Economic potential of Generative AI EY India

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. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language.

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. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.

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. In the entertainment industry, gen AI creates personalized recommendations for movies, TV shows, and music based on individual preferences. This technology can foster the same efficiency and accuracy that it does in other industries, making it a potential cost-saver for media companies. 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.

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. Artificial intelligence can solve many problems that humans can’t, such as traffic congestion, parking shortages, and long commutes. Gen AI is expected to play a role in improving the quality, safety, efficiency, and sustainability of future transportation systems that do not exist today. The information contained in this article does not constitute a recommendation from any Goldman Sachs entity to the recipient, and Goldman Sachs is not providing any financial, economic, legal, investment, accounting, or tax advice through this article or to its recipient. 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. 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.

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. In addition, jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth, according to the report. For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals.

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. However, https://chat.openai.com/ 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.

Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. 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.

For now, however, foundation models lack the capabilities to help design products across all industries. Generative AI is bringing a new possibility for product design and customization, with companies such as Adidas and Autodesk leveraging AI-driven design tools to optimize manufacturing processes. By harnessing the power of generative algorithms, these companies can create tailored products that meet the unique preferences of consumers, driving customer satisfaction and brand loyalty.

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. Generative AI, a subset of artificial intelligence, is revolutionizing the way machines learn and create. Unlike traditional AI, which relies on predefined rules, generative AI has the ability to generate new, original content.

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. 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.

This paradigm shift in AI capabilities is opening doors to unprecedented opportunities across various sectors. It’s like a digital artist, drawing inspiration from massive datasets to produce never-before-seen outputs. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data.

Marketing and sales: Boosting personalization, content creation, and sales productivity

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. 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.

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. 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. McKinsey & Co. estimates it would raise the financial value created by other types of AI by 15% to 40%.

Potential operational benefits from using generative AI for marketing include the following:

Generative AI represents a convergence of decades of research and development in the field of artificial intelligence. From the early days of symbolic AI, where algorithms attempted to mimic human reasoning through logical rules, to the breakthroughs in machine learning and deep learning. 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.

In addition, the workforce will need to develop new skills and capabilities and some business processes likely will need to be rethought. Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures. An Implement Consulting Group study commissioned by Google has estimated generative AI’s GDP contribution and implications on jobs in Sweden.

This marked a turning point, enabling the generation of highly realistic and diverse data, from images to text. Around the same time, Variational Autoencoders (VAEs) and Recurrent Neural Networks (RNNs) began to demonstrate their ability to generate novel content. Generative Artificial Intelligence (GenAI) is becoming a glowing lighthouse of possibility for businesses, public sector, and communities. The pace of which tools such as ChatGPT and Gemini are being used is reshaping how businesses operate, communicate, and learn. And with this there are use cases appearing on how this technology will bring real world, tangible results, which we will look at in this article.

“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. Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design.

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. 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.

Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders. Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do. We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. Additionally, the deployment of generative AI in decision-making processes or using social scoring indexes for applications such as hiring or in criminal justice systems, high profile examples of which are raising concerns about algorithmic bias. The models can inadvertently perpetuate and amplify existing societal inequalities if not carefully designed and monitored.

Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. 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. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences.

Generative AI’s evolution has been gradual, fueled by substantial investments in advanced machine learning and deep learning projects. 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. This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success. 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.

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.

This not only improves customer satisfaction but also frees up human resources for more complex and strategic tasks, thereby enhancing overall business efficiency. PandoraBot.io provides custom, powerful AI bots that level the playing field by offering your business the unfair AI advantage. With AI, small businesses are rethinking their approaches to customer experience, productivity, revenue, and growth in both the B2B and the B2C domains.

We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be. 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. 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. Generative AI like QuickVideo.ai holds significant economic potential in the marketing and creative industries.

We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1). At the same time, advances in AI are expected to have far-reaching implications for the global enterprise software, healthcare and financial services industries, according to a separate report from Goldman Sachs Research. With well-known tech giants poised to roll out their own generative AI tools, the enterprise software industry Chat PG appears to be embarking on the next wave of innovation, after the development of the internet, mobile and cloud computing transformed the ways we operate as a society. Analyzing databases detailing the task content of over 900 occupations, our economists estimate that roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI. They further estimate that, of those occupations that are exposed, roughly a quarter to as much as half of their workload could be replaced.

Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. It does not account for potential knock-on effects the technology may have on customer satisfaction and retention arising from an improved experience, including better understanding of the customer’s context that can assist human agents in providing more personalized help and recommendations. 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.

Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions.

All of the above is something the recent AI EU Act is attempting to combat and regulate for protection of the consumer with the above being banned, alongside other items, for all countries that make it law in its planned timeline over the next couple of years. “This includes increasing the level of productivity through direct efficiency gains as well as accelerating the rate of innovation and future productivity growth,” Korinek says. In the transportation industry, self-driving vehicles are powered by generative AI, enabling them to navigate roads and make real-time decisions.

