Generative AI and Its Economic Impact: What You Need to Know
Generating Value from Generative AI
The research estimates a potential boost to productive capacity of US$621 billion in India, US$1.1 trillion in Japan, and US$79.3 billion in the Philippines alone, with studies ongoing in Malaysia, Indonesia and South Korea. These country findings are consistent with other global studies—for instance, a recent report by McKinsey estimates generative AI could add up to US$4.4 trillion a year to the global economy. This is the third installment of the EY-Parthenon macroeconomic article series on the economic impact of AI. The series aims to provide insights on the economic potential of generative AI (GenAI), including new developments and actionable insights to arm companies’ decision makers. The third article in this series discusses future productivity effects of GenAI by examining multiple scenarios, historical lessons and recent case studies.
This is significantly short of the projected profits of sales and marketing, which both exceed $450 billion. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity.
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.
Significant impact on different jobs and industries
Reinforcement learning from human feedback (RLHF) is an alignment method popularized by OpenAI that gives models like ChatGPT their uncannily human-like conversational abilities. In RLHF, a generative model outputs a set of candidate responses that humans rate for correctness. Through reinforcement learning, the model is adjusted to output more responses like those highly rated by humans. This style of training results in an AI system that can output what humans deem as high-quality conversational text. Zero- and few-shot learning dramatically lower the time it takes to build an AI solution, since minimal data gathering is required to get a result. First, many generative models are sensitive to how their instructions are formatted, which has inspired a new AI discipline known as prompt-engineering.
Individuals can utilize the tool on a personal level and reorganize large sets of data, compose music, and create digital art. History suggests that technological advancements lead to the creation of new jobs and long-term economic growth, including the development of roles that can’t even be imagined today. Across Asia, the goal should be to ensure these opportunities are equitably distributed, along with investments to ensure the workforce is adequately prepared. Accountability has to be a core principle, to ensure that machines remain subject to effective oversight by people.
100 articles on generative AI – McKinsey
100 articles on generative AI.
Posted: Sun, 19 May 2024 07:00:00 GMT [source]
In plain words, the efficiency axiom requires the total value to be distributed among individuals. The symmetry and null player axiom refer to “same contribution, same value” and “no contribution, no value”, respectively. These principles lay the groundwork for a revenue distribution method that ensures that every participant receives a share of the total value that reflects their contribution to the coalition. The Shapley value’s unique ability to satisfy these conditions makes it a powerful tool for analyzing cooperative scenarios and allocating resources or costs in a manner that is widely considered fair. © 2024 KPMG Bağımsız Denetim ve Serbest Muhasebeci Mali Müşavirlik A.Ş, a Turkish Corporation and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee.
Generative AI models – the risks and potential rewards in business
Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves.
This differs from our SRS framework, which uses the log-likelihood as the utility since there is no such thing as prediction accuracy for generative models. For the WikiArt dataset, we selected four disjoint subsets of paintings from four renowned artists. A model, initially trained on a broader set of training images (excluding those belonging to the four artists), served as the base model. The SRS is computed by further fine-tuning the base model on various combinations of the four painting sets belonging to the selected artists.
Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. Generative AI (henceforth, GenAI) represents the latest evolution in the field of Artificial Intelligence (AI) and is a group of AI models designed to generate new content, spanning text, images, and videos (Huang & Rust, 2023). Unsurprisingly, numerous firms have already started using GenAI to perform key innovative marketing activities. For instance, Coca Cola used GenAI to co-create new beverages, such as its Coca-Cola Sugar Y3000. The adoption of a self-supervised learning approach, coupled with advancements in computing power (e.g., GPU) and a novel model architecture known as Transformer (Vaswani et al., 2017) that allows faster training, led to the emergence of foundation models.
This technology is developing rapidly and has the potential to add text-to-video generation. Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. 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.
Before building the Mark I
Perceptron, which today rests in the Smithsonian Institution, Rosenblatt and the Navy simulated it on an IBM
704 mainframe computer for a public demonstration in July 1958. But the perceptron was such a simple neural
network it drew criticism from Massachusetts Institute of Technology computer scientist Marvin Minsky,
cofounder of MIT’s AI laboratory. Minsky and Rosenblatt reportedly debated the perceptron’s long-term
prospects in public forums, resulting in the AI community largely abandoning neural network research from
the 1960s until the 1980s.
