previously estimated, potentially automating half of today’s work activities between 2030 and 2060. Credera combines transformational consulting capabilities, deep industry knowledge, and AI and technology expertise to deliver valuable customer experiences and accelerated growth across a broad range of industries worldwide.

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So makes you think about where we truly stand and what is the approach we can consider taking for AI’s impact on the world’s economies. People seem to be obsessed with looking ahead rather than dealing with how AI is impacting the world today. Other areas are less impacted and this is explained by the nature of gen AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI. The rush to invest in gen AI reflects the rapid growth of its developed capabilities as explained in the timeline below. This continued technological innovation has been made possible by a significant and rapid growth in funds, reaching a total of $12 billion in the first five months of 2023. By preparing for these changes today, businesses and economies can position themselves to thrive in the AI-driven world of tomorrow.

• This can reduce the need for human labor, raising concerns about job displacement and income inequality.
• This marked a turning point, enabling the generation of highly realistic and diverse data, from images to text.
• Excitement over this technology is palpable, and early pilots are compelling,” the McKinsey report said.

Generative AI possesses the power to create human-like content instantaneously, unlocking new levels of productivity across various sectors of our economy. As this technology develops, I believe it will continue to empower the transcendence of previous capabilities. The large language model (LLM) released by OpenAI is the first program to make generative artificial intelligence (AI) easily accessible to the public.

Consultants by automating repetitive tasks, generating code snippets, and providing innovative solutions. According to a survey conducted by a Consulting organization, 82% of IT consulting firms have reported increased productivity and efficiency through the adoption of generative AI tools. The latest generative AI applications can perform a range of routine tasks, such as the reorganisation and classification of data. But they can write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. In this context, the McKinsey & Company report indicated that generative AI can achieve this feat by automating tasks that consume 60% to 70% of the employees’ workday. This percentage is higher than the company’s previous report, which indicated that generative AI could automate tasks that consume half of the time employees spend performing their jobs.

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Following Moore’s Law, the processing power of today’s most advanced chips is exponentially greater than the most advanced chips of a decade ago. The UK has a strong history of scientific discovery and AI innovation, including at its world-leading research universities like Oxford and Cambridge, and AI companies like DeepMind, now a part of Google. The UK AI market is currently valued at $21 billion and projected to scale by orders of magnitude over the next decade. The UK government has also announced $1.3 billion for supercomputing and AI research, on top of $2.8 billion in prior AI investments. France has invested at the national level, including EUR 1.5 billion from 2018 – 2022 and an additional EUR 500 million for AI “champions” in 2023. Most importantly, Paris is betting on open-source firms to win the technology debate and provide room to innovate within existing and pending EU regulatory frameworks.

Many businesses are hesitant about incurring a major security or ethics breach—not unlike the early days of PCs, the internet and mobile computing. But like those technologies, gen AI will move through its current era of vast disruption to become an unquestioned part of the fabric of work. With due diligence, governance and a phased implementation, these new tools can, and should, be safely deployed without constraining the potential gains in innovation, efficiency and productivity. In the sectors where the technologies were widely implemented, productivity increased, much as it did after the first Industrial Revolution, when humans stopped digging trenches and turned instead to steam shovels.

GenAI has the potential to fundamentally change the marketing function – from storyboarding to creative content to customization for different media channels and audiences. First, entrepreneurs should consider that challenging incumbents in the era of ML will be more difficult since incumbents’ use of ML for proprietary data makes them more formidable competitors. This fact implies that entrepreneurs need to become riskier and more creative in the future to obtain a competitive edge. They may therefore seek support from angels or venture capital firms and use their financing and experience to become more novel in their ventures.

AI that has been trained on these models can carry out a variety of tasks; it can categorise, modify, condense, respond to queries, and create new content, among other activities. Artificial Intelligence has gradually integrated into our daily experiences, from the generative AI technology that operates our mobile devices, to self-driving cars, to the instruments that stores employ to astonish and please shoppers. Significant achievements, like the moment when AlphaGo, an AI-driven application created by DeepMind, triumphed over a global Go champion in 2016, were acknowledged but soon lost attention from the general public. Narrativa has experience across multiple industries, like life sciences, finance, marketing, entertainment, and many others.

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A published scholar in the fields of artificial life, agent-oriented software engineering and distributed artificial intelligence, Babak has 31 granted or pending patents to his name. He is an expert in numerous fields of AI, including natural language https://chat.openai.com/ processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.

By automating time-consuming tasks, creating high-value content, and targeting specific buyer personas, companies save money while creating new job roles. Contrary to fears of job displacement, the widespread adoption of generative AI is expected to create new employment opportunities. As businesses harness the technology to drive innovation, there will be an increased demand for skilled professionals in AI development, data science, and related fields. This surge in job creation is a positive driver for economic growth, fostering a workforce that is adaptive to the evolving technological landscape. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development.

We have partnered with leading companies in these areas, like the Leukemia & Lymphoma Society (LLS), Microsoft, and the Wall Street Journal. To think we’re only getting started, it’s interesting to see the different emotions people have about it from exciting to worrying depending on how they are looking at this technology. Business owners might be looking Chat GPT at the benefit gains and higher profitability that are available on lower business overheads and resources while academia and social communities might have a growing concern with the rate of adoption and how AI is being used. Or does it mean a safer, better economy because of the ethical and societal uses being a priority with a law around it?

This slide showcases various statistics about potential impact of generative AI on economy and across different business functions. It showcases stats related to global GDP, sales productivity, research and development costs etc. Introducing Key Statistics Highlighting Potential Impact Of Economic Potential Of Generative AI SS to increase your presentation threshold. Encompassed with five stages, this template is a great option to educate and entice your audience.

Economists at the National Bureau of Economic Research found a 5% increase in the number of openings for highly skilled jobs that had been considered vulnerable to AI, such as white-collar office work. The timeframe for the study was 2011 to 2019, the period when businesses started using deep learning to automate tasks. The researchers concluded that new technology can increase demand for more skilled workers even when it replaces those who do routine work. Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3).

Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. Contrary to the fear that AI will lead to widespread job displacement, generative AI has the potential to create new job opportunities and stimulate economic growth. As businesses adopt AI technologies, new roles in AI development, maintenance, and oversight will emerge.

And I’d (Tom Allen) say that Tesla are using it in ways for an incrementally improving customer experience, part of which will be using Generative AI. Tesla cars have consistently updating updates which are likely going to be combined with the full leverage of the other companies under Elon Musks portfolio such as XAI, the competitor to OpenAI which has been demonstrated in recent media, and SpaceX. For example, Musk hinted the next Tesla will be able to get to 0-60 under 1 second thanks to the help of SpaceX’s design team.

This can significantly reduce production costs while increasing content reach and engagement, boosting marketing ROI. Generative AI stands as a catalyst for economic transformation, offering innovative solutions across various sectors. For example, in the creative industries, companies such as Artbreeder and Runway ML are democratizing artistic expression by providing accessible platforms for AI-generated content creation. Artists and designers can now explore novel ideas and streamline production workflows, leading to enhanced creativity and efficiency. Generative AI has a rich historical background that traces its roots back to the early days of artificial intelligence. The concept of machines capable of generating human-like outputs has been a persistent goal in the field.