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 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. And situations like this are likely going to become a reality for companies in various sectors of different sizes. 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.

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. According to our analysis, the direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function. This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs. By accelerating the coding process, generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design. One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.

Case Studies and Reports About AI

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. Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations. Retailers can combine existing AI tools with generative AI to enhance the capabilities of chatbots, enabling them to better mimic the interaction style of human agents—for example, by responding directly to a customer’s query, tracking or canceling an order, offering discounts, and upselling. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information.

The McKinsey’s updated adoption scenarios, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates. [Deep learning models] can, for example, either classify objects in a photo or perform another function such as making a prediction. In contrast, one foundation model can perform both of these functions and generate content as well. Foundation models amass these capabilities by learning patterns and relationships from the broad training data they ingest, which, for example, enables them to predict the next word in a sentence. Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain.

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. People seem to be obsessed with looking ahead rather than dealing with how AI is impacting the world today. Numerous case studies and reports have pointed to AI’s impact on various industries, the economy, and the workforce.

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. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates. The breakthrough moment arrived with the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow and his colleagues in 2014. GANs introduced a novel approach where two neural networks, a generator and a discriminator, were pitted against each other in a competitive learning framework.

Imagine creating personalized marketing videos at scale with AI-powered avatars that can speak different languages or cater to specific demographics. This can significantly reduce production costs while increasing content reach and engagement, boosting marketing ROI. 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. This can reduce the need for human labor, raising concerns about job displacement and income inequality.

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. For example, generative AI can help retailers with inventory management and customer service, both cost concerns for store owners.

Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. The economic potential of generative AI is likely going to experience exponential growth in ways we probably haven’t considered or seen coming.

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. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.

Learn more about the overall report on The economic opportunity of generative AI in D9+ and get links to all country reports. 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. The first wave of gen AI, conducted especially by LLM models, have seen a huge adoption and experimentation in different contexts.

• In contrast, one foundation model can perform both of these functions and generate content as well.
• This level of customization not only enhances user satisfaction but also drives customer loyalty and revenue growth.
• 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.
• 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.
• But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.

Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts. 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).

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 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.

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. 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. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions.

A study by Accenture found that artificial intelligence could add $14 trillion to the global economy by 2035, with the most significant gains in China and North America. The study also predicted that AI could increase labor productivity by up to 40% in some industries. 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. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually — [..] by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. From art and design to music composition, the technology empowers creatives to explore new frontiers.

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. Recently Edelman released its 2024 Trust Barometer Report on trust people have on the government and media. With its ability to leverage vast amounts of data and predict outcomes, AI can significantly improve decision making, optimize production, enhance product quality, and reduce waste.

Gen AI can also help retailers innovate, reduce spending, and focus on developing new products and systems. 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. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. Several real-world use cases highlight the versatility of generative AI, from legal question-answering applications like Harvey to fashion design with AiDA and marketing content generation by Jasper. You can foun additiona information about ai customer service and artificial intelligence and NLP. Companies like Exscientia demonstrate accelerated drug development processes using generative AI. 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.

Capturing the full potential of generative AI, however, depends on a number of drivers of AI adoption – from a robust operating environment to the availability of skilled AI practitioners. An Implement Consulting Group study commissioned by Google has estimated generative AI’s GDP contribution and implications on jobs in Finland. Or does it mean a safer, better economy because of the ethical and societal uses being a priority with a law around it? It seems the only penalty at the moment is a fine for companies in the countries not abiding by the law with a grey area for how governments and police can use the soon-to-be-forbidden technology.

The wealth and development of the country’s economy is certainly an influential factor when assessing the pace of adoption of this new technology. The adoption is likely to be faster in developed countries, where wages are higher and the costs to automate a particular work activities may be incurred. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries.

If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read” vast libraries of corporate information stored in natural language and quickly scan source material in dialogue with a human who helps fine-tune and tailor its research, a more scalable solution than hiring a team of human experts for the task. Chatbots and virtual assistants powered by generative AI can understand and respond to customer inquiries with a level of nuance that was once thought impossible.

“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 the economic potential of generative ai 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.

The healthcare and pharmaceutical industries are experiencing a shift with the adoption of generative AI, something which The AI Journal covered in its AI in Healthcare report. Startups like Insilico Medicine are leveraging AI-driven simulations and predictive analytics to expedite drug discovery and development processes. By accelerating the identification of promising drug candidates, these companies are poised to address unmet medical needs more efficiently, ultimately improving patient outcomes. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks.

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. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design.

The McKinsey report defines generative AI as applications typically built using foundation models. While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. 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.