- The significant cost of annotation severely restricts the volume of data available for model training, limiting the ability to generalize effectively to novel settings (Bommasani et al., 2021).
- Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth.
- Foundation models and generative AI can enable organizations to complete this step in a matter of weeks.
- He has written about BMW’s erratic strategy for electric vehicles, Walmart’s controversial decision to close its Store 8 innovation lab, and Goldman Sach’s failed efforts to build a consumer bank.
- This stands in contrast to previous technological shifts which placed lower-skilled workers at greater risk of losing out.
Gen AI has increased accuracy and productivity and has lowered costs in various industries. 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 that can be beneficial but also problematic from a privacy perspective.
The state of AI in 2023: Generative AI’s breakout year
Generative AI creates significant value in banking because of its ability to efficiently process large amounts of data, perform deep analytics, and automate decision-making. Reduce legal risks and financial losses by predicting risks and automating compliance management in the risk and legal areas. In corporate banking, AI improves the accuracy of credit assessment and risk management, shortens approval times, and reduces the risk of loan default. It can also enable AI to improve customer satisfaction and loyalty through personalized services and intelligent customer service, and promote business growth. In 2022, AI applications in the financial services industry were mainly focused on product and service development, accounting for 31%. This shows that AI technology plays a key role in driving financial innovation, designing personalized financial products and services, and has an important impact on improving customer experience and competitive advantage.
As organizations begin to set gen AI goals, they’re also developing the need for more gen AI–literate workers. As generative and other applied AI tools begin delivering value to early adopters, the gap between supply and demand for skilled workers remains wide. To stay on top of the talent market, organizations should develop excellent talent management capabilities, delivering rewarding working experiences to the gen AI–literate workers they hire and hope to retain. A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work.
However, this will be possible only if individuals affected by the technology were to shift to other work activities that at least match their 2022 productivity levels. 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). The analyses in this paper incorporate the potential impact of generative AI on today’s work activities.
The risk is that firms invest ahead of learning, without any real clarity about the use case or customer problem they will solve. Investing in innovation without validating your assumptions is an expensive mistake to make, as Goldman Sachs learned from rush to build a consumer bank. It cost them $4 billion to discover that it didn’t matter to consumers that they were “a big bank with a big balance sheet” (quote from CEO David Solomon). On the one hand, research supports a complementary view, as both Reisenbichler et al. (2022) and Reisenbichler et al. (2023) show that GenAI alone is insufficient; instead, adopting a “human-in-the-loop” approach is necessary. On the other hand, Girotra et al. (2023) find that ChatGPT-4 does not require humans to generate more and better ideas than MBA students. Thus, we conclude that the “jury is still out,” underscoring the need for future research to investigate the boundary conditions of the relationship between GenAI, marketing capabilities, and firm value.
Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries. Generative AI technology can not only dramatically improve the speed and accuracy of data processing, but also create far-reaching impact in multiple areas such as customer service, risk management, and decision support. This article will explore the development trend and application value of generative AI in the global financial industry through in-depth analysis of a number of key data, and analyze how this technology can lead the banking business into a new era of intelligence. One concern is potential strategic behaviors, such as copyright owners merging or splitting their data to maximize their royalty share.
Such automation would free up time from routine work and leave time for creative work and innovation. This, in turn, could compensate for the slowdown in productivity growth that has occurred in recent decades. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time.
Earlier techniques like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks processed words one by one. Transformers also learned the positions of words and their relationships, context that allowed them to infer meaning and disambiguate words like “it” in long sentences. Artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of ChatGPT seems to mark a turning point. Chat GPT OpenAI’s chatbot, powered by its latest large language model, can write poems, tell jokes, and churn out essays that look like a human created them. Prompt ChatGPT with a few words, and out comes love poems in the form of Yelp reviews, or song lyrics in the style of Nick Cave. Organizations continue to see returns in the business areas in which they are using AI, and
they plan to increase investment in the years ahead.
For example, Li, Raymond, and Peter Bergman explore how algorithm design can improve the quality of interview decisions in the context of professional services hiring. McKisney’s projections chime with a recent study into the impact of AI-based conversational assistants. The research found that after deploying customer service agents, 14% more customer queries were able to be resolved, reducing time spent by employees by 9%.
Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy.
Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques.
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. The question of whether generative models will be bigger or smaller than they are today is further muddied by the emerging trend of model distillation.
For one thing, gen AI has been known to produce content that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed. Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used.
