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June 16, 2023
The economic potential of generative AI — Executive Summary
The next productivity frontier
Michael Chui; Eric Hazan; Roger Roberts; Alex Singla; Kate Smaje; Alex Sukharevsky; Lareina Yee; Rodney Zemmel
June 2023
This is an Executive Summary, of the publication “The economic potential of generative AI”, published by MGI, and authored by Michael Chui; Eric Hazan; Roger Roberts; Alex Singla; Kate Smaje; Alex Sukharevsky; Lareina Yee; Rodney Zemmel, on June 2023.
Key insights — Summary
1.Generative AI’s impact on productivity could add trillions of dollars in value to the global economy.
2.About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D.
3.Generative AI will have a significant impact across all industry sectors.
4.Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.
5.The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.
6.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.
7.The era of generative AI is just beginning.
Key Insights — Detailed
1.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 would increase the impact of all artificial intelligence by 15 to 40 percent.
- 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.
2.About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D.
- Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes.
- Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks.
3.Generative AI will have a significant impact across all industry sectors.
- Banking, high tech, and life sciences are among the industries that could see the biggest impact 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.
4.Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.
- Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today.
- In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working.[1]
- The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time.
- Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.
5.The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.
- Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, 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.
6.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.
- Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities.
- Combining generative AI with all other technologies, work automation could add 0.2 to 3.3 percentage points annually to productivity growth.
- However, workers will need support in learning new skills, and some will change occupations.
- If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.
7.The era of generative AI is just beginning.
- Excitement over this technology is palpable, and early pilots are compelling.
- 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.
Table of Contents
Key insights
Chapter 1: Generative AI as a technology catalyst
Chapter 2: Generative AI use cases across functions and industries
Spotlight: Retail and consumer packaged goods
Spotlight: Banking
Spotlight: Pharmaceuticals and medical products
Chapter 3: The generative AI future of work: Impacts on work activities, economic growth, and productivity
Chapter 4: Considerations for businesses and society
Appendix
Glossary
Glossary
Application programming interface (API) is a way to programmatically access (usually external) models, data sets, or other pieces of software.
Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require human intelligence.
Artificial neural networks (ANNs) are composed of interconnected layers of software-based calculators known as “neurons.” These networks can absorb vast amounts of input data and process that data through multiple layers that extract and learn the data’s features.
Deep learning is a subset of machine learning that uses deep neural networks, which are layers of connected “neurons” whose connections have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text, and audio.
Early and late scenarios are the extreme scenarios of our work-automation model. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. The reality is likely to fall somewhere between the two.
Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.
Foundation models (FM) are deep learning models trained on vast quantities of unstructured, unlabeled data that can be used for a wide range of tasks out of the box or adapted to specific tasks through fine-tuning. Examples of these models are GPT-4, PaLM, DALL·E 2, and Stable Diffusion.
Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have, such as the ability to generate content. Foundation models can also be used for nongenerative purposes (for example, classifying user sentiment as negative or positive based on call transcripts) while offering significant improvement over earlier models. For simplicity, when we refer to generative AI in this article, we include all foundation model use cases.
Graphics processing units (GPUs) are computer chips that were originally developed for producing computer graphics (such as for video games) and are also useful for deep learning applications. In contrast, traditional machine learning and other analyses usually run on central processing units (CPUs), normally referred to as a computer’s “processor.”
Large language models (LLMs) make up a class of foundation models that can process massive amounts of unstructured text and learn the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural-language text, performing tasks such as summarization or knowledge extraction. GPT-4 (which underlies ChatGPT) and LaMDA (the model behind Bard) are examples of LLMs.
Machine learning (ML) is a subset of AI in which a model gains capabilities after it is trained on, or shown, many example data points. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt and can become more effective in response to new data and experiences.
Modality is a high-level data category such as numbers, text, images, video, and audio.
Productivity from labor is the ratio of GDP to total hours worked in the economy. Labor productivity growth comes from increases in the amount of capital available to each worker, the education and experience of the workforce, and improvements in technology.
Prompt engineering refers to the process of designing, refining, and optimizing input prompts to guide a generative AI model toward producing desired (that is, accurate) outputs.
Self-attention, sometimes called intra-attention, is a mechanism that aims to mimic cognitive attention, relating different positions of a single sequence to compute a representation of the sequence.
Structured data are tabular data (for example, organized in tables, databases, or spreadsheets) that can be used to train some machine learning models effectively.
Transformers are a relatively new neural network architecture that relies on self-attention mechanisms to transform a sequence of inputs into a sequence of outputs while focusing its attention on important parts of the context around the inputs. Transformers do not rely on convolutions or recurrent neural networks.
Technical automation potential refers to the share of the worktime that could be automated. We assessed the technical potential for automation across the global economy through an analysis of the component activities of each occupation. We used databases published by institutions including the World Bank and the US Bureau of Labor Statistics to break down about 850 occupations into approximately 2,100 activities, and we determined the performance capabilities needed for each activity based on how humans currently perform them.
Use cases are targeted applications to a specific business challenge that produces one or more measurable outcomes. For example, in marketing, generative AI could be used to generate creative content such as personalized emails.
Unstructured data lack a consistent format or structure (for example, text, images, and audio files) and typically require more advanced techniques to extract insights.
References:
[1] “A future that works: Automation, employment, and productivity,” McKinsey Global Institute, January 12, 2017.