The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. In practice, a commercial AI model could undergo millions of transactions on a daily basis. It suffices to estimate the aggregated payoffs each copyright owner deserves instead of calculating the payoff as specified in (2.4) for each AI-generated content. To save computational cost, we can evaluate the SRS for only a small fraction of all transactions and scale back to obtain estimates of the revenue distributions from all transactions; see a detailed discussion in the supplementary materials. My concern – see my previous article, “AI and Corporate Innovation” – is that too many firms are listening to McKinsey and too few to Professor Acemoglu’s more skeptical analysis.
The best-known example of generative AI today is ChatGPT, which is capable of human-like conversations and
writing on a vast array of topics. Other examples include Midjourney and Dall-E, which create images, and a
multitude of other tools that can generate text, images, video, and sound. Additional
factors, such as powerful, high-performing models, unrivaled data security, and embedded AI services
demonstrate why Oracle’s AI offering is truly built for enterprises.
Building on Chan’s (2023) AI Ecological Education Policy Framework, the guidelines offer a suggestive frame of reference for faculty and students to integrate generative AI into their coursework. Furthermore, feedback from 118 students and 14 academics at a teacher education institution in the Philippines underscores the guidelines’ potential benefits, concerns, usefulness, and necessity in their academic undertakings. While the policy may not cover every detail exhaustively, it seeks to provide practical and context-sensitive recommendations for ethical, honest, responsible, and fair use of AI in course development, implementation, and student engagement. Consequently, other higher education institutions in general, and academics in particular, may adopt and/or modify the guidelines to suit their positions, goals, needs, and directions.
The report Generative AI models — the risks and potential rewards in business examines what the future holds for ChatGPT and other generative artificial intelligence (AI) applications, including how they work and the risks and potential benefits. Generative AI’s
ability to produce new original content appears to be an emergent property of what is known, that is, their
structure and training. So, while there is plenty to explain vis-a-vis what we know, what a model such as
GPT-3.5 is actually doing internally—what it’s thinking, if you will—has yet to be figured out. Some AI
researchers are confident that this will become known in the next 5 to 10 years; others are unsure it will
ever be fully understood. Businesses large and small should be excited about generative AI’s potential to bring the benefits of
technology automation to knowledge work, which until now has largely resisted automation.
Where could reinvention take your business?
In the same way that “digital native” companies
had an advantage after the rise of the internet, Ammirati envisions future companies built from the ground
up on generative AI-powered automation will be able to take the lead. Oracle has partnered with AI developer Cohere to help
businesses build internal models fine-tuned with private corporate data, in a move that aims to spread the
use of specialized company-specific generative AI tools. Neural network models use repetitive patterns of artificial neurons and their interconnections. A neural
network design—for any application, including generative AI—often repeats the same pattern of neurons
hundreds or thousands of times, typically reusing the same parameters.
Many companies are actively exploring how to integrate GenAI into their business operations. Leaders are generally optimistic about the development of the technology and are actively investing resources in the technology, which has great potential to improve customer experience and optimize operational efficiency. In 2022, AI technology has been widely used in the global financial services industry, especially in customer experience management, risk management, data analysis, and other fields. AI not only optimizes front-end customer service, but also penetrates into back-end financial reporting, cloud management, and risk control, providing financial institutions with powerful data support and intelligent decision-making capabilities.
Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. Generative AI holds enormous potential to create new capabilities and value for enterprise. Many generative models, including those powering ChatGPT, can spout information that sounds authoritative but isn’t true (sometimes called “hallucinations”) or is objectionable and biased.
Even AI
experts don’t know precisely how they do this as the algorithms are self-developed and tuned as the system
is trained. About 75 percent will be created in customer service, marketing and sales, software development, and research and development – and thus in areas that are heavily knowledge- and people-based. A waterfall graph shows the potential additional value that could be added to the global economy by new generative AI uses cases. An initial $11.0 trillion–$17.7 trillion could come from advanced analytics, traditional machine learning, and deep learning. And additional $2.6 trillion–$4.4 trillion of incremental economic impact could be added from new generative AI use cases, resulting in a total use-case-driven potential of $13.6 trillion–$22.1 trillion.
So, if those working in sales and marketing continue to pivot toward generative AI, the savings they make could be major. Sales and marketing processes are likely to see the biggest productivity uptick, largely because of generative AI’s potential in transforming the customer experience. Due to the varied and sprawling applications of generative AI, no industry is expected to be exempt from its impact. However, McKisney’s findings predict that marketing and sales, software engineering, and research and development (R&D) could account for a staggering 75% of its total profits. Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language.
But this comes with its own costs, primarily in the form of data infrastructure, which must also be factored into the price tag of using an LLM for business functions. The gray area in the guidelines of how generative AI should be defined, implemented, and regulated has given rise to AI washing. A bit like greenwashing, AI washing is a marketing tactic where developers of LLMs, chatbots, and AI tools overstate the efficacy of the technology.
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.
Similarly, for the FlickrLogo-27 dataset, we selected four disjoint subsets of logo designs from four brands, and computed the SRS using a base model trained on logo images from other brands. Our goal was to assess whether the SRS can reflect each copyright owner’s contribution to the generation of images. It is also likely that if we focus exclusively on AI for productivity the economic potential of generative ai gains that these will just become a new industry norm. Competition within industries like customer service will lead to any advantage just being competed away, so all you have done is innovated to stand still. We believe that generative AI models have the potential to transform businesses through automating and executing certain tasks with unprecedented speed and efficiency.
Second, for a market-based asset to confer a sustainable competitive advantage, it must be convertible, rare, inimitable, and non-substitutable (Srivastava et al., 1998). On the other hand, a unique aspect of GenAI is that its output depends on the input that users provide, suggesting a complementary relationship with key market-based assets. For instance, GenAI creates new content based on its world knowledge at the time of training. You can foun additiona information about ai customer service and artificial intelligence and NLP. As of January 2024, the latest knowledge available to some ChatGPT models dates back September 2023. This limitation could severely hinder GenAI’s ability to produce valuable output in supporting new product development, given the frequent shifts in consumer preferences. Firms can curb the risk of creating something misaligned with current customer needs by ensuring they have up-to-date market knowledge to feed GenAI.
Early adopters are likely to achieve a significant advantage in transforming industry challenges into opportunities. The rise of generative AI also poses potential threats, including the spread of misinformation and the creation of deep fakes. As this technology becomes more sophisticated, ethicists warn that guidelines for its ethical use must be developed in parallel. The AI step was a research paper, “A Logical Calculus of Ideas Immanent in Nervous Activity,” by
Warren McCulloch, a psychiatrist and professor at the University of Illinois College of Medicine, and Walter
Pitts, a self-taught computational neuroscientist.
AI revolutionizes commercialization by improving access, marketing, and customer engagement. Oracle’s partnership with Cohere has led to a new set of generative AI cloud service offerings. “This new
service protects the privacy of our enterprise customers’ training data, enabling those customers to safely
use their own private data to train their own private specialized large language models,” Ellison said. In R&D, generative AI can increase the speed and depth of market research during the initial phases of
product design.
These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments.
While 43% of respondents reported trusting AI outputs to at least some degree, 31% either somewhat or highly distrusted the code their tools produced. By carefully engineering a set of prompts — the initial https://chat.openai.com/ inputs fed to a foundation model — the model can be customized to perform a wide range of tasks. You simply ask the model to perform a task, including those it hasn’t explicitly been trained to do.
Therefore, as generative AI capability grows, more organizations will need workers who oversee the reliable, fair, and ethical use of the technology. “There will still need to be human judgment to account for potential algorithmic bias, as well as person-to-person interaction to manage important stakeholder relationships,” Mazhari explains. When it comes to the ability to generate, arrange, and analyze content, generative AI is a gamechanger—one with transformative social and economic potential.
Generative AI can add EUR 13 billion to Finland’s GDP – McKinsey
Generative AI can add EUR 13 billion to Finland’s GDP.
Posted: Mon, 22 Jan 2024 08:00:00 GMT [source]
Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption. Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent. From a methodological perspective, a crucial aspect warranting future research is the use of Shapley value ratios for revenue distribution. The key challenge with directly using the Shapley value lies in the unknown total revenue for any coalition of copyright owners’ data. However, the efficiency property of the Shapley value [34], which ensures the sum of Shapley values equals the grand coalition’s utility, loses meaning when considering ratios.
- Prompt ChatGPT with a few words, and out comes love poems in the form of Yelp reviews, or song lyrics in the style of Nick Cave.
- These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves.
- More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually.
Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms. Our research found that marketing and sales leaders anticipated at least moderate impact from each gen AI use case we suggested. They were most enthusiastic about lead identification, marketing optimization, and personalized outreach. 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. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities.